Most breakthrough innovations take at least 5 years or more to come to scale. Most corporate business cases are three years or less.

This punishes innovators massively. Combine this with the fact that the tenure in most corporations is less than 5 years — and you have a no-win situation for innovation.

There are two ways to fix this: fancy and simple.

The fancy way is to capitalize your R&D efforts according to an empirically derived amortization schedule specific to sector. The easy way is to use a 5 year NPV on innovations, like Amazon does.

In this workshop, DI Partner John Sviokla and Director of Research at Valens Research Robert Spivey explain why, using insights from a 25,000 company global database on financial performance.

For the last 12 months, DI has been working with Valens Research on a proprietary methodology that provides an apples-to-apples comparison of how much firms spend on R&D and innovation and whether or not it creates economic value. We evaluated the financial data of over 10,000 companies, normalized using uniform accounting principles.

The result is what we’re calling the Growth Innovators Matrix, an instrument that quickly can show how an industry (and the companies within it) are doing in terms of investing in growth-based innovation, and their ability to turn that investment into value.

The matrix can help teams see how they stack up relative to peer organizations. And for individuals looking to make the case for increasing R&D investment, we believe the matrix can help draw a direct line between investment and potential impact.

Some of the most interesting findings from the study:

• Growth Innovators have roughly 2x higher valuation/asset multiples than the average across all companies.   
• 17% of companies across industries are Growth Innovators.
• Growth Innovators are represented across industries, although unsurprisingly some industries have a disproportionate share.

The report outlines how we arrived at the placement of companies within each quadrant, and provide suggestions on how organizations can move from one quadrant to another.

We’d love to share the study with you and get your feedback. To get immediate access, just fill out the brief form above.

P.S. Check out the end of this report to find out how to get a Custom Growth Innovator’s Matrix for your industry free of charge.

Building a successful business is really hard. So you should do everything you can to improve your likelihood of success. Startup advisors often play an important role in improving the speed and outcomes for the startups they advise. This article explores what advisors are, what they do, and how to get the most out of them.

• What is an advisor
• Do advisors invest in the company?
• Why you need advisors
• Real life examples
• Helpful mentors dramatically increase fundraising success
• Why you need REAL advisors
• What to look for in advisors
• Forming your advisory team
• Advisor Compensation
• Advisor contracts
• Maximizing advisor contributions

What is an advisor?

Startup advisors help management teams make better decisions, move faster, and improve outcomes. Examples of the sorts of things advisors often help with include:

• Advice on business model strategy and positioning
• Advice on key areas of the business (e.g., user acquisition, product architecture)
• Honing your pitch decks and presentations
• Introductions to potential investors
• Introductions to key customers
• Help identifying and recruiting talent
• Acting as a sounding board for organizational and people issues

Advisors are almost always experienced business people or domain experts who know things or have relationships the startup management team doesn’t.

What advisors are not

Advisors are not mentors, at least in our lexicon. Mentors offer personal support and advice to entrepreneurs, not to the broader company. Advisors work on behalf of the company and all of its shareholders.

By definition, advisors are not employees. To the extent they formally engage (the relationship is often informal), they are independent contractors.

Legally and practically, advisors are not board directors. Directors also advise and support the company, but the context is quite different. Board directors have a legal status that comes with certain rights and duties that don’t accrue to advisors. Directors have the right to contribute to decisions about the strategy and operations of the company, and a right to be informed about the company. They also have a fiduciary duty to act on behalf of the interests of all the company’s shareholders, ensure they remain suitably informed about the company, and a duty of care in performing their duties. Advisors have no such duties or rights outside those expressed in a written advisory agreement.

Because advisors are not employees or directors, they often act more like mentors—meaning they emphasize the interests of the management team over the other shareholders. For that reason, in many cases entrepreneurs find they can be more open with advisors and more easily avoid conflict with them when dealing with high stress situations.

Do advisors invest in the company?

Advisors may invest in your company as well. Many of them are wealthy, and if they’re interested enough in what you’re doing and believe enough in you to actively help, it shouldn’t come as a surprise when they ask to invest.

But there are many reasons why advisors might not be able or willing to invest. Some simply don’t have the cash. Others might have external constraints that make it too difficult, for example corporate employer policies on equity holdings, or venture capitalists who have to avoid even the appearance of conflict for LPs and partners. Frankly when you’re early on, many advisors are waiting to see where you get before they decide to push any cash your way.

It’s trite to say (as some do) that entrepreneurs shouldn’t engage advisors who aren’t willing to put some money into the company. There’s almost always a point on a company’s path when it’s interesting enough for some advisor attention, but still too uncertain for them to risk cash on it. Cash is emotionally and practically different from time, particularly for non-entrepreneur advisors who may not have as much experience with risk capitalization.

Advisors who do invest are very often more engaged and attentive, so it’s almost always a good thing when they do. One potential downside is that advisors who invest sometimes begin to feel that you have an obligation to listen to their advice, or even an obligation to heed it.

My advice generally is to first focus on the value contributions and working relationship you have with advisors. Any conversations about investment should flow naturally, and should be viewed in the context of their advice more than their money—unless of course they are sophisticated investors with enough capital to really move the needle for you. And don’t exclude promising advisors just because they won’t invest. In the end, there are no hard and fast rules here, and you should constantly seek to optimize for a faster, better outcome.

Are advisors important?

In a word: yes. The right advisors engaged in the appropriate way can dramatically speed progress, reduce risks, and increase your likelihood of success. Changing the way things work (e.g., creating a new business model), inherently involves a level of complexity that requires diverse expertise and difficult problem solving. Advisors can offer operating experience and insights into areas of expertise that you’re very unlikely to have available on your early stage team. Those insights can have a fundamental impact on your company.

One of the most important things a good advisor will do is force you to reconsider your assumptions. It seems intuitive that diversity improves decision-making by bringing to bear differing perspectives. That’s true, but it doesn’t tell the entire story. Research out of Kellogg School of Management demonstrated that:

“Diverse groups outperformed more homogeneous groups not because of an influx of new ideas, but because diversity triggered more careful information processing that is absent in homogeneous groups.”

Real world examples

The product did too good of a job

In 2008, I was doing a startup turnaround as CEO of a recruiting technology venture portfolio company. I had no experience in recruiting at the time, hadn’t been part of making the original investment, and stepped in as a venture capitalist on behalf of my firm to try to fix a company that was quickly headed in a bad direction.

We made a seemingly impossible turnaround on the product in six weeks (the product team I brought in was amazing). But we were surprised that the recruiters targeted by our product were ambivalent about it. It was one of our advisors who pointed out a perspective that seemed alien to us: our product was doing too good of a job. Recruiters report their value to their bosses in part by filtering thousands of resumes into a much smaller set of good candidates. Our product automated a lot of that work for them, giving them more time to be good at the parts of their job only a human could perform. But because it made them feel left out of the loop, they felt threatened and diminished by it.

The solution was fairly quick and easy; we built in more participation and choices on their part, and ensured all of the reporting showed the scale and complexity of the applicant funnels they were managing. Their satisfaction immediately improved.

As performance-driven entrepreneurs without an insider perspective we would likely have remained blind to the recruiter concerns until it was too late. Score one for advisors.

Near premature scaling

A number of years ago I was on the advisory board of a startup operating a two-sided marketplace connecting small businesses and consumers. Registered user growth was strong, and CPAs (Cost Per Acquisition) were in line with industry averages. Unit margins were good, and NPS (Net Promoter Score) was great. It was early and low scale, but the founder was convinced things were working well enough to ramp up growth by increasing spending on acquisition.

However, I was concerned the acquisition model wouldn’t scale—it relied too heavily on street marketing by the team, which I believed was artificially skewing the CPAs down. I also was concerned that the CAC (Customer Acquisition Cost) was higher than it should be because consumer geographic demand had to match the small business supply geography, which often didn’t occur. In other words, newly registered users often couldn’t convert into a paying customer due to lack of local coverage. And finally, I thought they should factor in the cost of acquiring the other side of the market (their small businesses) into the equation because it was very costly, and the volume per business location was too small to justify the volume they generated on the consumer side.

I recommended a strategy of focusing intensely on each geographic area to create a sustainable cycle instead of trying to scale more broadly. At first, the team pushed back. But as they looked further into it, they saw the risk of premature scaling. They paused a planned funding round, and refocused on achieving sustainable metrics. Within a year, the business was humming and they later sold to an acquirer. But things could have gone a very different direction had they attempted to scale before resolving the core unit economic elements of the business.

Scale can sneak up on you, too

This next example shows the other side of the scaling dilemma. This founding team was purchasing goods at Costco for resale to their corporate customers. It was intended to be temporary: an expedient way to learn what worked without too much up front cost and complexity. Their instincts were right, but they hadn’t done the math or thought through the implications of switching their process too late. We pointed out to them that:

• Their volume was low, but growth seemed to be reaching an inflection point
• The time and energy to set up wholesale sources would be very painful if the transition occurred when they were already at scale
• The payment timing for Costco (immediate, or at least 30 days on a credit card) was generally worse than they could get working with a wholesale provider
• Costco shopping wouldn’t scale (nor would their credit card)
• Costco prices at scale would significantly impair their working capital cycle
• The limited choices at Costco constrained their ability to effectively test demand

Thankfully, the team listened to us and acted quickly. Within a few weeks we had them set up with wholesale vendors who extended them credit based on our relationships with them. It was disruptive, but manageable at their then current scale. The result was a significant improvement in margins and cash flow cycles, as well as further increases in growth because the founders could spend more time selling—and less shopping. If they hadn’t made this change, they would have quickly run into a wall where their working capital was insufficient to support continued growth, but it would have happened without enough warning for them to raise the capital needed to support continued operations. Thankfully they avoided that existential threat, and continued on to grow 11x in topline revenue over the next 12 months. Cash flow was really tight, but they made it happen.

Helpful mentors dramatically increase fundraising success

Examples like the above are probably why startups with helpful advisors raise so much more money than ones that don’t. The Startup Genome Report shows that average funding raised by stage was dramatically higher for startups with helpful advisors.

Funding raised is a reasonable proxy for startup success and progress. The findings from the Startup Genome Report imply that beyond validation stage in particular, startup advisors add tremendous value. In fact, it appears that startups that “don’t have helpful mentors” don’t raise any money at scaling stage—another way of saying that most never get there.

It’s all about REAL advisors

Unfortunately, many founders seem to think of advisors as more of a checklist or branding exercise than a real resource. We regularly see advisors listed in pitch decks only to find later that the advisors have virtually no interaction with the team.

Starting up is hard, lonely, and often frustrating. It often feels like the world just doesn’t understand the potential for what you’re working on. That’s probably why so many founders treat advisors as a sort of endorsement or validation, particularly early on. “Take me seriously, look at my advisors!” or “You know I’ll be successful with advisors like this!”

The problem is that advisors do no good unless they’re actively engaged. What sort of an endorsement is it to have a headshot in a pitch deck but the advisor isn’t willing to take the time to actually help? And what good will an amazing advisor do by merely appearing in a pitch deck and taking a call or two?

That’s why having inactive advisors in a pitch deck is a form of vanity that at best offers no real benefit, and at worst reflects very poorly on founders. Venture capitalists are very likely to check in with your advisors—we certainly do—and it’s quite awkward for entrepreneurs when we find that the advisor almost never speaks to the team. We occasionally find that the advisors don’t even recall the company or the team and is confused as to why we’re asking about them. That’s a sure-fire way to eliminate your fundraising prospects with that VC.

The lesson here is simple: have (and list) only real, engaged advisors.

What to look for in advisors

Founders too often look for obvious or flashy advisors rather than focusing on finding the ones who will add the most value to the business. Another common mistake is settling for the most readily accessible people instead of taking the time to identify and cultivate relationships with the most valuable advisors.

As we said previously, flashy but uninvolved advisors aren’t helpful. Poorly suited advisors can be even worse. That’s why I recommend a thoughtful and planned approach to identifying and recruiting advisors. I recommend looking for advisors who:

• At least understand the realities of running a startup (some should be seasoned startup executives)
• Have a deep understanding of the domains that touch upon your business (e.g., technology, industry)
• See the world differently from you and from each other
• Know people, particularly people you don’t know who might be useful
• Aren’t afraid to challenge you and ask tough questions
• Have the time to focus and actually help
• Share your passion and are inspired by your vision

And don’t forget about actively incorporating diverse perspectives. It can be incredibly eye-opening (and value creating) to witness a very different perspective being laid out before you.

Some checkboxes you should probably be able to check in terms of advisors:

• Someone who has successfully built a company with a similar business model and customer (e.g., enterprise SaaS)
• A veteran of your target industry who knows the prevailing attitudes and many key players personally
• A customer meta-expert: someone who really understands how your target customer thinks, feels, and behaves, and preferably is also one of them
• A serial entrepreneur who knows the ins and outs of building startup teams, operations, and cultures
• A technology or product expert, preferably with experience building teams

Forming your advisory team

Attracting advisors is similar to seeking funding; you have to inspire them with excitement about the vision for what you’re doing. Transactional advisor relationships are unlikely to work well. After all, startups are highly risky and unpredictable. Convincing an uninspired advisor to help—and potentially expose her brand and network to risk—via transaction is unlikely to happen. Vision is a critical component of a successful entrepreneurial venture. You’ll definitely have to bring it out to attract quality advisors.

It also often takes personal interactions over an extended period of time to instill a sense of excitement in potential advisors and to identify the ones who can actually help you. That’s why I recommend building your advisory team gradually over time. Building gradually helps avoid bringing on poorly fitting advisors, and enables you to avoid the clutter imposed by unnecessary advisor interactions. Time is precious, and you certainly want to avoid too much management overhead in the formation and maintenance of your advisory team.

Evolution of your advisory team

I tend to think of advisors existing at three distinct levels:

1. Advisory network
2. Advisors
3. Advisory board

I also tend to think about forming your advisory team in the context of slowly building from level 1 to level 3 above.

Advisory Network

Your advisory network comes first. It’s a relatively broad set of people who have an expressed or at least implied willingness to offer advice and assistance to you. How do you find these people? I recommend constantly asking people for advice. Invite the smart ones to your advisory group.

Probably the best way to interact with this network is via regular (quarterly) emails supplemented with targeted asks depending on needs and advisor capabilities. The regular emails should keep the advisors abreast of what’s going on to keep them engaged and reduce friction when you do get around to making asks of them. These regular communications can also incorporate your most important general asks. On top of the regular emails you can reach out directly to appropriate members of the advisory network as needed.

As I mention later in this article, your advisor network members shouldn’t be compensated, and shouldn’t be asked to sign advisory agreements or NDAs. It’s just not worth the cost and complexity for you or for them. If there’s something you’re worried about sharing with them (you probably shouldn’t be), then be careful sharing it or tone down the details.

Your advisory network is most likely to offer fairly passive and infrequent contributions such as pointing out interesting companies, a few introductions, and general ideas and feedback. When members of the network come to you with more specific contributions, consider moving them to an advisor role.

One minor risk that’s worth pointing out is that if you have an advisor who engages too deeply, and starts to feel that she has made meaningful intellectual property contributions to the company, you could find yourself in a legal dispute about IP absent an advisory agreement. That’s one more reason to consider pushing someone to advisor status if they’re becoming more active in helping you.

Advisors

Your advisors should form naturally from your advisory network. These will be the people who provide the most value to you, both because their capabilities match your needs and because they reliably offer assistance—and come through with it.

Advisors may or may not be compensated, as mentioned later in this article. In many—in my experience most—cases advisors are doing it because they like to help, not in the hopes of compensation. But in the end, I recommend compensating your advisors for reasons discussed in later.

As you get to know your advisors you will start to identify a few who can stand out for their advice and assistance. It tends to happen naturally due to the nature and direction of your business. That’s when you might consider adding an advisor to your advisory board.

Advisory Board

Your advisory board should be formed of key advisors who agree to engage on a regular cadence for a specified tenure. This board should have a regular call and meeting schedule—perhaps monthly calls with once quarterly in-person meetings. The board should be small enough to be nimble, but large enough to offer the key expertise and experience that you need during any given time period. That usually means between 2-4 people.

The tenure of members of your advisory board should map to likely evolutions in the needs of your business. I strongly advise an explicit tenure (e.g., not open ended) for several reasons:

• Your needs for advice and assistance are likely to evolve over time
• It sets expectations in advance, and makes it much easier to transition people off as needed without offending them
• It constrains their commitment, making it more likely for busy people to accept the role

Terms should probably be for one to two years. If you have a near-term need that’s unlikely to persist, one year might work. Otherwise, two years probably makes more sense because things always seem to take longer than you think, but two years probably offers enough time to get a lot of value out of an advisor.

Advisor Compensation

As I mentioned previously, you should generally expect to compensate your advisors with some equity. I do not advise offering equity to your advisory network; presumably they will accept this light level of engagement without an expectation of compensation. It would also be hard to justify the cost in both equity and time (and legal costs) of offering equity to a fairly broad set of people in an advisory network.

Your advisors, and certainly your advisory board, however, will probably expect some equity. And even if they don’t, there are good reasons you may want to give it to them. Offering equity allows you to require an advisory agreement, which can clarify expectations and offer important intellectual property protections for the company. If you have an advisor who’s clearly adding value and appears to be a level-headed, reasonable person, you probably don’t have to worry about getting her under contract. But less sophisticated or rational people should probably be under contract—or maybe shouldn’t be advising you at all.

How much equity do advisors get?

Advisor equity commonly ranges between 0.10% and 0.25% for a (typical) two-year engagement. In unusual circumstances it can be much higher: 1% or more. Generally I think it’s a bad sign if an advisor expects too much equity. It implies she doesn’t assign too much value to the company, and likely means she isn’t particularly passionate about your vision.

The amount of equity that’s appropriate depends on several factors:

• Stage / value of the company
• Level of effort
• Expected contribution of the advisor

On the other side of the coin, this isn’t a time to be an equity skinflint. I’ve already pointed out how much value an advisor can generate. So instead of thinking about how much equity you’re giving away, I’d recommend thinking about how much net value their participation will add to your company. If you give away $25,000 worth of equity, but see a $100k increase in enterprise value, you’ve made a good deal for everyone involved.

Another way to think about it is that startups are extraordinarily unlikely to achieve a meaningful liquidity event. But when they do have meaningful exits, they’re often really (really) meaningful. As such, I advise optimizing more for the likelihood of a positive outcome rather than the amount of it you own at the time. One-hundred percent of nothing is—wait for it… nothing.

To put things in context, for a startup with a post-money valuation of $10 million, a 0.10% award is nominally worth $10,000. If an advisor allocates three hours a month on average over the course of two years, that’s $138.89 per hour—theoretically. For one thing, value is in the eye of the beholder and many startups are overvalued and advisors know it. For another thing, advisors are typically awarded common equity, which is worth less than preferred equity, so it’s not as simple as multiplying equity percentage by post-money valuation to get the equivalent value.

Advisor equity structure and vesting

In my opinion, advisor equity should be in the form of common stock options. There’s no reason to add complexity to your cap table and voting structure by offering preferred equity to advisors. And as long as the option strike prices are properly set (e.g., at a reasonable fair market value at the time of award), advisors can expect to defer any tax payments until the time of liquidity. As always, consult your tax and accounting professionals and pay attention to regulatory elements such as 83b elections.

All advisor equity should vest. The timeframe should map to the expected time horizon for value creation, which is typically one to two years. I recommend a three month cliff because there’s always a risk that an advisor won’t work out. And if that happens it’s likely to be obvious fairly quickly. A cliff enables you to end the relationship with the advisor with the three month window without losing any equity, and without having to deal with a hard to explain entry on the cap table.

Advisor equity should feature 100% acceleration on single trigger. In other words, if you sell the company before they’re fully vested, they should get everything they would have gotten over the entire vesting period upon sale. After all, they probably did their job, and there shouldn’t be an expectation for them to hang around to advise the new owners.

Advisor contracts

You need an advisory agreement for any advisor who will receive equity. There are many reasons to put it in a legal document:

• Putting expectations in writing ensures clarity and a “meeting of the minds” about the nature and level of contributions that can be expected
• A contract makes it harder for them to skimp on their duties
• It shows that both parties are serious and actually committed
• Establish clearly that the advisor is not conflicted, and that she will inform you if she becomes conflicted
• A properly constructed agreement protects intellectual property that you may expose from them, or even receive from them (e.g., patentable ideas that by law can lose protection in the absence of NDAs)
• It provides an opportunity to vest advisor equity over time, which is an important tool for recapturing equity that wasn’t really earned

Some advisors will agree to an NDA, IP assignment, and even non-competition without complaint. Sophisticated ones will almost always balk at some or all of those provisions. Venture capitalists, for example, generally won’t sign any of these sorts of provisions. We see so many deals and so many ideas that it’s far too risky to expose ourselves to claims of infringement. Corporate executives are likely to balk as well, for similar reasons. If you do end up at an impasse about any provisions like these, I would recommend dramatically narrowing the scope of your requested protections. The risk that any of your advisors will take advantage of you is vanishingly small. And it’s nothing compared to the value they might create for you. If you construct appropriately narrow provisions, then you can probably protect yourself against the actual bad actors without capturing the 99.99% of good actors in your net.

It’s generally a good idea to seek legal counsel when constructing important agreements. A good lawyer, for example, can play a critical role in negotiating an appropriately narrow agreement for an advisor who is concerned about your standard provisions.

Alternately, there are standard forms that might work if you’re really tight on capital. Here are two good ones worth considering:

• Founder Institute’s standard advisor form
• Upcounsel’s standard advisor form

Maximizing advisor contributions

It’s not enough to just find and recruit advisors. You have to take the initiative necessary to maximize the value of their contributions. For the most part your advisors will be very busy, and it will be up to you to ensure that you pull the most value from them.

Just as importantly, you ought to minimize the management overhead associated with your advisory team. Startups feature a crushing enough workload as it is.

I’ve seen advice to focus on transactional engagements with advisors to minimize overhead. I think that’s a poor idea. Instead, a regular and thoughtfully managed advisor system can ensure that your advisors remain informed about what’s going on with your business and the industry so you don’t have waste cycles on reeducating them every time you interact with them.

And a properly managed advisory board can offer a very strong incentive for busy advisors to brush up on things and bring their A-game to advisory board meetings; the last thing they want to do is look like an idiot in front of their advisor peers. And it forces you to think through your needs and goals in advance, which is likely to lead to a more efficient and productive meeting.

Advisors also tend to have strong opinions and inherent curiosity. And for many (most?) advisors, their role helping you is a rare chance to indulge in things that interest them rather than having to be disciplined about their own affairs. As a result, it’s not unusual for them to barge into areas of the business where they’re uninvited and maybe even unhelpful. At least there is a risk they won’t focus on the areas where you need them most.

That’s why I recommend some best practices for working with advisors:

Structured interactions

It’s best to set a regular cadence for core interactions with advisors. For example, I recommend monthly calls and quarterly in-person meetings with your advisory board. This ensures that they can plan ahead to be available, and makes it easier for them to maintain an awareness about what’s going on. If you let things stretch too long between engagements, they’re likely to forget context, requiring inefficient catch-up cycles and increased confusion. That’s not to say that you shouldn’t reach out to advisors on an as-needed basis as well. It’s just that your ad hoc requests should be on top of regularly established interactions.

Share information in advance

You should prepare advisor briefs in advance of advisory board meetings—something like a board deck. You should also try to do brief write-ups before every meeting or call with an advisor to give them context. This ensures that you can spend your time creating value rather than catching up. Experienced business people are accustomed to walking into meetings well-informed.

Focus on specific issues / asks

Most advisors help companies at least in part because they enjoy it. And because they’re not involved in the daily activities of the business, they are often curious about how things work, and may have ideas about directions the business could go. I would recommend listening to whatever it is they want to talk about, but balance that with the need to keep them focused on the task at hand. That’s why your write-ups and initial framing of conversations should be very specific. I recommend listing the specific area(s) where you’d like their help, and where possible describe the options you’re considering. Once you’ve framed the issue(s), you’re much more likely to cover the things that matter most to you.

Conclusion

Advisors can be a critical resource for startup teams. Yet many founders make strategic errors in assembling and managing their advisor group. Don’t assemble a fake or perfunctory advisor team. My advice is to treat it as an important initiative that will require time and energy to achieve full potential.

Embarking on a digital transformation effort is full of potential risks. According to McKinsey, 70% of digital transformation efforts fail.

And while there are dozens of reasons your transformation initiative might run into trouble, there are a couple of consistent traps we’ve encountered. As with most situations there’s usually some pareto optimization opportunity. We believe avoiding the following 5 issues will address a big chunk of your risk as you start a digital transformation project inside your organization.

1. Faulty digital transformation premises
2. Lacking a product deployment skill set
3. Attempting to tackle digital transformation alone
4. Not having digital transformation mentors
5. Not proving the value early on

Faulty digital transformation premises

If you’ve seen the movie The Big Short you’ll recognize the following quote: “It ain’t what you don’t know that gets you in trouble. It’s what you know for sure that just ain’t so.”

It’s a wonderful encapsulation of what led to the irrational exuberance leading to the 2008 financial crisis. It also illustrates something we see in large companies engaged in digital transformation.

For example, many large companies are in the throws of an end-to-end ERP upgrade to the cloud, or are planning to get started. The premise is that an ERP upgrade gets you the business results that increase profitability and improve operations.

But this is simply not true. We’ve consistently seen evidence of this. Big company implements large ERP provider. 36 months later they have difficulty identifying where the value is.

This isn’t because implementing a cloud based ERP is a bad idea. It’s a great one. But typically this gives you the capability to do new things with your data that can create opportunities for profitability and operational improvements. It doesn’t accomplish it by itself.

Driving innovation without a production deployment skill set

Many companies now have Chief Digital Officers in place. Some have innovation teams, perhaps even an innovation fund. Even organizations lacking a formal innovation governance model have begun holding design sprints, hackathons and other innovation-focused activities.

These are great things to have. But often what’s missing is the skill set necessary for bringing the ideas that are surfaced into full scale operations.

Unless you’re a team that’s experienced at bringing a wide variety of emerging technology to market from concept to successful user adoption, your teams will struggle. Many large companies have learned this lesson the hard way.

Many of these initiatives are expected to be implemented by the existing IT and development infrastructure. But the workflow for these types of engagements is typically much different.

With innovation projects, you’re ideally engaging in rapid, iterative testing. You’re delivering incremental value in the form of minimum viable products, testing or piloting them with internal or external stakeholders, and rapidly improving them as you deploy to progressively larger sets of users.

There’s also often the issue of technology stacks. While the end product will need to integrate with internal systems, you’re often better served early by using technology stacks that favor speed and ease of deployment over enterprise systems.

Even organizations like SAP have realized this, providing the ability to “bring your own language” (BYOL) into build packs that work seamlessly with the larger enterprise architecture.

But adopting those languages and knowing how to leverage them is often met with (understandable) internal resistance. And so teams attempt to execute on lean startup principles with legacy or enterprise tools that make such speed and agility difficult.

Going it alone.

“Data is a team sport”.

“There’s no I in team”.

“If you want to go fast go alone. If you want to go far, go together”.

All of these maxims get at the same idea. If you’re going to innovate, do it as a team.

This doesn’t necessarily mean mean staff up and build internal skill sets, although in many cases that might make sense. But in many cases, it pays tremendous dividends to bring in outside team members to either execute or provide perspective.

Teams with start up experience know the fastest path to market is often cobbling together existing third party solutions. They focus on staying close the user, delivering value, and accept that code or platform debt is part of the trade. They fully expect to replace and refactor work later. That’s a perspective internal teams often lack.

Likewise, people with startup backgrounds (and to a lesser degree agencies) often have pattern recognition that internal teams are missing. Part of why DI is able to provide value at a product strategy level is our experience building startups ourselves. That perspective is incredibly helpful when trying to make product decisions like:

• What data represents leading indicators for success or risk.
• How to design for internal power users and novice external users simultaneously.
• How to overcome chicken and egg problems when dealing with marketplace businesses.
• The critical importance of first time UX and how to optimize for adoption.
• The techniques that can drive referral and increase incremental revenue.
• How to architect products that have self-priming growth loops.
• The potential pitfalls with conversational interfaces or voice enabled devices.

Even if your internal team does the heavy lifting, bringing in an external team to provide guidance and feedback can be incredibly helpful.

A common approach we have leveraged is to co-create solutions, in the beginning leaning heavily on the external team for execution while the internal team learns the ropes, and over time transitioning more responsibility to the internal team as they develop the necessary skills and perspectives.

Not having mentors.

Along the same lines, a common gap is the higher level strategic perspective. While internal teams have deep domain expertise, they also often suffer from being too close to the problem, or lack the pattern recognition that comes from seeing a variety of different industries and organizations.

Again startups provide a great model. Most venture backed startups have a board of directors, as well as third party advisors. These are indispensable resources who can identify and help avoid potential obstacles, bring novel solutions from other industries, and make business-transforming introductions. They also can provide coaching to navigate the slog of iteration.

Creating a similar board of advisors, either at an individual initiative level or at the portfolio level, can often mean the difference between success and failure.

Not proving the value.

According to Harvard Business Review, a common pitfall is lacking a clear hypothesis around value creation.

This mistake often leads to funding projects that were executed on well but still had no value associated with the win. Allocating resources, driving team energy and engagement, and crossing the finish line without tangible value creation to point to can often be the kiss of death for innovation teams and the leaders within them.

Successful innovation initiatives often take time. And there’s a definite need for patience and sufficient air cover at the executive level to give fledgling ideas room to iterate and find value. But that doesn’t mean you need to start an engagement with no clue how you’ll extract value out of the other end if successful.

Modeling out potential options for growth and value creation, understanding the potential addressable market, having a hypothesis for “exit” (even if that exit is simply bringing the initiative in-house) are all great strategies to leverage to de-risk this pitfall and ensure that successful initiatives represent legitimate wins for the organization.

Create plans to mitigate these digital transformation risks

As you put your digital transformation agenda in place, it can be helpful to outline all of the potential risks you can see and develop mitigation strategies for each. Conduct a “pre-mortem” – try to anticipate what could go wrong, and make sure you have the right systems, processes, and partners in place to execute on your digital transformation initiatives brilliantly.

We have mentioned previously that corporations often view innovation as a useless buzzword. So often the word has little or no substance. But if you believe that innovation really is important—at least the kind that has substance—then you’re not alone:

“Business only has two functions: marketing and innovation.”
—Peter Drucker

And for good measure (and from the person who developed the theory of “creative destruction”):

“Carrying out innovations is the only function which is fundamental in history.”
—Joseph Schumpeter

Yet it’s worth exploring, both to assess whether we’re right, and if so to understand why. Knowing why will power our conversation about what to do about it.

Maybe we can set a threshold by finding some industries where innovation is not particularly important. And if we have trouble identifying any, that might go a long way towards establishing that innovation is broadly important.

Is innovation important in government?

Governments tend to be slow and stable. Significant change can take decades. After all, stability is one of the key demands we tend to make of government. Without a stable government it is difficult to maintain a stable currency, assured property rights, reliable market transactions, and public safety.

Perhaps governments should avoid innovation, which can be risky and destabilizing.

Sometimes it’s not that easy. The Arab Spring of 2011 resulted in very rapid change in at least seven governments. And it’s nothing new; history is replete with revolutions and other forms of civil unrest. That sort of existential risk seems like a good reason for innovation in government—whether to crack down on it, or avoid it. So there’s likely a tension between stability and innovation when it comes to government.

Even long-term stable governments such as the US seem to be constantly evolving. The US federal government typically enacts between 200-500 laws per two-year Congress. Some involve commemorative coins, but others such as the JOBS Act or Dodd-Frank are more far-reaching.

So, while we’ve only changed the US Constitution 27 times in 200+ years, we’re still actively evolving some pretty important parts of our government. Stable governments frequently evolve in the face of changes in technology, society, and international affairs.

If that’s not a form of innovation, I don’t know what to call it. And given that it’s one of the key activities of government, innovation seems awfully important even for government.

What about highly regulated industries?

Maybe innovation isn’t particularly important in highly regulated industries. After all, they’re likely protected from most threats by regulatory moats.

Taxis

Most major cities strictly limit the number of cabs out on the streets, as well as their appearance, tools / technology, and pricing. Those constraints tend to work in favor of incumbents. That’s probably why Yellow Cab Company, which formed in Chicago in 1907, flourished for almost 100 years. But it succumbed to ride sharing services, filing for bankruptcy in 2015.

Banks

US banks are highly regulated. And the government effectively erected a defensive moat by only issuing one new bank charter between 2010 and 2017.

That, combined with heavy regulation after the 2008 financial crisis is why in 2007 the top three US banks held 20% of the country’s deposits, but as of the end of 2017 they held 32%. In the meantime they have allocated capital to technology improvements, but little to innovation.

Apparently, business as usual is working pretty well for them.

Yet digital disruption is a looming threat to even the largest banks. CB Insights writes eloquently on the topic, explaining that “emerging companies generally don’t attack incumbent players head-on, but rather focus on tackling specific verticals.” They go on to show an image of “Unbundling The Bank,” which demonstrates the multitude of smaller players eating away at the various components of traditional banking.

My friend Tom Loverro at IVP wrote a great post in 2014 pointing out the existential threat to the banking industry posed by startups:

“Non-bank actors are attacking just about every valuable and revenue-generating activity that traditional players engage in”
— Tom Loverro

The government is speeding things up with its new fintech special purpose bank charters, which will make it easier for new players to displace traditional banking. These fintech innovators often focus on underserved markets to rapidly establish meaningful market share. For example, the 20%+ of US households that are underbanked. And capital partners are ready and willing to finance this disruption: $31 billion alone went towards fintech investing in the US in 2017.

Electric utilities

Electric utilities in the US are typically monopolies whose profits are effectively set by regulations. Surely they aren’t threatened by disruptive innovation. Their maximum profitability is determined by what’s called a “rate base” calculation derived from a percentage return on assets, with a US average of 14.5% as of 2015 (read this blog post by Coley Girouard on Advanced Energy Perspects for more information).

But as Coley points out, changes in dynamics in the energy industry are putting significant pressure on electric utilities. Aging and outdated infrastructure combined with improvements in energy efficiency at the edges of the grid—for example residential solar—are threatening the stability of the entire industry.

At DI we have engaged with a number of major utilities in the US. It’s clear they are very aware of the need for innovation even in this regulated monopoly industry. Apparently even a government mandated monopoly and preset profitability model aren’t enough to protect an industry from disruption.

What about intellectual property monopolies?

Government regulation can create effective barriers in other ways. For example, intellectual property rules might protect some industries or organizations from disruption.

Pharmaceutical companies

Drug discovery can be extraordinarily expensive—it costs on average over $2 billion to bring a drug to market. But successful new drugs are typically protected by patents that create effective monopolies for up to 20 years (usually less due to patenting process).

Health care rules and procedures often ingrain effective treatments into “standard of care” rules that generate guaranteed revenue streams for many drugs. Distribution, insurance coverage, and channel partnerships favor incumbents, too.

But it’s not all unicorns and rainbows for the pharma industry.

Regulatory and market dynamics are putting drug companies in a tough spot as health care costs rise, and pharma costs are put in the spotlight. The solutions that promise to lower discovery costs—such as AI or “in silico” drug testing— or to improve overall efficiency—such as the rise of PBMs (Pharmacy Benefits Managers) and digital patient engagement systems—are alien to pharma companies.

Other new technologies such as CRISPR and 3D drug printing and trends such as personalized medicine present increased chances for leapfrog innovation. Meanwhile, companies such as Amazon are more comfortable with disruption, and have increasingly powerful positions in the consumer and health care markets.

As a result, they’re edging into important parts of the drug market, and putting pharma companies at increasing risk. Even normally conservative hospitals are banding together to enter the drug business. It’s definitely a brave new world for pharmaceutical companies.

Consumer Packaged Goods

CPG companies enjoy intellectual property protections that can create competitive barriers to entry. Their trademarks and trade dress rights combined with marketing expertise and massive manufacturing and distribution reach can lead to significant long-term profitability.

Proctor & Gamble (P&G) was founded as a soap and candle company in 1837. P&G is known as one of the most innovation CPGs in the market. In 2013 they did almost $83 billion in revenue. By 2017 they had dropped to $65.7 billion—an almost 21% decline.

Much of this is due to dropping about 100 underperforming brands to focus on their big money makers. That strategy resulted in a much smaller drop in EBITDA during the period: $17.5 billion in 2013 down to $16.8 billion in 2017. Meanwhile their market value during the period grew from almost $200 billion to almost $240 billion.

Yet some argue that P&G’s recent success is unsustainable without significant change. Per a 2016 NY Times article, P&G’s CEO suggested that the “rise of e-commerce and social media has reduced the cost of entry for new competitors.”

One key element of that is the increasing importance of direct-to-consumer sales, for which large CPGs were not built and are not well suited. But it’s probably much more than the rise of e-commerce that presents a threat to major CPGs.

An article from BCG presents it in stark terms: “[s]cale was once all important. On its own, however, it no longer guarantees competitive advantage… FMCG companies… need a new playbook.” Niche products and brands are taking market share away from the large incumbents. They point out a number of factors including: asset-light production, expanded distribution options, variable-cost marketing, and ease of coordination as underlying factors enabling the small players to beat the big ones at their own game.

How about network effects businesses?

Network effects can create substantial competitive barriers to entry. From railroads to faxes, network effects have been instrumental in the formation of sustained value creation. Are businesses that benefit from network efforts largely insulated from disruption?

Microsoft built an empire based in part on network effects. As described in a 2008 NY Times article:

“Microsoft attracted consumers and software developers to use its technology, the software that controls the basic operations of a personal computer. The more that people used Microsoft’s operating system (DOS and later Windows), the more that third-party developers built products to run on Windows, which attracted more users.”

That same article points to Google as the new king of network effects, suggesting that the Internet and open standards have eroded Microsoft’s competitive advantage.

Despite that, Microsoft’s share of the PC operating system market was over 90% in 2013, and remains almost 83% today. So while their desktop market share is declining, it remains dominant. Their profits during the period have remained strong with the exception of a significant dip in 2015. In mid 2018 Microsoft reported their best fiscal year ever both in terms of revenues and profitability.

It certainly appears that something is working for Microsoft. But they have run into some significant challenges as well—challenges which might persist for the long term.

Their business has been built around a partnership model rather than a direct-to-consumer model. While they have had some success in gaming, their pursuit of mobile and music have largely failed. Their enterprise business is thriving, but given the rising power and importance of direct-to-consumer models, it’s probably not safe to say they’re not out of the woods yet.

Businesses such as Facebook have also leveraged network effects to build powerful competitive barriers to entry. Once most of your friends are connected to you on a social platform, it’s probably hard for another platform to convince you to switch until they have both a persuasive reason and enough of your friends to create a critical mass. Given the cost of building a mass market social network, that probably makes it hard to fund competitors, on top of the difficulties challenging a network effects incumbent.

But it’s not impossible.

Instagram and WhatsApp both came at the market from a different angle, and Facebook ended up buying both of them. If their competitive power were unassailable, Facebook probably would not have had to make those acquisitions.

Snapchat has also made a good run at Facebook. That is part of a larger—and potentially existential—threat to Facebook from the changing dynamics of young consumers.

Many see Facebook as the product their parents use, and therefore less desirable. At the same time, the fundamental structure of Facebook may not weather well as consumers continue to explore new modes of social interaction and engagement.

Facebook obviously isn’t going away any time soon, but that doesn’t mean that they don’t need to continue to focus on continued innovation.

Local businesses?

So far, we have not done a great job of identifying industries where innovation isn’t important. There’s another type of business that might do the trick for us: local businesses.

Think hair salons, private schools, or independent retailers. They tend to be driven by geography, personal relationships, and brand intimacy. Most of these sorts of business models haven’t changed considerably in the past several decades. Are they mostly immune to disruption?

For decades, independent retailers were a key part of the fabric of our economy. They have long benefitted from personal relationships and geographic ties.

At first they were pressured by big box retailers such as Wal-Mart. Many went out of business. And then e-commerce and delivery systems combined to create an increasingly viable solution to bypass small and large retailers alike.

Big retailers have by-and-large adapted with their own e-commerce offerings. Independent retailers, despite their competitive advantages of geography and personal relationships are increasingly falling down in the face of market (and social) evolutions.

Hair salons are a good example of a local business. They, too, tend to be built on personal relationships and geographic ties. On top of that they are a service-oriented offering, and thus should be harder to disrupt via e-commerce. Their fundamental business model hasn’t changed for over 50 years.

Are they at risk of disruption? They are at least at risk of losing meaningful business.

Take for example the brands offering a direct-to-consumer e-commerce solution for hair coloring and related products. The same article mentions a hybrid solution where a salon professional applies a direct-to-consumer formula and teaches consumers how to do it. Or what about Salon64 and its hybrid salon, workspace, and coffee bar?

Even service oriented local businesses probably aren’t safe from disruption.

What about private primary and secondary schools? In most bigger US cities private schools occupy an elite niche, commanding their market due to a persistent supply demand imbalance—in favor of the schools. Parents fret about getting their kids into the right school. It has been happening for decades, and is only getting worse (for parents).

But there are warning signs for schools. New entrants with innovative technology and learning experiences are making a push for market share. Even WeWork is launching its own school called WeGrow.

Parents are understandably reluctant to try new things on their kids. These new schools are made possible in part by the exclusivity of the schools, and the increasing cost of private education. It’s likely that many private schools in the US will be faced with a need to consider more aggressive (e.g., higher level) innovation.

Innovation is broadly important

If you think your industry is mostly immune from the forces of disruption, you’re probably wrong. Virtually every industry is facing real threats—and in many cases existential threats—from disruptive innovation. There are many potential responses:

• Stick your head in the sand (perhaps the most common)
• Improve your ability to innovate internally
• Engage with an outside firm to enhance your innovation capabilities and to create new value creation models / products
• Invest in and / or acquire promising startups
• Utilize a hybrid approach (probably the best approach)

All of this brings up an interesting question… have things changed? Is innovation increasingly important? Those are the questions I will address in the next post.

Digital Intent uses a specific definition of innovation that we believe provides utility for innovators. But our definition is still pretty broad. Given the work we do, certain types of innovation are more meaningful than others.

We propose a framework for distinguishing innovation based on four levels of increasing complexity. This approach is neither the only framework — there are many — nor necessarily the best for every purpose. But we do believe that it is the best framework for understanding how to pursue different types of innovation most effectively.

Existing Innovation Frameworks

Consultants and academics often codify their experience into frameworks, designed to abstract experience into principles that can be broadly applied to a variety of situations. By operating within frameworks, they’re able to quickly navigate complex problems and arrive at novel solutions.

Many experts have described ways to distinguish between different types of innovation. What vectors of differentiation do they tend to use?

Our goal is to identify a consistent way to differentiate between various types of innovation that will inform us about when it’s appropriate to change our process and decision-making.

Extent of innovation

Some distinguish innovation based on the extent of novelty, complexity, or change involved. The OECD describes four different types of innovation, one of which is incremental innovation:

“Incremental innovation: innovation that builds closely on technological antecedents and does not involve much technological improvement upon them.”

“Incremental” and “sustaining” are two words commonly used for modest or low risk innovation. In a corporate context we often talk of “exploitation” (versus exploration) as well. The other end of the spectrum is described with a variety of words, each of which incorporates some nuances or variations of meaning. Examples include:

• Disruptive innovation
• Breakthrough innovation — generally used to describe significant, complex, and particularly meaningful innovation
• Radical innovation — similar to breakthrough, but more likely to emphasize the novelty or risk associated with the process

Disruptive innovation is a term coined by Clayton Christensen in the mid 1990’s to describe a specific form of creative destruction which is rooted in Joseph Shumpeter’s concept of creative destruction.

Steve Blank called out Christensen (and Shumpeter’s) theories in a 2014 speech:

“Disruptive innovation – this is the innovation we associate with startups. This type of innovation creates new products or new services that did not exist before. It’s the automobile in the 1910’s, radio in the 1920’s, television in the 1950’s, the integrated circuit in the 1960’s, the fax machine in the 1970’s, personal computers in the 1980’s, the Internet in the 1990’s, and the Smartphone, human genome sequencing, and even fracking in this decade. These innovations are exactly what Schumpeter and Christensen were talking about. They create new industries and destroy existing ones. And interestingly, in spite of all their resources, large companies are responsible for very, very few disruptive innovations.”

The term has become very popular, but has also been critiqued. Christensen defended his theory in 2015 by re-emphasizing the narrow scope of what he considered disruptive.

Specifically, Christensen’s term described innovation by upstarts targeting unmet needs in “low-end footholds” that the incumbents had ignored or been unaware of. He also clarified that for something to be disruptive (according to his definition at least), the existing market had to consider it undesirable at first. He went on to argue based on these tests that Uber is not a disruptor. Commence head scratching.

It appears, by the way, that Steve Blank probably understood disruption to be a broader concept than Christensen intended. After all, automobiles, television, and the fax machine are hard to fit into the notion of a low-end foothold.

Christensen has since walked back from his position that Uber is not disruptive, but the fundamental fact remains that the term he popularized—to him at least—is quite narrow, and rooted primarily in Joseph Shumpeter’s original concept of creative destruction.

The concept of creative destruction, while useful, is still largely a theoretical construct. Christensen inarguably contributed to economic theory with his notion of disruptive innovation, but it’s not clear to us due to its narrowness that it offers a particularly useful rubric for understanding innovation.

When most people use the word disruptive innovation, they probably mean the broader notion of innovation that disrupts an industry. That’s the meaning we assign to it at Digital Intent unless we specifically invoke Christensen.

In this broader conceptual vein, using the extent of innovation to distinguish types of innovation seems useful. If you’re doing something incremental, it seems more likely that traditional management orthodoxy would be appropriate. If you’re doing something disruptive, it seems much more likely that you would need to adopt new strategies to accommodate. But still, the terms we’re discussing feel like blunt instruments that don’t offer enough utility for our purposes. Let’s keep looking.

What you’re trying to improve

Another way to think of innovation involves what you’re trying to improve. For example, the same OECD report mentioned above describes product, process, marketing, and organizational innovation. Each of these appears to distinguish between the types of resources or capabilities an organization is trying to improve. An example:

“Product innovation: development of new products representing discrete improvements over existing ones.”

A different approach has been written about by Doblin in its Ten Types of Innovation. They focus on the ten different innovation targets, including: profit model, network, structure, process, product performance, product system, service, channel, brand, and customer engagement.

It’s definitely a worthwhile read. Jay Doblin, the cofounder, was an iconic designer with expertise in systems thinking and design theory. As a result, their approach tends to be derived from a product design perspective, and may not be as useful when it comes to the broader context, and specifically in the context of technology and business model innovation.

Steve Blank also talks about process innovation, but his version is different and would probably be labeled incremental or sustaining innovation by most:

“Car companies introduce new models each year, running shoes grow ever lighter and more flexible, Coca-Cola offers a new version of Coke. Smart companies are always looking to make their current products better – and there are many ways to do this. For example they can reduce component cost, introduce a line extension or create new versions of the existing product. These innovations do not require change in a company’s existing business model.”

In fact, today this sort of innovation seems a lot more like everyday execution than innovation.

Steve also talked about business model innovation, which probably falls under the “what you’re trying to improve” umbrella.

What’s a business model? That has been described many different ways, much like the definition of innovation. And similar to innovation, we have a preferred definition, specifically Blank’s:

“A business model describes how your organization creates, delivers, and captures value.”

Business model innovation and disruption often go hand-in-hand; successfully creating new ways of creating, delivering, and capturing value is often disruptive. (By the way, around Digital Intent you’ll hear us use the term “value creation model” frequently in place of business model. That’s a nod to the fact that commercial value creation isn’t the only game in town.)

Is defining innovation based on what you’re trying to improve the best approach for our purposes? Probably not.

Innovating a product or process, for example, probably has a lot of similarities from the perspective of team, organization, and the path it takes—and as a result aren’t as useful for our clients trying to take a strategic approach to innovation.

It’s probably more important to understand how radical the change is than what’s being changed. The exception might be business model innovation due to its likely implication of breakthrough or disruptive innovation (when successful).

Time horizon

Ralph-Christian outlines a concept he calls “Dual Corporate Innovation Management” that involves a balance between exploitation-oriented and exploration-oriented initiatives.

He identifies three (time) horizons for innovation based on balancing exploitation (“enhancing existing business models or technological capabilities”) and exploration (“novel, and often disruptive, business models or technological capabilities”) for establishing organizations. He advocates an innovation management system based on a portfolio approach driven by a gap analysis. Importantly, he points out that exploitation and exploration require different approaches to achieve success. In the end, he describes three horizons of innovation: core, growth, and future. His approach is useful from a corporate strategy perspective, and is rooted in traditional management orthodoxy. But it’s not really aimed at distinguishing different types of innovation.

McKinsey also describes three horizons of growth:

• Horizon 1 — maintain and defend core business
• Horizon 2 — nurture emerging business
• Horizon 3 — create genuinely new business

As they say, “Time, as noted on the x-axis, should not be interpreted as a prompt for when to pay attention—now, later, or much later. Companies must manage businesses along all three horizons concurrently.”

This seems like a sort of GTD (Getting Things Done) for corporations (Inbox, Today, Someday). It’s useful as a tool for allocating time and energy, but not as useful for understanding the nature of the innovation you’re undertaking, and not particularly informative about how you organize, lead, and manage the team that’s involved. It’s also not particularly useful outside of the context of an existing corporation.

Greg Satell’s 4 types of innovation

Greg Satell describes a matrix-style approach he describes as the “4 Types of Innovation.” His model incorporates the notions of domain and problem, asking the following questions:

• How well is the problem defined?
• And how well is the domain defined?

From that, he outlines a four-part matrix that includes:

• Basic research
• Disruptive innovation
• Breakthrough innovation
• Sustaining innovation

A key insight he offers is that when the marketplace shifts, often “innovating your products won’t help—you have to innovate your business model.” We find his approach to be insightful and interesting. But we believe his emphasis on problem as one of the vectors misses an opportunity. Whereas clarifying a problem is very important, particularly early on, we don’t believe it’s as useful to focus on that vector.

Digital Intent’s 4 levels of innovation

Digital Intent borrows from Greg Satell’s approach, but replaces the system (e.g., the value creation model) for the problem. Our two vectors:

• System is the value creation model (e.g., business model)
• Domain is the combination of resources and context, including underlying technology, geography, and other components to the system of value creation.

We also change the nature of the questions asked. Instead of asking how well the vectors are defined, we ask “how familiar is it?” The answer ranges along a spectrum from familiar (to the team), to unfamiliar (to the team), to novel (to the world):

Known to me — New to me — New to the world

This enables our framework to view the vectors more simply and objectively. It also more explicitly accounts for both the novelty and the relationship between the novelty and the organization involved.

We believe this new approach changes the model significantly, making the framework more useful. For example, this model makes it more natural to recognize tiered levels of innovation, which we found to be useful when we first looked at distinguishing innovation based on extent (above). It also recognizes that innovation can be experienced differently for different organizations.

Digital Intent’s four different levels of innovation:

Level 1 — Optimization: Familiar system and domain.

This is a known system of value creation using known resources and technology. These sorts of innovation tend to have clear proxies, and offer predictable timing, budget, and resource requirements. It’s easy to measure and manage risk, and it’s reasonable to expect a steady upward progress. It’s arguable this isn’t even really innovation, although we think it’s fair to use the term here as long as we’re careful to differentiate from other types of innovation.

Level 2 — Change: Unfamiliar system and / or domain.

There’s nothing truly novel (to the world) here, but something is unfamiliar to the team. Perhaps it’s the implementation of new (to you) technology or a new (to you) revenue model. This sort of innovation still offers a relatively predictable path, but it’s riskier and harder to predict than level 1. These innovations tend to take longer than level 2 innovations.

Level 3 — Transformation: Somewhat novel system and / or domain.

Level 3 innovation often involves new technologies and business models, albeit not radically so. Usually there are somewhat close proxies or partially understood or known technology. It starts to become hard to predict path, timing, budget, and required resources. Risks are very hard to assess. Often the complexity is in the combination. This sort of innovation tends to take a longer time.

Level 4 — Breakthrough: Truly novel system and / or domain.

Breakthrough innovation is the process of bringing to market a truly novel business model or domain. Either or both of the system or domain is new to the world. These sorts of innovations tend to take a very long time and are fraught with risk and uncertainty. They also tend to create significant value when they’re successful.

The Benefits of Innovation Frameworks

This approach to distinguishing types of innovation has the benefit of simplicity, with only two factors to consider. Each of the levels also has natural implications for key factors such as level of uncertainty and risk, volatility, predictability, typical timelines, etc. That’s useful because these are the factors that typically force changes in strategy, organization, and process to maximize the odds of success. By tying them naturally to the levels of innovation, we are increasing the usefulness of the framework.

The framework also works both in the context of a corporation, and for startups. And it recognizes that what might be transformative for one organization might be mostly change or optimization for another.

It has some weaknesses as well. The rating of the vectors is subjective. The model also may miss nuances (or perhaps substance) in the differences between innovation based on system vs. domain. After all, core technology innovation likely has meaningful differences from business model innovation.

Overall, however, we have found it a useful approach to differentiating innovation for the purposes of understanding how best to drive efficient value creation for our clients.

Definitions matter; it’s hard to have effective discussions and build on concepts without them. And the best definitions offer utility.

Mention the word innovation in a corporate context, and you’re likely to see some eye rolling. The word “innovation” has turned into something of a buzzword whose application is so broad and nebulous that it often feels useless.

But innovation is critical in the modern economy. That’s why we’re going to come up with a clear and useful definition for innovation.

For our purposes, a useful definition of innovation probably:

• Has easy to understand tests for filtering
• Reliably filters out the sorts of activities that set corporate eyes rolling
• Rarely excludes things most of us would consider meaningful innovation
• Is brief and simple

Various definitions of innovation

It won’t take much Googling to see that the definition for innovation varies widely. Here are some paraphrased selections from an article by Nick Skillicorn based on interviews with 15 innovators:

• Application of ideas that are novel and useful
• It’s about staying relevant
• Great idea, executed brilliantly, and communicated well
• Feasible, relevant offering with a viable business model perceived as new and adopted by customers
• Introduction of new products and services that add value to an organization
• As long as it includes “new” and addresses customer needs, any variation goes
• Fundamental way companies bring constant value to their customers
• Work that delivers new goodness to customers in new markets and radically improves the profitability equation
• Implementation of something new
• Implementation of creative ideas to generate value
• Anything new, useful, and surprising
• … and so on

There are good concepts in those definitions, but none seem to strike the right balance of clarity, utility, and brevity. We probably need to look elsewhere.

Webster’s definies innovation as “the introduction of something new.” It’s hard to dispute that’s accurate. But it doesn’t seem particularly useful.

Perhaps that’s because innovation exists outside of our business mindset. By their definition, introducing a new paint color that’s a half-shade different from any others known in the market is innovation. Most of us would not consider that “innovation,” at least not in a useful way. It might be the absolutely perfect shade for your new house, but I think we need a better definition for our purposes.

Peter Drucker said “Innovation is the specific instrument of entrepreneurship… the act that endows resources with a new capacity to create wealth.”

It’s eloquent. The concept of endowing resources with a new capacity to create value is interesting. But still it doesn’t feel like a particularly useful definition.

Scott Berkun thinks “Innovation is significant positive change.” That’s starting to sound better to me. He adds the notion of “significance.” If it doesn’t matter (to someone), should we call it innovation? I think not.

But this definition focuses on the outcome rather than the process. If you don’t know whether something is innovative until after the fact, it’s hard to apply the definition proactively. And while the word “change” implies novelty, it’s probably too broad. What if we’re looking at a change to a well-known business model that results in a loss in stock value but an increase in stability? There’s something novel about that, but it doesn’t feel like innovation to us.

Tim Kastelle writes innovation is “not just having an idea—but executing it so that it creates value.” I like that he incorporates three key concepts: an idea, execution, and value creation. You’ll see below this is probably the closest to our definition.

A proposed definition of innovation

Here’s how we define innovation:

“Innovation is the process of creating value by applying novel solutions to meaningful problems.”

The reason we believe it has utility is in the three explicit tests for “innovative-ness” we can apply:

• Is it novel? The notion of novelty is baked right into the word “innovation.” If it’s not new, it’s probably more optimization than innovation.
• Does it solve a meaningful problem? If not, maybe it’s art instead of innovation. That’s not to say art isn’t valuable, but it’s generally not designed to solve a problem. To us, innovation is.
• Does it create value? If not, maybe it’s an invention rather than innovation. Inventions can lead to value creation, but usually not until someone applies them through innovation.

It also points out that—at least where we’re concerned—innovation is a process. It’s not the result itself per se, but the path to get there. We know it’s possible to innovate unsuccessfully—there’s plenty of evidence that innovations often fail. What’s key is that you’re seeking to create value by applying a novel solution to a meaningful problem.

According to this definition, innovation isn’t limited to the commercial context. Non-profits can be innovative; their value creation metric is simply different.

The Value of Definitions

Don’t underestimate the role that definitions and language can play in getting your team on the same page. Even revisiting the way your organization defines this can unlock new ideas and help organizations prioritize initiatives. And it ensures that your team is making meaningful progress toward creating truly innovative solutions.

A growth model is one of the best tools we’ve seen for product management.

It helps you plan for exactly how you’ll get to success. It helps you identify the levers you can manipulate to acquire and keep users. It can tell you whether the expectations you’ve set (or have been set for you) are realistic. And they help you prioritize decisions for what to build or optimize next.

Growth models have become increasingly common in high growth startups. But we’ve never seen an enterprise product team implement one in our consulting engagements, nor have we seen one when listening to startup pitches at our sister company’s venture fund.

Which is unfortunate. Growth models aren’t just helpful when you’re riding a rocket ship. They can be useful pre-product market fit (and even pre-launch) to validate assumptions and provide rationale for how you’re going to get from here to there.

We first heard about growth models from Brian Balfour, and our approach is heavily influenced by his. Here’s how to create one.

Identify your north star metric.

The first step in building out a growth model is to identify the key metric that will determine product success.

While there will certainly be other metrics you’ll be monitoring, there is tremendous value in identifying a single metric to focus your team.

A good north star organizes everyone’s thinking. It helps you communicate with your team (or investors, or growth board or steering committee). And in our experience it tends to get teams to move faster since they’re trying to improve one thing.

A good north star metric is typically a retention or engagement metric of some kind. For most products, unless you have solid retention you don’t have a viable long term business.

It’s important to keep in mind the nature of your customer relationship – you might have a desire for your user to engage with you on a daily basis, but that might not map to their desire. My mortgage company might want me to have their mobile app on my phone and for me to log in regularly, but if I never have to deal with my mortgage company again I’m happy.

Some examples of good north star metrics:

• Daily active users: good for collaboration apps that are an essential part off my job (email, slack, CRM, etc) or social sites.
• Monthly active subscribers: good for box model or subscription sites. It sounds like it might be good for SaaS products as well, but it typically is a lagging indicator – ideally there is usage data that you can identify that is predictive of eventual churn if you don’t intervene.
• Marketplace businesses usually have some version of a chicken and egg problem. The answer is usually to prioritize the supply side of the equation, as this will lead to the demand side if successful.

That said, consider the nature of your business. WhatsApp prioritized number of messages sent per day, realizing that they were competing with SMS and had a usage pattern of many times per day if successful.

LinkedIn for a long time prioritized number of profiles created. While total accounts is usually something that savvy investors would discount (they care about active users, not total users), LinkedIn realized recruiters were how they made money, and recruiters cared about having lots of resumes to search on.

Figure out the inputs that lead to that metric.

Let’s say you decide monthly actives are the critical metric. The next step is to figure out how to get there.

The goal is to identify the levers that will get you to your end state. Each of these can be manipulated to increase (or decrease) your north star metric.

Acquisition

For acquisition, ask yourself what marketing channels you plan to use. What do you think are realistic numbers for each in terms of visits? How do you think you’ll get there?

Take SEO for example. Often a team will say organic search will be one of the ways they get customers. But how will they get them?

Well, organic search is a function of the number of keywords you rank for, the search volume for those keywords, and the expected clickthrough rate for ranking in a particular position. It’s also likely that ranking will take time.

You’ll most likely be engaging in some content marketing strategy coupled with link building. So how many articles will you create each month? Make a guess on average search position across all keywords. Assume that you won’t show up on page one for a while, and that it will take 12 months at least to reach a top 3 position. What does that look like?

Again, the point of all this granularity is to help you frame expectations for yourself and your team. “We’ll get users from SEO” sounds great. But without clear assumptions for how you’ll get there you’re stumbling in the dark and likely to create disappointment when you can’t hit those numbers.

The same should apply to all your other channels:

• PR: how many articles will you show up in? Can you estimate the average page views for an article on that site? What’s the expected click through rate on an article?
• Paid Acquisition: what channels will you be using? What’s the estimated audience for each? What’s the anticipated cost per click and clickthrough rate?
• Social: how do you anticipate growing your audience? What are reasonable numbers for organic reach? How many posts will you be publishing each month?

Activation

What happens once people land on your site? At DI we talk about activation instead of “user registration” because activation is more predictive of long term engagement, and there’s often a drop off between registration and them engaging in your core experience.

Identify the steps users have to take between visit to registration, and between registration and activation. Create some estimates for conversion rate for each step (if you have data already, plug that in instead. Try not to throw up.) Now you’ve identified the areas of product friction within your product and can work toward reducing them.

Referral

People often get confused when trying to model out referral. But it’s not that difficult – the key again is granularity.

Identify every opportunity to refer that makes sense. There are often more opportunities than simple having an “invite friends” screen. For example, lets say I have a collaborative todo list app. I could have an invite screen during onboarding. I could have another one from the dashboard. And I could have an “assign task” flow whenever a new todo is created. Again, take into consideration the nature of the customer relationship. I’m probably not going to send an email to my contacts suggesting they get a mortgage.

For each, document each step and make assumptions on conversion rate. On the invite screen, the user will send X number of invites on average. X% of users will see the invite and click through. X% of those will create an account, and x% will activate.

Add those new users to your total activated users. What you’ll quickly see is the power of referral. While it’s rare to have an app that’s truly “viral”, most apps can get some lift from referral. Which drives down your CAC.

Retention

You have to plan for churn. And for most products, churn is actually pretty steep. Look for baseline metrics for retention 1 month, 3 months, and 6 months later. And build that into your model in the form of cohorts.

Don’t be surprised when it looks ugly.

Once you’ve documented all this, step back and play with your model. You’re probably going to see that it’s going to take WAY more work than you thought to hit your numbers.

That’s okay. Kind of like getting your financial house in order, the first step is to confront reality. You’re not going to get on Techcrunch and be staggeringly successful. It’s going to be grind.

The growth model helps you understand what it’s going to take. It can help you and your team steel yourself for the slog that’s about to come.

Use it to manage your team

The real power of the growth model is as a management tool. You don’t want to create it once time and then ignore it. As you start executing, plug the results into your model. Adjust your assumptions as you start to capture real data.

Use the model to prioritize decision making. Whenever you’re discussing a new idea for a feature or product iteration, ask yourself which variable in the model is it going to impact and estimate what that impact will be. this can be extremely helpful in seeing which work will have the biggest impact if successful. It can help diffuse conflict around what to focus on (or salespeople who rely on anecdotal data, or a boss that’s typically the loudest person in the room.)

Use it to set realistic goals for your team. If you’ve decided to prioritize SEO, it’s helpful to show your team how much content they’re going to have to crank out and how many links they’re going to have to acquire to hit their numbers. It can help you be realistic while also creating urgency.

Get access to our growth model template.

There are few tools we’ve seen that have been more useful for product teams than a growth model. It sounds tedious, and the model will 100% be inaccurate. But the time it takes will be more than worth it in terms of setting expectations, prioritizing the right things, and measuring impact.

We’ve created a growth model template with detailed assumptions for a fictional product that you can customize to fit your needs. Fil out the form below to get immediate access.

Progressive Web Applications (PWAs) could dramatically change the application landscape. They combine the best things about the web with the best things about native applications, allowing you to build rich web applications that can live on a user’s device and take advantage of native functionality.

In a nutshell, Progressive Web Apps are sites that can self-install on your device. It makes it possible to fully or incrementally offline an app onto your device.

The Chrome team has made big investments in this technology with their Service Worker and Web App Manifest specifications. And as of April of this year, Apple quietly added support for these two specs as well. That means that, with some limitations, progressive web applications are ready for prime time.

Benefits of Progressive Web Applications

They are responsive.

PWAs work on any device. While universal applications have addresses the issue of designing or different device types to a degree, you still have to deal with different platforms (android vs. iOS vs web). PWAs hold the potential to address this.

They work offline.

One feature of PWAs is “service workers”, which allow for offline connectivity. For field sales teams or other situations where there is low (or no) connectivity, PWAs will be able to function where web apps can’t.

They feel like native apps.

A big part of the appeal of native applications are the interactions and navigational concepts that have emerged. PWAs have similar functionality, allowing your app to have the same fast, responsive feedback and microinteractions for users.

PWAs also can access various functionality at the device level. This includes things like geolocation, the accelerometer and other sensors, the camera and audio, Apple Pay, and more.

Simpler growth hooks.

DI thinks a lot about how the growth hooks you can leverage inside of products to drive distribution, retention and referral.

PWAs have the potential to be more discoverable via search engines. For anyone who’s good at SEO and who knows the costs of mobile app installs, this could be a boon. Housing.com was able to drop its user acquisition costs on Android from $3.75 to just 7 cents.

PWAs also have less friction for registration and onboarding since they are easily shared with a URL (which also assists in referral). And since they can take advantage of native functionality like push notifications, they can facilitate a retention loop and make it more likely you can build user habits.

Faster iteration.

Your speed through the build-measure-learn loop is critical to getting from your initial product to a product that users love. Since there is no submission process, updates are available immediately. This also means that companies could publish internal applications for their employees without having to distribute through the app store.

Save Money.

You can also upgrade your existing web applications to PWAs, which saves you a ton of money vs. building native app experiences. While this probably isn’t a replacement for your mobile apps for consumer products, for internal enterprise applications it might make a lot of sense.

Limitations of Progressive Web Applications

There are still limitations to PWAs. On iOS, you can only store files up to 50MB offline. They can’t access some device-level functionality like Bluetooth, Face ID, AR Kit, etc. They aren’t able to run in the background, and they don’t have any access to user information stored on a device (like contacts or app passwords).

Android has more functionality – you can store larger files, you can access bluetooth, leverage push notifications and more.

That said, many of these limitations are likely to change in the next 12 months. PWA support was extremely spotty 12 months ago.

Progressive Web App Examples and Success Stories

The success stories for PWAs are early, but compelling.

• George.com saw a 31% increase in mobile conversion and 2x lower bounce rate.
• Bookmyshow.com increased conversions by 80%.




• Twitter Lite increased engagement by 65%, while using only 3% of the device storage space.
• The Weather Channel had over 1 million users opt into push notifications and saw an 80% decrease in load time.

Start Building PWAs today

For any organization with a large field operation, or people gathering data in locations with unreliable connections, PWAs can be a fast and effective way to increase efficiency. And for everyone else, PWAs hold promising potential to create more engagement and drive increased conversions.

We’d love to help you bring the power of Progressive Web Apps to your organization. Reach out to schedule a collaborative workshop or design sprint, or for more information.

Voice is increasingly becoming a preferred method of interacting with devices. It’s easy to see why. Voice-based interfaces are:

• Fast. On average, users can speak 150 words per minute, but can only type 40.
• Easy. Users don’t have to stop what they’re doing. Voice interfaces fit into our lives more seamlessly.
• Personalized. Answers can be tailored based on the user’s location or context, and based on previous interactions.

Thanks to the proliferation of APIs and platforms currently available, voice interfaces are relatively easy to stand up. However, most interfaces don’t get used. They solve non-existent problems, or they do so in a way that is frustrating for users. The end result is a dismal level of retention and ongoing use for most skills.

DI has spent considerable time over the last year wrapping our heads around the implications of the voice revolution. Having built voice interfaces for telecommunication companies, meal delivery startups and more, DI has learned what works in the space and the pitfalls to avoid in crafting a usable voice interface.

Prototype early and often.

By far the most important piece of advice is to prototype early and often. Your voice interface will be much different than your web or mobile app.

You’ll likely discover that a step of your process is super clunky, or that additional steps are necessary to give the user the information they need. Plan for several iterations.

Avoid bad reviews.

Reviews play a big role the rankings and discoverability inside any platform, and the big voice platforms are no different. Even if you have a responsive cadence for providing feedback and fixing problems, it’s rare for customers to update reviews after the fact. Your best bet is to hit them off at the pass.

Set good expectations.

One great way to avoid unnecessary bad reviews is to tell your users what they will and will not be able to do with your skill. Put it in the description of the skill itself. Put it in Alexa Cards. Put it everywhere else you do promotion.

Identify edge cases.

Your users are going to behave in strange ways. Expect it. Create graceful error messages to guide users back on the right track. Consider how the app will respond when users provide all, some, or even none of the necessary information.

Be careful about wording.

Often phrasing that sounds good on paper doesn’t work when translated to voice. For example, if you ask “would you like X or Y”, don’t be surprised if users respond with “yes”. Focus on keeping your answers short and concise – Alexa speaks slower than a normal human would.

Think about brand.

Just because an interface doesn’t have any visual elements doesn’t mean it’s not an opportunity to build or reinforce your brand.

Ask yourself what your product or service’s personality is like. How would they talk to other people? Craft your copy to match.

Naming matters.

Don’t assume your users will understand how to pronounce the name of your skill. Don’t assume that it sounds the same regardless of accent. Test it. Also focus on what language consumers would use when trying to find your app. Don’t use jargon – think about them like keywords, because that’s what they are.

Make sure you have analytics set up.

Just because it’s a voice interface doesn’t mean you can’t capture data on how users interact (or don’t) with it. Study the most successful paths users take, and figure out how to funnel more of your users through those paths.

Obsess over “completed query percentage.”

By far the biggest predictor of retention is the percentage of queries successfully resolved on the first try. Users get frustrated with buggy voice interfaces much more quickly than other modalities.

Tracking and iterating to improve this percentage is a great way to boost retention, which can have a huge impact on growth. In fact, a 20% increase in retention results in nearly 4x the growth rate.

Add functionality.

While you want to constrain your application to a limited set of use cases, you’ll often find places where users go off the rails, or identify use cases you hadn’t thought about before. Adding this functionality can increase your completed query percentage, which increases satisfaction.

Keep user requirements simple.

Every additional piece of information you require reduces your completed query percentage. Figure out ways to make the interaction simpler.

Domino’s pizza, for example, has default order functionality inside of user accounts, allowing users to say “Alexa, order me a pizza.” and have it complete that task without asking for any additional information.

If a request does require multiple steps, make sure you keep the session “open” – customers get frustrated if they need to invoke your app for every request.

Use text to streamline onboarding.

There is not currently a fantastic way of linking user accounts with an Alexa skill. The best approach we’ve seen is to leverage text messaging.

Ask the user for their phone number (which, unlike email, has a high likelihood of being captured successfully) and send them a text message to verify their account and finish onboarding.

Place subtle triggers into your skills to drive retention.

One of the biggest issues with driving retention is the inability to send notifications – every interaction must be initiated by the user.

One sneaky way to train users can be to remind them at the end of a completed query to use it again. Something as simple at “here is your daily briefing”, or “check back tomorrow” can provide subtle, even subconscious cues to your users.

Leverage existing channels to drive adoption.

You’re not going to get enough traffic yet from the various app stores to drive significant volume, and paid ad networks don’t exist yet either. Your best bet is to leverage your existing assets – social, email, your website and others.

You probably need to augment off the shelf tools with your own.

While out of the box APIs can get you a long way, they’re often insufficient. For example, a product catalog probably needs to be backed by a fuzzy, full-text search, because people don’t say full product names, i.e. the full name of a Purely menu item.  Rather, they might say, “add the steak” to my cart.  They might also list multiple items in one breath.  Alexa will figure out what they are saying, but an API without fuzzy matching won’t find any matches for that specific menu title.

Start innovating with voice interfaces today.

It’s still early in the lifecycle of voice interfaces, and new patterns will emerge as customers and brands figure out what works and what doesn’t.

But being early to any platform usually creates a virtuous cycle. You learn faster than your competitors, you build an active install base, and you dig a moat. The friction inherent in getting an Alexa skill installed becomes an advantage once you’re on their device. If you can build a habit around your skill, they are highly unlikely to switch.

DI would love to talk with you about ways your customers could leverage voice to interact with your products or services. Don’t hesitate to reach out with questions or to discus how to bring voice interfaces to your organization.

Gamification suffers from vagueness – ask 100 people what they think it means and you get 100 different answers.

We think the best definition of game mechanics is from Gartner – they describe it as “the use of game mechanics and experience design to digitally engage and motivate people 
to achieve their goals.”

“Gamification is the use of game mechanics and experience design to digitally engage and motivate people to achieve their goals.”

An even simpler way of thinking about it is “nonfiction gaming”. You’re using the tools of game design to create environments and experiences that people – consumers, internal team members, etc – want to engage with.

What Gamification is NOT

Gamification isn’t badges. When you say gamification, people often immediately associate it with badges, points, and levels. While those are game mechanics you can certainly leverage inside of an experience, that’s a very simplistic definition.

Gamification isn’t the solution to a bad experience. Part of why gamification has become so popular is that it seems to offer the promise of magically increasing engagement in anything it’s applied to. And while there are many examples of where gamification has been used effectively (which we’ll discuss), it’s not a magic pill.

If your product or service is fundamentally flawed, incorporating game mechanics won’t make enough of a difference to turn failure into success. As we often say at DI, the best growth hack is a great product.

Gamification does not mean “games.” Gamification borrows from games using tools and techniques to make non-game experiences more game-like. But it does not mean your objective is to create a game where one doesn’t exist.

You can think of gamification on a spectrum. On the one end, you have micro-gamification. This is the use of individual game mechanics in a very tactical fashion to influence a specific metric. You can think of it as a subset of the larger discipline of conversion optimization. Conversion optimization can include elements of gamification, but not all conversion strategies leverage game mechanics.

SproutSocial is leveraging micro-gamification on this registration form to increase motivation.In the middle you have “gamified experiences”, where you are taking either a new or existing application and baking in multiple game mechanics across the entire user journey to create a much more engaging experience.

And on the far end you have full scale games. Here, the entire experience is a game, but it’s still designed to accomplish a business objective of some kind.

All three can be leveraged inside of companies, and all we’ve implemented all three for clients.

What Can I Do With Gamification?

The use cases for gamification are vast, but we generally put them into one of three buckets.

The first is to increase top line awareness and engagement with a brand. Game mechanics can incentivize users to participate and increase their exposure to your brand, and to get their friends to do the same.

There are many examples of this in action. Some of our favorite examples:

• MTV’s My Chart lets users create their video chart based on various game dynamics, and obtained 500,000 votes and 150,000 videos viewed within 3 months
• Buffalo Wild Wings ran a gamified campaign that generated more than 100 million social impressions as well as a 500% increase in participation rate.
• DI has worked with a company called Billow that leverages game mechanics for social promotions. Eight O’Clock coffee used a campaign we helped create generated 55,265 likes and 227k plays in a little more than a month. Another campaign they did for Chapstick got them over 1m story impressions and increased their fans by over 400%.

The second is to improve the onboarding, retention and use of new or existing applications. At DI we talk all the time about the customer funnel, which is basically the 5 phases of a customer’s journey with your product. Game mechanics can help improve metrics in the activation, retention, revenue and referral phases.

A few examples:

• Autodesk gamified their free trial, increasing trial usage by 54% and channel revenue by 29%.
• The SAP Community Network gamified its community platform, increasing usage by 400% and community feedback by 96%.
• Domino’s Pizza increased sales revenue by 30% using game mechanics in its app.
• Nike drives over 5,000,000 users to beat their personal fitness goals every day of the year with their Nike+ experience.
• DI worked with a community management platform called SocialQnect. They used game mechanics to create communities with hundreds of thousands of users with millions of threads in each.

The third use case is to increase compliance with internal systems and improve sales and support effectiveness. Some examples:

• Spotify replaced annual reviews with a mobile, gamified solution with over 90% of employees participating voluntarily.
• Google designed a Travel Expense System resulting in close to 100% of employee compliance for travel expenses.
• Engine Yard increased the response rate for its customer service representatives by 40%.

There are other use cases for gamification as well – you can leverage gamification to increase user happiness when interacting with an interface – what we call “delighters”. Easter eggs are an example. Effective use of delights can spur word of mouth, and particularly effective implementations can increase customer satisfaction.

The essential components of a gamification model.

A well thought out gamification model has the following pieces:

• Players – your prospective users or customers. When designing gamification loops we talk about them as players.
• Win State – your goal with the gamification loop. Your win state is where business objectives and player goals align.
• Actions – the series of steps necessary to reach the win state. Game mechanics are applied to actions to increase their effectiveness.
• Analytics and Feedback Systems – in order to know if your efforts are working you need both qualitative feedback from players and quantitative analytics.

Without these elements you either won’t have a complete loop, or your loop won’t be effective, or it will but you won’t know it.

So how do you build the model? The process is very similar to any other development project. DI’s process for gamification includes the following steps:

• Understand the mission from the business perspective and the player’s perspective.
• Identify the behavior change that accomplishes that mission, ideate around possible tools for making that happen, and design potential approaches.
• Prototype the solution, roll it out and test for efficacy.
• Iterate on the solution, making sure to monitor its effectiveness with each iteration.

Step 1: Understand

During the first phase, we are seeking to understand three primary things:

• The key business objectives we’re trying to improve.
• The players involved in driving that change.
• The motivations that drive those players so we know how to create an experience that accomplishes their goals and the business objectives.

Identify the business objective.

Gamification can represent a big waste of time if you’re unclear what it is you’re trying to accomplish. So clarity on business objective is imperative. Each step in the customer funnel has concrete objectives that can be optimized leveraging gamification.

• Activation: If you’re an ecommerce company or an app that has a registration component, you might be trying to increase the percentage of people who complete their checkout process or who successfully sign up.
• Retention: Most applications don’t spend enough time measuring and optimizing their engagement and retention metrics. Gamification represents a great mechanism to help users build habits.
• Revenue: SaaS businesses often operate on a “free to paid” model, where they give users a free trial and upgrade them after some duration or level of usage. Optimizing the % of people who upgrade through game mechanics is a huge opportunity.
• Referral: referral loops are a critical component of any application looking to have a positive viral coefficient. Game mechanics are one of the best ways to improve the effectiveness of your referral loop.

Note that these same principles apply for internal applications – you want to make sure as many team members as possible adopt, and continue use over an extended period of time. Otherwise your investments in new technology initiatives are doomed to fail.

An important note: often the business objective you’re trying to improve is NOT the business objective you’re going to design your gamified experience around. To figure what experience you need to create, it’s essential to dig into the root causes behind a problem instead of simply accepting it as a given.

For example, lets say your CRM tells you sales are sluggish. Rather than implementing a campaign to increase call or meeting volume, you first ask a couple questions and you discover the following:

• It turns out the CRM is reporting that data because your sales team isn’t accurately or expeditiously tracking their activity.
• Your team isn’t tracking their activity because they’re on the road all the time.
• They aren’t using the mobile app your CRM has because it’s frustrating to use.
• It’s frustrating to use because they haven’t participated in the training.

So we move from an activity issue to a tracking issue to a training issue. And perhaps the best step is to implement and gamify an initiative to increase the number of salespeople taking the training program on the CRM.

Identify the players.

Once you’ve identified the business objective, you need to figure out who the players are. In the above example, the obvious answer is your sales team.

There are certain situations where you’ll actually have multiple players:

• Most online communities are subject to what’s known as the 90:9:1 rule. It states that 90% of your users will mostly be lurkers, with 10% of your users contributing the most content and 1% being your power users who are on the community constantly.
• Marketplace businesses have at least two types of users, often in the form of buyers and sellers or a “supply” and “demand” side. Those users have very different motivations.
• Many apps have a consumer or end-user facing side and an administrative side. Both types of users are important – the software world is full of admin backends that never get used.

In each of these cases you have multiple types of players, and they’re all important. You’ll want to identify how you can create experiences for each of them that in aggregate help accomplish your business objective.

Identify the player’s goals.

The last step is understanding the player’s win state. We need to know what they’re trying to do, and what motivates them so the solution we create can compel them to take action.

BJ Fogg runs the Stanford Persuasion Lab. And he has developed a model for what he believes drives behavior change.

There are three elements to it – Motivation, Ability and Triggers. If a user is sufficiently motivated to complete an action, it’s sufficiently easy to do, and you show the appropriate trigger at the appropriate time, a user is likely to complete the action you want.

Gamification is about uncovering the behavior change you’re after that accomplishes your business objective and the player’s win state, then using game mechanics to increase a user’s motivation.

Behavior Change = Motivation + Ability + Triggers

So how do you increase their motivation? The goal of your experience should be to make the player feel awesome in some way. Awesome can take several forms.

• You can try to make your players feel smart. Which means achieving mastery of some kind. Learning new things. Solving puzzles. Accumulating knowledge.
• You can make them feel successful. That they’ve overcome a challenge, they won, they achieved something.
• You can make them feel structure. Which means that they understand their purpose, the larger mission, the goals and the rules are all clear.
• You can make them feel more socially connected. This could be friendly competition, collaboration, or even just bonding.

Inside an internal organization, salespeople are often motivated by success and social rewards. Customer support teams are often motivated by structure and success. Engineers are motivated by looking smart and socially rewarded. Customer personas will have similar leanings. Identifying them and understanding them are essential.

The danger of using money as a motivator.

Often companies will try to motivate players by bribing them. While this sounds like an easy way to avoid doing the hard work of understanding their other motivations, it can often backfire.

When money is used as an external reward for some activity, the subjects lose intrinsic interest for the activity. The introduction of an extrinsic reward has a permanently altering effect on the intrinsic interest. It does not matter how often or rarely you use an extrinsic reward, just the fact that the reward is introduced changes the game forever.

This mistake is commonly made in loyalty programs or apps with a store of value. Even here, redemption is not the core motivator of loyalty programs. Status is. Foursquare proves this, as does FarmVille. Money goes in. Nothing comes out. In fact, FarmVille did a promotion with 7 Eleven, but it wasn’t redeem your credits for Slurpees. Rather players bought Slurpees in order to get FarmVille credits.

Step 2: Ideate

With your objectives defined, your actors named and their motivations uncovered, you’re ready to start identifying the behavior changes that accomplish that mission – the series of actions that will lead to the confluence of your business objectives and the player’s goals. We call this the win state.

There is no silver bullet to gamification. It’s common to hear people starting out in gamification say “Let’s just add some points, and a leaderboard, and the natural desire for people to be at the top of the leaderboard will do the rest.” This is called PBL (points, badges and leaderboards). And it’s wrong.

This is wrong for two reasons. First, there are usually a series of steps that have to take place, or micro behaviors that have to change to lead up to a larger change that accomplishes your business objective. It’s essential to break each step down and identify solutions for each.

Secondly, badges, leaderboards and other gamification elements whose primary motivation is competition actually only appeal to a subset of your players.

The importance of a gamer’s type.

Professor Richard Bartle has classified humans as gamers into four types: Killers, Achievers, Explorers and Socializers.

• Killers want to beat other people. I want to win, and for you to lose. This might sounds bad, but often is a good thing. Killers are often the most engaged players inside of your community. You don’t necessarily want to discourage them, but you do want to shape their behavior. For example, a community app could implement an incentive system that specifically rewards positive comments with points.
• Achievers look similar to killers, but there is an important distinction. They want to win, but they don’t care or desire it to be at the expense of other people. These are people who are motivated by mastery for its own sake rather than the comparison of themselves to others.
• Explorers want to go places and find out stuff. These are the people who love to find easter eggs in applications, like the kids who knew all the secret levels in Super Mario Brothers. They will be the ones who kick the tires on all your features, who look for cracks in the system to exploit (not for the competition but for the joy of discovering the loophole,) and who create wikis sharing their discoveries with others.
• Socializers don’t care what they do as long as they are doing it with their friends. They want lightweight, non-confrontational, interactions with other people. FarmVille players were usually Socializers.

Perhaps the most surprising thing people find when we discuss gamification is the breakdown of each type of player. Many people assume that most players are either Killers or Achievers – that is how most gamification initiatives are designed. But in reality, Killers only make up about 1% of players. The most common player type by a huge margin are Socializers, who represent as much as 80% of the player universe.

These percentages obviously change depending on the type of players you are targeting – it’s likely your target players skew in certain ways. But at a minimum it’s wise to avoid the assumption that they will primarily be motivated by competition. In fact, cooperative games out-ratio in popularity competitive games by a factor of 3:1 according to Jane McGonigal.

Cooperative games out-ratio competitive games in popularity by a factor of 3:1. – Jane McGonigal

The importance of a player’s stage

In addition to the type of players you’re targeting, it’s important to keep in mind the stage of the experience the user is in. Their goals and expectations are different depending on how long they’ve been a user.

A good game gives players the feeling that they can master it. But as soon as a player masters it, the game cranks the difficulty up. This keeps them in a state of “flow.”

Compare that to any non-game. Business applications, complex image manipulation software, CAD programs, and even a simple banking website tend to offer the first time user a lot of complexity. The level of difficulty the system is exposing to the user is far above the skills that the user has at that moment. The system pushes the user into the “frustration zone,” the zone where the user feels overwhelmed and doesn’t know where to begin.

On the other hand, when a user becomes very familiar with the system, the system complexity tends to remain at the same level as at the beginning, thus moving the user in the “boredom-zone”.

The question that arises for a gamification designer is how to design business applications that balance the skills of the user with the exposed complexity of the system to keep players in the flow state as long as possible.

The importance of frequency.

Lastly, you need to keep in mind the nature of the application itself, and specifically the frequency of the behavior you’re trying to create. There are no right answers here, although certain types of experiences lend themselves to certain frequency patterns:

• Several times a day (email, FB, CRM, etc.)
• One to several times a week (banking systems, time sheets).
• Once a month (mortgage companies, utility billing systems, etc.)
• Only once (polls, quizzes, etc.)

Gamification has the power to actually increase frequency. But whether you should is a different question – if increased frequency doesn’t accomplish a business objective or a user goal, and simply represents a vanity metric, the answer is probably not.

So which game mechanics should I use?

We’re commonly asked which game mechanics work the best. But as you can probably guess by now, different mechanics work better for different players, with different dispositions, at different stages in an experience. This is why PBL doesn’t work – you need to be more nuanced, and pick the right tools for the job.

Some sample game mechanics are below. Any of these can be effective when used in the right context. But understanding the business objective, player, and the desired win state are imperative first steps.

Given that understanding, how do you decide which game mechanics to use? Different mechanics lend themselves to particular types of actions. The best approach is to ideate around potential mechanics that will suit the given action you’re trying to get players to take, and test each approach through prototyping.

Let’s say you’ve built a question and answer platform, and you’re trying to improve retention (your business objective). The “lightbulb moment” when players truly understand the app and its value might be when they’ve received a fast helpful answer to their first question (your user goal). The series of actions might look something like this:

• Players register for the application.
• Players ask their first question.
• Players receive their first response.

Let’s also assume that you’ve done the work to understand your player types. You’ve learned most of your new players aren’t killers or achievers, so appealing to status (or implementing game mechanics around that like PBL) might not be the primary mechanism for achieving this win state. However, you have a second set of players -the folks who will answer questions asked by your new players. Your data indicates they skew higher on Achiever.

An ideation process might result in hypotheses like the following:

The hope will be that some combination of these actions creates the most likely scenario to achieve your win state. Armed with your hypotheses, you begin to prototype.

Step 3: Prototyping

Prototyping gamification isn’t too different from other uses of prototyping. Your goal as always is to identify a viable approach as quickly and cost effectively as possible, using a deliverable that is high enough fidelity to communicate the final implementation with no additional waste.

At DI, prototyping consists of a series of steps, in increasing fidelity.

Start with sketches.

Starting out your goal is to get as many ideas down as possible, as cheaply as possible. And for this purpose it’s hard to be paper and pencil.

Sketching allows you to rapidly ideate around potential implementations of each game mechanic for the action in question. Your focus is on quantity, not quality at this point.

There are a variety of exercises you can conduct to make this process more effective, many of which we fold into our DI Design Sprint process. Below are some basic guidelines:

• Go for volume. We will often conduct time boxed exercises (10-20 minutes or so) where the goal is to get as many ideas down as possible. The forced constraint and the emphasis on volume forces you to avoid perfectionism. Drawings are crude, with just enough detail to communicate the point. We’ve found going for volume can actually increase the likelihood of novel solutions – your first ideas are likely to be rehashes of what you’ve done in the past, and new ideas tend to emerge once those ideas are exhausted.
• Leverage design patterns. This might sound a bit contradictory, but when designing solutions leveraging game mechanics it’s usually wise to have the team be aware of what the various mechanics are, and how they are best used. Just as knowing how the play the classics usually precedes being able to effectively improvise, so too does understanding the tools in your toolbox improve the likelihood of identifying novel implementations of those tools.
• Make it collaborative. Sort of. Sketching exercises can be effectively done in a group setting, but we don’t believe in collaborative sketching. Rather, the team engages in a series of miniature sprints, where everyone sketches solutions by themselves. You then come together as a group to discuss, combine and co-create the best implementation based on the individual solutions. To the previous point, it’s often wise to start with a session to train the team on the various game mechanics you can leverage – giving them their toolbox.

Move to mockups. Don’t worry about wireframes.

Once you’ve identified potential solutions through a collaborative sketching process, we advocate for moving directly to high fidelity designs, forgoing the traditional wire framing process.

Remember the goal of prototyping – you want to get something in the hands of customers with the least amount of waste. While mockups take a bit more time than wireframes do, in our experience wireframes are usually insufficient to communicate your intent to players. They simply don’t understand what wireframes are.

Your sketching process likely provides your team with a “good enough” version of a wireframe for the purposes of prototyping – you don’t have to have all the details worked out yet, until you know if you’re directionally on the right track.

Get out of the building and in front of customers.

At DI we often say customer feedback is oxygen. It’s how we keep ourselves honest, how we know whether our solutions are accomplishing their intended objective, and minimizes waste.

Customer feedback is oxygen.

Customer feedback is oxygen.It’s constantly a surprise how rarely organizations show intermediate deliverables to customers. Too many decisions at the tactical level are made inside of conference rooms, with people who are simply too close to the problem and who have baked-in incentives to see the solution work.

Customers, even loyal ones, don’t care about any of that. They will be much more likely to tell you what works and what doesn’t with your implementation, saving you considerable cost that would be incurred by waiting until the solution is live in the world.

Don’t be surprised to find it taking multiple loops to arrive at a viable solution. Humble yourself before engaging in a customer feedback process, and remember that any work spent iterating now is much cheaper than it will be to change later.

Do some math.

The last point relates to specific types of game mechanics. If you do end up implementing some form of a points system or have levels or other forms of social currency, you want to make sure the system has “balance.”

Good point systems have a concept of progressive engagement. They start out easy, giving players points for doing seemingly trivial activities in order to get them engaged and to help them build up an initial store of value (which increases their incentive to continue playing.) As they continue to play, points either become harder to come by, or more likely the number of points required to “level up” become larger. This is one way to keep players in the “flow state” as discussed previously.

You want to make sure your points system doesn’t have any obvious flaws in it. There are several things to look out for:

• Points are too hard to acquire early on.
• Points are too easy later.
• A certain level deviates from player expectations by either being disproportionately easy or hard relative to it’s neighbor levels.
• Certain actions have disproportionate effects – for example a certain type of action having too much weight in the point system relative to its difficulty, creating a situation where players “game the system.”
• Incentivizing actions in a way that players are too incentivized to engage in behavior that doesn’t address your primary business objectives.

The way you solve for this is by modeling your system out. You run it through a simulation to see how the decisions you make impact the game over time.

If you have an existing application, you can leverage your historical data to implement such a model. While your game mechanics will (hopefully) alter future behavior, it can still be helpful to see how your system would have rewarded past behavior – for example, seeing how many of your players would have reached certain levels in certain periods of time.

Step 4: Iterate

Once you’ve prototyped your solution and have customer validation that your implementation might be on the right track, it’s time to implement version one.

The complexity of your development will depend on the mechanics you decide to use – implementing a progress bar is much easier than implementing a full points system, for example.

What’s essential is to make sure the development team understands the system will likely change over time, and to plan for that change by creating a flexible system.

The importance of analytics and feedback.

In order for you to know whether the system works or not, it’s imperative you have two feedback mechanisms in place. The first is an analytics framework.

The most important consideration when evaluating potential analytics solutions is to capture event-based data, not simply transactional data. Transactional data are things like a successful purchase on your commerce site, or a registration for your SaaS app. Event data, on the other hand, are the series of actions the user takes leading up to a transaction – the pages they click on, the links they click, how long they spend on a page, the flow through your app, etc.

The reason this is important should be evident – since your gamification loop is leveraging game mechanics to influence a series of player actions in support of an win state, you need to know whether the actions work. And the actions will be captured most of the time with event based data.

Event-based data doesn’t just happen – it’s important when discussing gamification implementation with your development team that the logging of any event based data relative to the gamification loop be captured as well. Make sure this requirements are incorporated into the larger requirements or sprint documentation.

The second important piece is a qualitative feedback system. Analytics is critical, in that it tells you what happens. But it can often be hard to figure out the why.

Rather than creating a bunch of hypotheses for how to improve a step in your loop that isn’t working, you can save considerable time by asking customers directly, using that information to inform your iteration work.

A feedback system doesn’t have to be fancy. In fact, it doesn’t even have to be coded into the app itself. All you need is a mechanism to reach out to players who interacted with your loop, to understand why they took the actions they took.

You can do some of this work up front before deploying in the form of user tests. User tests are great at uncovering usability issues (or “ability issues” in the Fogg Persuasion Model) that you can address before deployment. They aren’t accurate representations of reality in that they completely neglect the motivation component. But by conducting them you can eliminate some of the barriers to adoption and get to a successful win state much faster.

Look out for unintended consequences.

One final thing to pay attention to are unintended consequences of the model you developed. While you will have addressed potential balance issues by running the models in the previous step, you might still miss aspects of the loop that make it easier to manipulate.

It’s important to assume that players will try to game the system. For example, lets say your app has a review mechanism to suggest vendors to users in a marketplace app. You implement a game mechanic that gives players an extra point in your points system for leaving reviews.

Not long after implementing the loop, you find that you have thousands of reviews. But on closer inspection, the reviews have one word statements like “Great!” or even “I haven’t tried them but I get points for leaving reviews.”

So you try to remedy this by building a rating system for the reviews themselves, giving people points for highly rated reviews. Problem solved!

Not so fast. Now you have people giving each other positive ratings on their reviews regardless of their actual content, creating “voting rings.”

Assume the system is going to be gamed, and look out for ways people use the app in unintended ways to exploit a loophole.

Remember that you want to avoid upsetting these people, as they are most likely your Achievers or Killers and can represent a disproportionate amount of activity relate to their size. Close the loophole in future iterations, announce the changing of the terms and explain the rationale behind it.

Build Version One Now. Learn What Works. Repeat.

You won’t nail the loop on your first pass – some aspect of it won’t work as well as you intended, or the system will disproportionate reward the wrong behavior. The solution is to get the system up faster, see how people use it, and use the analytics and feedback systems to improve it. Rinse and repeat.

It’s important to not get discouraged, and to make sure the team understands that big wins can take time. We once implemented a referral loop for a client, and it took 7 iterations to nail it. But once we arrived at the solution that worked, they reaped the rewards of more registrations and much less average cost per customer for years.

The sooner you begin working on your loop, the sooner you’ll see results.

We’d love to help

If you think you’d like to dip your toes into the gamification space and start trying some things, we’d love to help you make that happen.

DI has a full service team that can help you execute from start to finish. We can help identify the players and their motivations, identify the actions necessary to reach the win state, ideate around potential game mechanics to leverage throughout the process, even design and build the solutions. And we love to work inside of an iterative process to maximize the likelihood of success.

Gamification is a lot more involved than most people think. But a well-architected system can dramatically improve retention, referral and virality. To take your system to the next level, don’t hesitate to reach out.

Design Thinking is about creating solutions with the user in mind. Sounds obvious – of course the product you’re building is for the people who will use it.

But as many have found, without a solid process underpinning your work, it’s extremely easy to replace the user’s point of view with your own, to develop tunnel vision, to think about business objectives rather than user needs.

Design thinking provides a toolkit and methodology for keeping the user front and center throughout the design and development process. Ask 100 practitioners about their process and you’re likely to get 100 different answers. But there are some consistent themes underlying most design thinking approaches.

The Design Thinking Methodology

There are a variety of different methodologies that have been developed over the years, all of which are pretty similar. We leverage the methodology developed by the Stanford d.school:

Start with Empathy

By far the most important principle is to develop and deploy empathy. Before we knew what design thinking was, we realized that what separated great product designers from everyone else was the level of empathy they would develop for the users they were designing for.

Empathy is about understanding the user – their aspirations, their fears, their values. How they move about their day. What they use to make their lives better.

There are several methods for building empathy:

Brainstorming

The least effective way to do this is to brainstorm your way to it. Sitting in a conference room making assumptions about your users is better than nothing – at least you’re consciously thinking about them – but it’s not nearly as effective as getting outside of the building and actually talking with your users.

Customer development interviews

Customer development interviews are a much more effective mechanism to develop empathy. It’s critical these conversations aren’t your attempt to convince users about your solution – they are strictly about trying to understand their worldview, their lives, and their needs.

Observation

While proper customer development interview creation can gather a tremendous amount of insight cost effectively, sometimes it’s worth the additional effort to actually observe users in their environments. There can sometimes be a disconnect between what they say and what they actually do. Often their needs and values aren’t even obvious to them – watching what they do can sometimes uncover these unstated preferences and values.

Define a point of view

You’ll gather a lot of insights by talking to users. Before you jump to creating a solution, it’s imperative to gather all this material and attempt to make sense of it.

We’ve found putting everything up on a wall in a collaborative session with your team can be an effective way to attempt to find meaning and patterns. The specific methods you use – post it notes, user journey maps, experience diagrams, etc. – are ultimately up to you and your team. But what matters is getting it all up there where everyone can see it.

Preserving beginner’s mind

In spite of your best efforts, you lose beginner’s mind extremely quickly. You’ll often find yourself forming opinions after a couple conversations.

Having the big picture up on a wall can be helpful for you in forming new connections and challenging those initial assumptions. Likewise, having other team members see these insights for the first time will undoubtedly lead to new connections and insights.

With insights synthesized, you’re ready to define your point of view about the design challenge. This is often in the form of a problem statement – a clear, actionable guiding statement informed by your research, to be used as a north star for the steps that are to follow.

This step is often where things can go wrong. It’s been said defining the right problem is 90% of the work. Taking this step makes sure you are focused on the right problem, and that you’ve clearly identified what that problem is.

What makes a good problem statement

It sounds counterintuitive, but good problem statements are often smaller than you think. It’s a temptation for people to want to define the problem broadly. But this actually makes the steps that follow harder.

Good problem statements are often smaller than you think.

Constraints have a tendency to unlock more useful ideas. Tight problem statements also make it more likely you develop a compelling core experience in the final solution – and as we’ve discussed often, having a tight core experience is predictive of eventual product success.

Finally, having a tight problem statement makes it easier to know if you’re solving it. It reduces variables, helps you figure out the levers that matter, and makes it easier to measure success.

The Opportunity / Satisfaction framework

One tool that’s useful in this process is the opportunity / satisfaction framework. You’re going to uncover a bunch of potential opportunities. But not all of them are created equal. In many cases users are actually pretty satisfied with the existing solutions – even if that solution is something they cobbled together themselves. Displacing an incumbent is certainly possible, but it’s hard. And if customers are generally happy with it, your task will be much harder.

Great problem statements will galvanize your team, be easy to explain, will excite potential users, and will help you avoid the dreadful mistake of building a solution that tries to do too much.

Ideate potential solutions

Another pitfall in product design is focusing on a single solution too quickly. It’s rare the first idea is the best one. Odds are if it’s obvious to you it’s obvious to others.

Typically the best solutions come from looking at the problem from a variety of angles or facets, and focusing initially on breadth of potential solutions. Ideation is the process for developing those ideas.

While this can certainly be done by a single person, again it’s often helpful to have it be a collaborative process. Different people will bring different perspectives, which by definition multiplies the raw material you have to identify potential solutions.

The specific techniques or tools you use again can depend on your team’s preferences and the specific problem you’re trying to solve. It could simply look like brainstorming sessions. However, we’ve found certain frameworks or exercises can help frame the problem in a way that leads to more ideas.

How might we

One framework we’re fond of is called “how might we.” It’s exactly what it sounds like – asking “how might we” to subsets of the problem statement. They can stem ether from the problem statement itself, or from artifacts created during the synthesize step. User journey maps can be a great way to break the problem statement down to component parts, and provide plenty of fodder for good how might we statements.

You can conduct a series of ideation exercises on the the how might we statements the team thinks are most valuable to solve. This allows you to focus and constrain your ideation further than by trying to tackle the entire problem statement.

It’s critical that you capture as many ideas as possible early on before making decisions

There are dozens of other tools you can use – sketching, creating mind maps. Etc. But the common thread is to defer judgment. It’s critical that you capture as many ideas as possible early on before making decisions. To the degree you place value judgments on ideas too early, you effectively shut down the creative process and stifle the creativity that can lead to truly novel solutions.

Making decisions

Once you’ve generated a sufficient set of potential ideas, you can leverage discussions and other decision making frameworks to narrow and select ideas to focus on.

Dot voting is a popular approach – with the ideas all up on the wall, people walk around silently and place sticky dots on the ideas they think have the most potential.

We’ve found it’s helpful to provide criteria for evaluation. Leaving it up to people to determine their own criteria can sometimes cause participants to lose focus on what problem you were initially trying to solve, or the feasibility of a potential solution, or the strengths and weaknesses of the team. Criteria like “smallest level of behavior change required”, or “technical difficulty” or “most likely to delight” can be examples of such criteria.

Prototype

Prototyping is all about getting potential solutions in the hands of users as quickly as possible. They help you think through your potential solution, communicate it to stakeholders, and rapidly test potential solutions inexpensively.

Prototypes can take many forms – it often doesn’t mean code. It can be something as simple as written product concept. It can be mockups. It can be a clickable keynote. What matters is creating the prototype that allows you to get useful insights as quickly as possible.

Prototypes also don’t have to address the entire problem statement. You can prototype specific steps of the user journey, chaining them together over time as you hone in on solutions that are effective.

Test

Once you have your prototype in hand, you go back out and talk to your users to get feedback. Just as in the first step, a prototype is not about selling your solution or convincing them. You want to see how they interact with it, and have them tell you what they think about it.

You are very likely to identify issues that need to be addressed. This isn’t a bad thing – in fact it’s the whole reason why prototyping is valuable. The point of doing it quickly and inexpensively is to identify issues and reduce risk when it’s cheap and fast.

The critical importance of iteration

The worst thing you can do in the prototyping process is not use it to capture feedback and iterate in response. It’s not uncommon to find teams falling down here – they get attached to their ideas and aren’t willing to step back and go through the loop again. But testing is another opportunity to develop even more empathy and potentially refine your problem statement. To ignore this opportunity is foolish.

To the degree you can convince your team to focus on speed with prototyping, you can minimize this tendency. By setting the expectation that these things are meant to be thrown away, you can get them to move faster and test more. And by reminding them that the purpose of testing is to figure out what’s wrong vs. what’s right, you can set expectations appropriately.

Plan for as many iterations as you need to get this right. It’s tempting to build the final solution, but it’s almost always more expensive in the long run to have to iterate on the working product. Resist that temptation until you’re confident you’re on the right track.

Leveraging Design thinking in your organization

Design thinking isn’t dogmatic – different techniques and tools will work more effectively for you than others. What matters is committing to a process of developing empathy, and using the insights you generate to rapidly and iterative pursue potential solutions.

The best way to build these muscle in your organizations is to simply start. Start with a potential hypothesis or problem you’ve heard about from customers. Or simply start going out and talking to customers about their issues with your solutions or other problems they have. You’ll stumble through it at first, but you’ll learn what works and doesn’t for your organization by trying.

DI can help – our customer development sprints can help teach your team how to conduct user-centered interviews while gathering valuable insights in the process. And our collaborative design sprints can take those learnings and rapidly arrive at compelling solutions. To learn more about either, feel free to contact us.

One of the biggest impediments to innovation lies in the tension between operating the core business and pursuing new initiatives. Nowhere is this tension realized more fully than in IT departments.

IT as a rule is organized and incentivized to keep things running smoothly. They naturally develop cultures of risk aversion over time, primarily out of self-preservation.

They are responsible for implementing (and more importantly maintaining) any technology infrastructure the organization deems to be of strategic importance. They are often under pressure to deliver faster than they’d like, and the expectation is that everything works without a hitch.

As a result, risk management becomes the dominating consideration. And for good reason – if a core process goes down or has bugs, it can mean millions of dollars in lost revenue or additional costs. Failure is simply not an option.

The obvious problem is that an IT culture perfectly suited to maintaining the core business is woefully unprepared to deal with the ambiguity and perceived risk of new innovation initiatives. Telling a group of people to spend 80% of their time maintaining the core with excellence (meaning no mistakes), and 20% of their time experimenting on new stuff is often a recipe for failure.

And yet the imperative to innovate remains. And technology is usually a critical piece of the puzzle.

The ideal scenario is a distinct set of people – organized, managed and incentivized in ways that maximize the likelihood of success.

Bimodal IT – A Working Solution

The Bimodal IT architecture is a pattern commonly used in successful transformation initiatives. And it works.

According to a 2017 study by Gartner, roughly 4 in 10 CIOs reported implementing some form of Bimodal architecture. And of those, 71% reported Bimodal architectures improve the success rate of their innovation initiatives.

In a Bimodal architecture, there are effectively two IT teams working concurrently – what we call the Execution Team and the Innovation Team.

The Execution Team is tasked with maintaining and improving the core business. Great ones do engage in regular incremental innovation initiatives, working to continually improve “the way we do things here.”

The Innovation Team is tasked with exploration. This could be identifying entirely new business models, new ways of serving customers, or thinking about radical new ways of doing the work of the core business.

The consistent thread is an emphasis on experimentation. They understand many of their initiatives will fail. The goal is to test numerous ideas, killing the bad ones quickly. Release cycles are short, and prototypes and beta tests are the norm.

Both threads are critically important. It’s not about simply standing up an Innovation Team while the Execution Team engages in the status quo. The end goal is to reimagine both. What matters is recognizing the need for each, and setting each up for success.

Trying to combine both into the roles of the same people creates a tension that is difficult to overcome. Likewise, simply standing up an Innovation Team like some splinter cell of rebels or cool kids creates unnecessary political drama and fails to appreciate the need to reimagine the core as well.

While Bimodal makes intuitive sense, it can be difficult to stand up and implement. What follows are some suggestions that we’ve observed tend to increase the likelihood of success.

Start Small

A wholesale change effort is usually doomed. When trying to enact change of any kind, it’s typically best to start small. The implementation of a Bimodal architecture is no exception.

One effective approach is to stand up a Bimodal process for a single project. For a specific initiative, carve out a small group of people to represent your seed group for your Innovation Team. They work independently from your Execution Team, although they can come from your Execution Team.

The project should be small and self-contained. And the goal is not the success of the project itself, although you certainly want it to succeed.

The primary goal is to help this new team begin to form their own culture and identity. To recondition them to understand that their objective is to move fast, break things, and fail quickly on the way to finding out what works. Emphasis in this project should be on standing up some initial processes, equipping them with some tools, and focusing on learning.

In most organizations, having a strong leader (typically the CIO) providing sufficient air cover to the early team is critical to its success.

Integrate with the Lines of Business

In many organizations the general assumption is that IT owns all of the initiatives related to technology, assuming all risks but with little upside in the event of success.

In a Bimodal process, the technology leaders inside the innovation team become partners with the LOB owners. The LOB devotes its own full time resources to the initiative alongside IT. Ideally these resources are co-located for the duration of the project.

The project or product manager is ideally from the LOB – this can help ensure the team is clear where the ultimate ownership for the project lies.

You’ll often find within any LOB there is talent that is looking for a new challenge. They might be considering the startup world, and might be frustrated with the pace of change.

These initiatives can be a fantastic way to retain this talent, giving them the rush of excitement inherent in pursuing new initiatives with the security and ground cover of the existing operation.

Execution and Innovation Teach Each Other

One big pitfall in standing up these groups is the risk of cultures increasingly clashing. There can be a huge temptation for the Innovation Team to think that they can be the “creative” people, under no obligation to adhere to any form of process or rigor.

Likewise, particularly if the Innovation team pulls the more risk-tolerant team members from the core, there can be a temptation for the Execution team to become even complacent, assuming that any innovative thinking is the other team’s job.

It’s imperative that you watch for and snuff out this type of thinking. In a well functioning organization, these two groups will build on and learn from each other.

This again is aided considerably by the help of a strong leader who makes sure that both teams are recognized, and rewarded simultaneously, not simply the fledgling Innovation team. Language is also critically important. Using language that portrays both teams positively (Execution and Innovation vs. terms like “legacy”, for example) can avoid any unintentional devaluing of the core team members.

Make it clear one of the goals of the Innovation team is to identify practices and techniques that can be leveraged inside the core organization. While their team might be concerned with more disruptive initiatives, the reality is there are always dozens of opportunities to make incremental improvements inside the core.

The Execution team in many organizations can benefit tremendously from incremental innovation initiatives – moving from application silos to platforms and reusable code, the development of APIs or microservices, etc.

Uncovering, prioritizing and implementing these ideas take many of the same skill sets. Your end goal is a company that is innovative, not simply a department.

One way to do this is to have the Innovation team be a rotation program. People come into the program to learn the company’s approach to innovation. They get to practice them on real projects that matter. And then they return to the core business, often more energized and comfortable with change.

You can also make an effort over time to transition the Execution team to more “agile-ish” development practices. This gets both teams operating with similar terminology, development cadences and practices over time which can increase the perception that they’re in this together.

Teach your team to fish

A Bimodal architecture can resolve the stalemate between your execution and innovation initiatives. But getting started can sometimes be tough.

Many DI clients use an engagement with our team as an opportunity to stand up this Bimodal infrastructure. While pursuing a new product development or digital transformation initiative, DI team members simultaneously work on the product itself and teach the client’s internal team the tools and methodologies they need to ultimately to it themselves.

An easy way to start is with a Design Sprint, which results in a ton of momentum around an idea in as little as a week, and in the process introduces many design thinking methods to your team.

If such an initiative would make sense inside your organization we’d love to talk.

We live in a time of unprecedented disruption. Companies and industries are being transformed at an insane pace. The world is full of companies like Slack, which started in 2014, already has a billion dollar valuation, and is transforming internal team communication.

In the face of this rapid change, more companies have realized the importance of dedicated innovation initiatives. Great progress has been made in that direction – companies are standing up innovation groups, organizing hackathons, and orchestrating other ideation events with their team members.

And yet making innovation actually happen is incredibly difficult.

While many companies are putting the structure and support in place to get the ball rolling, getting actual products in the hands of actual customers is proving to be a much more difficult process than many companies anticipated.

Understand the Rules

The first stumbling block is often a lack of understanding on the “rules of the game”. A large organization has built up a system of “rules” that people know how to play. They know what success or failure looks like.

But innovation is a fundamentally different game, and requires different rules. Companies must define success much differently. And the rules dictating how innovation happens in the organization need to be articulated.

By definition, companies have become successful at scale because they’ve built incredible “execution engines” – they’ve become the best in the world at delivering their product to the world.

They’ve six sigma’d the delivery of that product to reduce as much variability as possible, in order to deliver a predictable solution to their customers every time. And all the systems, policies, procedures, tools for measurement, and incentives for their team are designed around that.

But innovation doesn’t fit into any of those boxes. An execution engine can operate the way it does because they’ve identified a product that solves a legitimate customer pain, and have figured out how to deliver that at scale.

Innovation is fundamentally different – it’s about uncovering or discovering a new solution. More than that – it’s about uncovering a new customer problem worth solving, then creating a solution.

That’s a messy process.

Frequently you’ll find the initial problem hypothesis isn’t legitimate. The problem is either a nonexistent one, or it’s not critical enough for customers to pay for to solve, or there simply aren’t enough customers to make it worthwhile. Finding a problem worth solving often involves thrashing and iteration, which can be uncomfortable for organizations not used to it.

Likewise, the solution is often not right at the outset. It either doesn’t solve the problem at all, or doesn’t do so in a way people are willing to pay for, or does but is too complicated or costly for customers to embrace. Getting to a solution that works again takes persistence and iteration.

This process is hard to execute when new ideas are held to the same standards as the execution engine. Little seeds of innovation are going to go through several metamorphoses en route to becoming something the world wants.

In the beginning, it looks a lot like wasted time and failure. But it’s not – it’s evolution.

Savvy innovation teams know this, but often they haven’t given the organizations an alternative way of assessing their progress. They haven’t delivered the organization a rigorous process, a framework for decision-making, or a paradigm allowing the organization to know whether they’re moving forward or simply lighting money on fire.

But they can. And the startup world has done most of this work for them. In the last few years a much richer language for describing this process has evolved – terms like “innovation accounting” and “validated learning”.

With new innovations, the fundamental job is to identify the hypotheses underlying a given idea and prove or disprove them as quickly as possible.

You do this by spending less time in meetings and more time in front of customers.

You do this by shipping minimum viable products (which can be as simple as a landing page or clickable prototype) and getting customer feedback.

You do this by rapidly testing unique value propositions instead of spending months debating on the right “big idea”.

You do this by staying close to the customer throughout their use of the product, listening to what they say and carefully watching what they do, looking for that “lightbulb moment” to go on for them.

You do this by forgetting about immediate ROI (scrutiny very few innovations can stand up to early on), and instead looking for sustained engagement, upticks in referral, improving customer lifetime value, and validation of channels that appear to drive cost effective acquisition at scale.

Embracing these tools and this language will take some time. Success requires the people holding the innovation team accountable understand what progress looks like. It looks like lots of tests, many of which fail, that systematically reduce the risk of the business the innovation is intended to become.

Adopt a Portfolio Approach

Venture capital learned long ago that, in spite of their brilliant team members and disciplined diligence process, they aren’t great at picking winners.

The solution has been to pursue a portfolio approach. They don’t know which individual idea will be a winner. But they know if they invest in 10 or 20 ideas, their chances of success dramatically increase.

Innovation groups should adopt the same mentality.

Organizations have some obvious advantages over startups. They have existing customer bases. They have access to customer insights many startups might not be able to come by. They have channel relationships they can leverage. But it’s dangerous to assume these advantages mean the success rate with new initiatives is higher.

Rather than putting a ton of resources towards any single idea, innovation teams should diversify. Identify 10 or 20 initiatives. Invest a small amount of resources to validate whether they have promise. Kill the ones that don’t earn the right for additional resources.

It sounds counter-intuitive, but pursuing a portfolio of innovation will result in better results, more rapidly. In the beginning, initiatives can be staffed with individual people. In fact, a single person can be simultaneously validating multiple ideas. Any development can and should be deferred until the fledgling idea has been sufficiently validated in the marketplace.

Become an Internal Champion

1% of innovation work is having a great idea. The rest is about execution. And in the beginning, when you’re trying to get buy-in to explore the validity of an idea, execution looks a lot like inside sales or politics.

Successful innovation teams have at least one person who’s job is to navigate the myriad minefields that are resident inside big co.

It’s not that the company is deliberately trying to stifle innovation – quite the contrary, everyone in the organization agrees on its necessity. But when you get into the details, tons of interpersonal issues can come up.

There are incentives issues. It can be challenging getting the resources you need – everyone has their own stuff going on, and if there isn’t a direct incentive for them to help you they likely will focus on the things they are being measured on.

There are risk issues. Many people in established companies are there precisely because innovation sounds a lot like risk. The idea of failing over and over again en route to a product that works doesn’t sound palatable. Getting those folks to take political risk to support your idea can be tough.

There are support issues. For IT in particular, it’s their job to think about the future of things. If the fledgling idea gets any traction, it’s likely to become absorbed into their world at some point, and dealing with disparate systems and code bases is frustrating.

There are ROI issues. As we’ve discussed, there will be people who think this new idea is a waste of time because it isn’t contributing to the bottom line quickly enough.

There is zero correlation between how good your idea is and how likely the organization is to embrace it. Learning how to maximize the chances of making something happen requires savvy internal champions.

It’s imperative innovation groups learn how to have the right conversations with the right people. They must know how to build consensus, what areas to compromise on without ruining the integrity of the idea or spoiling what makes it so disruptive in the first place.

They must be able to communicate the benefits of innovation accounting and the portfolio of innovation. While these approaches might initially sound like risk, it’s possible to model out how an organization can validate 10 ideas with MVPs for less than it costs to build a full-fledged version of a single idea.

Overcome Inertia, Get Momentum, and Start Getting Some Wins

To move past the inertia inherent in existing company structures requires a lot of work and political savvy. But often all it takes is a little bit of progress to get people excited.

When an innovation group can show their executive teams clickable prototypes, videos of users interacting with them, models rationally describing why ideas should live or die, and all of it with less cost and time than big co is used to, they can develop a reputation for making things happen and earn the right to truly transform the enterprise.

This is based on a presentation DI partner Sean Johnson has been giving for innovation groups at several companies in the last few weeks. If you’re more of a visual learner, we’ve made a video of his presentation below. You can also view the slides on SlideShare.

The Rise of the Third Interface

There have been various phases in how we have interacted with computers. The first phase was the Terminal Interface – using the command line or DOS prompt. This interface was embraced by early adopters but did not become mainstream because it required people to have a working knowledge of the guts of the machine and knowledge of the precise syntax to use to execute commands.

The second phase was the Graphical Interface. It used visual representations of programs, files and actions, leveraging many of the mental models people already had from the real world. This made it much easier for users to interact with a machine, and adoption took off.

But it still had limitations. It represented an abstraction and could lead to confusion. The field of user experience sprang up to help make these interfaces simpler and more intuitive.

A third wave has emerged, and it is what we’re calling the Conversational Interface. In some ways it returns to the simplicity of the command line, in that it is primarily a text-based medium. But it differs in that rather than requiring the user to know exact commands, it offers the ability for the user to interact with a machine (or a person behind a machine) using natural language.

This change is subtle but profound. At a minimum Conversational Interfaces are much more user friendly than the command line. But the promise and excitement around them is their eventual ability to parse complicated requests, execute them in real time and return a result that almost feels magical. Like talking to a person.

In short, we are moving from us having to learn how to interact with computers to computers learning how to interact with us.

“We are moving from us having to learn how to interact with computers to computers learning how to interact with us.”

Why are Conversational Interfaces Interesting?

It seems as though users clearly prefer conversational interfaces. Many of the web’s most recent success stories primarily leverage a conversational interface. In fact, in just 4 short years the top 4 messaging apps eclipsed the top 4 social networking sites (in terms of monthly active users).

And the rise of platforms like Siri, Google Now, Cortana, and Amazon Echo are going further. They don’t rely on typed out text, but still represent conversational interfaces, perhaps in the best possible way.

Why do people prefer these kinds of interfaces? We believe there are several reasons.

They feel more personal than apps.

Most native apps (and websites, for that matter), have the same experience for every user. It would be difficult to impossible to design a customized series of screens, with different language on each page to speak to each type of user, and with different imagery and visual aesthetic.

In a counterintuitive way, the removal of all of the artifice actually opens up the opportunity for a more personalized experience. As Jonathan Libov observed, “Language is the most powerful, useful, effective communication technology ever, period.”

This personalization can manifest itself in several ways. At its simplest, a retrieval-based approach to conversation is able to create a “choose your own adventure” style of interactions with customers. While the first couple questions are consistent for each user, it quickly takes on a unique experience.

This personalization can be magnified by remembering user preferences. Alexander Weidauer demonstrates a great example, showing how the question “how is my business doing” could be answered in two completely different ways depending on a person’s role in the company.

Conversational interfaces are more appropriate for many interactions.

In a series of customer development interviews about chatbots and conversational interfaces, we uncovered several kind of interactions where customers prefer them.

“I like the idea of not having to download an entire app.”

The first and possibly most important relates to the nature of interaction customers want to have with a brand. While brands are constantly pushing for deeper levels of engagement with their customers, customers don’t always want it. Mobile apps are a great example – of course companies want their customers to download their app. But customers treat their phone’s home screen as precious real estate, and simply are unwilling to give their mortgage company (or even their phone provider) a spot on that page.

Those same customers are much more willing to engage in a conversation through a platform like FB messenger. They know it’s there when they need it, but is out of the way when they don’t. While mobile apps still should represent a crucial piece of a company’s technology strategy, there are many customers they might not be engaging with because of the depth of relationship implicit in such a platform. Conversational interfaces allow them to engage those customers.

“I’d actually prefer to give my information to a bot. No judgment.”

The second insight is related to sharing personal information. When sharing things like medical information (a user’s weight, for example), financial information (how much they’ve saved for retirement) or their adherence to a plan of some kind, they actually prefer talking to a computer.

The belief is that unlike a person, who might consciously or unconsciously judge their choices, computers will simply provide helpful objective information.

“Sometimes I don’t want to browse – I just want you to tell me what to get.”

The third insight is related to curation. It is unlikely that a traditional ecommerce experience would be viable inside of a conversational interface. And there is plenty of evidence that the visual-based browsing modality is an enjoyable one for many people.

But there are times when a user would prefer not to have to hunt through your entire catalog, and would instead like to be guided in a much more focused way.

“My kids text all day. They don’t use email at all.”

The last insight was the strong preference for conversational interfaces with people in their teens and early 20s. Their content consumption patterns are dramatically different than that of older generations. They send hundreds of text messages. They live inside of Snapchat. They never check email. They use social sites less frequently than older demographics (with the exception of Instagram.) For companies or brands looking to reach teenagers, understanding the nuances, benefits and limitations of conversational interfaces seems wise.

They’re Available Wherever a User Wants

Many brands have been hesitant to play inside of the confines of the walled gardens that are FB, Whatsapp, etc. They (rightfully) understand that they are effectively renting their real estate from these companies, and in the face of quarterly earnings these companies continue to decrease organic reach and force you to pay for access. It’s possible that they might charge rent on your bots in the future as well.

But this mindset assumes that your app (vs. Facebook’s or anyone else’s) is your product. It’s not.

You are no longer a web app or a mobile app. You provide a product, service or experience. And that experience can and should be able to be delivered wherever is most convenient for your customers.

As Chris Messina puts it, “Conversational commerce is about delivering convenience, personalization, and decision support while people are on the go, with only partial attention to spare.”

This means that you need to think beyond the confines of your own app. It’s becoming increasingly important to think about your products and services in the right context.

Some companies have come to the same conclusion, and are rapidly experimenting. You can now order a Domino’s pizza from Twitter with a pizza emoji. You can order Taco Bell from inside Slack. You can order an Uber using your Amazon Echo.
These companies are happy to jump onto the next big platform because of the tremendous opportunity it opens up in terms of customers. Just as newspapers and televisions and shopping malls used to be the aggregators and platforms of yesterday, there are now a dozen or so platforms with hundreds of millions of customers, waiting to be reached.

One Interface, Multiple Departments

The last big benefit of conversational interfaces is the simplicity for users to interact with several departments through a singular interface. There doesn’t have to be multiple websites or phone numbers or email addresses to visit.

With the right plumbing behind the scenes, a user should be able to interact with several areas inside your organization through a single text field, just as a terminal allows you to navigate to disparate parts of your computer. This sounds simple, but the benefit to users is profound.

Messaging apps become the new home screen.

For these reasons, it’s not unreasonable to envision a future where users spend more and more time inside of the conversation view of various messaging apps, to the point that it effectively becomes their home screen.

In such a world, it becomes somewhat similar to email in that it’s natural for me to jump in and out of conversations with friends and brands. Having real estate in that threaded view becomes critically important in such a world. But unlike email, these platforms have made it impossible for users to receive spam – they have to opt into any communications with brands, and at any moment can banish them with forever with a couple taps. For this reason these platforms won’t suffer the same fate as your inbox – real estate on these screens will continue to be extremely valuable.

How to Make Great Conversational Experiences

So how do you take advantage of the opportunity that conversational interfaces present? The following are some usability and strategic guidelines we’ve identified in our research of existing implementations and through customer interviews.

Keep in mind we’re in the very early stages of this opportunity, and many of the things that are not currently possible will soon be trivial. The platforms will only become more powerful and useful for customers.

Conversational Interfaces vs. Bots

Many of the exciting applications of conversational interfaces surround the use of bots and machine learning. But conversational interfaces are powerful in and of themselves, even if they are augmented partially or entirely by humans. They’re great interfaces for on boarding new users inside of native apps, and many of the use cases we’ll discuss in this paper are just as relevant without the use of bots.

The interest in bots and the advent of machine learning that can actually simulate human conversation make conversational interfaces much more powerful. The benefits to the user are largely the same (meaning I can communicate using natural language and have the interface understand and respond to me intelligently). But by leveraging technology, it allows these kinds of interactions that previously required human intervention to happen at scale.

It’s Easy to Build a Bot. It’s Hard to Build a 
Useful One.

Building a bot is actually a trivial exercise – you can build one in a weekend without much trouble. But to make a bot that actually does something requires more work.

There are several layers to creating a useful bot experience. You have the bot itself, which will likely leverage one of a variety of retrieval based platforms to communicate with the user. It will allow your system to understand a user’s input and the intent behind their request, but it won’t be able to do anything with it by itself.

Your bot “brain” needs body of knowledge to support it and provide relevant answers. This will come from a couple places. The first is your app, or database, or whatever houses the information that is proprietary to your business or organization. The second comes from a series of integrations with the other tools your organization relies on – CRMs like SalesForce, your eCommerce platform, etc.

You likely will need some form of human interface with your system, at a minimum to monitor the interactions users have with your system to identify opportunities for improvement, and possibly to intervene and handle portions of the customer interaction directly.

And finally you have the integrations with the platforms themselves. Each will require its own code, has its own conventions and data structures, and likely has different user expectations. A user interacts with a bot differently inside of Slack than with their Echo – the system should adapt the experience to reflect those differences.

All (or most) of these layers are critical to building a bot that works well. But they don’t all have to be online at the outset.

Narrow the domain.

The most effective way to layer in functionality over time is to make sure you narrow the domain or scope of he interface. The less a bot does, the less integration work you have to do, the fewer opportunities there are for the conversation going off the rails, and the more likely you’re able to create an experience your customers appreciate.

When you first put a conversational interface into the wild, one of the first things people will do is try to break it by asking strange questions. But as long as you’ve done a good job of communicating the domain up front, they won’t get upset when it responds with canned variations of “I don’t understand.” Your user isn’t going to expect your customer support bot to have a conversation about politics.

Making sure you communicate the domain of the interface at the outset of a customer engagement ensures that expectations are properly set.

Start with your script.

Before you start working with one of the bot platforms or writing a line of code, spend some time thinking about how you expect the conversation to flow. You can leverage flow chart tools to think through when to branch, when to have open input vs. forcing the user to select options, when to show results and what those results should include, what the calls to action should be, and how you want an interaction to ideally resolve itself.

You can test your flows via SMS using a tool like Twilio (or even having a person behind the curtain using the pre-written responses) and doing some user testing. If it doesn’t work as an SMS conversation, it’s unlikely to work as a bot.

Spend a lot of time error handling.

Error handling is important for any type of application, but the importance is even more critical inside of conversational interfaces. Every interaction will either enhance or erode trust with users, which can increase abandonment.

The nice thing is that every conversation is like a log file – if you’re paying attention, every interaction with a customer is an opportunity to improve your interface. And unlike the app stores which can have 7-10 day lag times, you can update your conversational interface daily and have it instantly available.

Have a personality

Because the chrome of an app gets stripped away, there are limited opportunities to have your conversational interface reinforce the brand. The best tool at your disposal is going to be your copy.

Even if your interface is bot-based, you can and should take the time to craft copy that sounds human, and embodies the traits of your brand.

Your brand team has likely done this exercise before and already knows the personality of the brand very well, but if not it pays to spend time on that here. What would your brand sound like if it were a person? Would it be funny? Would it use formal language or be more colloquial?

Clearly visualizing the personality of your interface and embodying that in your responses to users will go a long way toward creating a positive experience.

Simplify data entry whenever possible.

The magic of conversational interfaces is not about the input users make into a system. It’s about the response being relevant, fast, personalized and humanizing.

This means that while you do want users to interact with a simple text input for many interactions, there are plenty of opportunities to simplify data entry by suggesting sensible defaults.

Dropping some multiple choice options, confirmation buttons and the like can often be preferable to having to type in “yes”. As a general rule, when you’re trying to guide the conversation or when a user should be selecting from a couple of options, give them tools to make that simple.

This also means taking advantage of tools to eliminate the need for data entry altogether. FB Messenger of example already knows and gives you access to the user’s name, so you don’t have to ask. There are other features that are sure to be coming like access to a user’s location (you can prompt the user to supply location using the location button on the Messenger keyboard, and a small number of companies like Uber already have direct access to location data). Those features should make many data entry tasks unnecessary, allowing you to deliver value to the user faster.

I said it’s like a phone tree, not “literally create a phone tree.”

You can take the principle of limiting data entry overboard though, and actually create a worse experience for users. Asking users a bunch of multiple choice questions to help them drill down is not nearly as efficient as letting them tell you what they’re looking for, and one of the benefits of the conversational platforms is explicitly supposed to be to allow those kinds of interactions.

If you need 6 pieces of data from a user to begin showing results, you can still make it easier for them by asking them to search first and supply intelligent follow up questions based on their input. For example, rather than forcing them to tap:

Women’s clothes > dresses > work > red > size 4 > under $200

You could instead have them search for “red work dress”, guess they’re looking for women’s clothing, and only ask 2 follow up questions.

This approach wouldn’t be limited to the initial search – it could also be leveraging for filtering results. Rather than having to tap multiple times to filter or modify, allow a user to simply say “show me the first one in blue” and have it respond appropriately.

Conversational interfaces lend themselves perfectly to curated search. Rather than trying to drill users down through 10,000 SKUs, you can use a subset of your best selling or most interesting products, grouped by theme or user type.

Think about ways to make searching easier.

Just because a user is interacting with a text input doesn’t mean you have to be limited to text.

Allowing a user to drop a photo in for example, can be a powerful way for them to tell your system exactly what they’re looking for. Google and others have open source libraries for handling these kind of processing tasks already.

You could also bake in small delighters like allowing users to communicate with emoji. Even though the meaning of emoji often can only be understood in the context of the people having the conversation, there are likely global instances where it’s meaning could be clear to a bot.

Develop a history with me over time.

While remembering user preferences and defaults is important for any application, it takes on additional importance inside of a conversational interface. Because we are trying to approximate what it’s like to communicate with a live person as much as possible, having the interface “remember” what was previously discussed is essential.

A user should never have to tell a conversational interface something more than once. Even if they are interacting with different departments, a user’s history of products purchased, previous support issues, etc. should be shared and baked into future interactions.

Doing so allows for some fun opportunities. It’s possible to imagine a return or exchange being as simple as a single request from a user, with the interface understanding the intent and handling all the details for them without the need for followup or transferring to different departments.

Over time, such a “relationship” becomes more valuable for the user. If a retailer knows I ordered a small shirt, it can start with that assumption (clarifying of course).

If it knows I returned a small for a medium, it could either update my preferences to be a medium, or it could remember that I’m usually a small, but when products run small proactively suggest the medium instead.

If a system knows I bought a present for my mom last year roughly around this time, it could proactively remind me to get mom something again this year and suggest things similar to what I ordered last time.

Use notifications intelligently and prudently.

As we mentioned earlier, the big messaging platforms have learned the lessons of our past. They’ve seen how email gets abused. Facebook saw firsthand what brands will do if you let them with the first incarnation of their platform.

As such, they’ve deliberately made their platforms customer-initiated. And they’ve made it extremely easy to block a conversation whenever a user gets annoyed.

As such, it’s imperative that notifications be used with extreme moderation. Even if you’re a reputable brand, don’t assume people will stick around if the conversation stops being relevant – many users stopped using CNN and other early bots because the daily cadence of communication was not in alignment with user expectations.

Again, while stopping push notifications takes a couple of steps on most devices, blocking a bot takes a single tap. Don’t just transfer your push notification strategy over to your conversational interface strategy.

When is the Right Time to Start Building Your Conversational Interface?

A lot of the early excitement with conversational interfaces was immediately followed by usr being nonplussed with the current implementations. This is not unusual. When the app store first opened up on the iPhone, many of the early apps were very crude implementations of what was to come.

But this isn’t a bad thing. The people jumping in and trying early iterations of tools are much more forgiving. Your version might suck, but so does everyone else’s.

There are several reasons why we think being early in the game is beneficial.

It’s a Great PR Opportunity.

Being early maximizes the likelihood the the media will care. In two years nobody is going to cover a story about how your brand is launching a conversational interface or bot (unless it’s implementation is incredibly novel). But right now they’re eating it up.

You Can Capitalize on an Unsaturated Channel.

The first banner ad was on HotWired in 1994, and had a clickthrough rate of 78%. The average FB CTR as of 2015 was .171%. That a 450x difference.

With any new marketing channel, new ad unit, or new platform, performance initially is great. There’s a novelty to it, and users are open to trying new things. But as a channel gets saturated, its effectiveness wanes, user demand begins to calcify.

Just as users now download zero new apps on average, a time is coming when their willingness to adopt new conversational tools will decrease dramatically.

You Figure Out What Works Faster.

One of the best concepts that emerged from the Lean Startup movement is the concept of the build/measure/learn loop. Companies who adopt a cadence of experimentation and are able to navigate their way through that loop quickly develop big competitive advantages over time.

The benefit of being early is less about first mover advantage and much more about your ability to figure out what works and what doesn’t faster than your peers.

More data = smarter interfaces

While the systems you’ll be leveraging are retrieval-based today, there will come a time (most likely sooner than we think) where truly generative models become viable.

The effectiveness of a learner is directly correlated to the amount of data you can provide it. The more data you have, the more intelligent your interface can become over time.

Even in the context of a retrieval model data is your friend. Since you can tweak and improve your interface on a daily basis if you want, every conversation is an opportunity to improve. Every conversation teaches you what users want be able to do with your interface, what it’s limitations are and what opportunities might exist to add value. It makes sense to start figuring that stuff out now.

Build Version One Now. Learn What Works. Repeat.

For those reasons, we see no reason to wait. Open platforms with multiple billions of users, with tons of demand for great applications and limited supply simply don’t appear often. Whenever one does, it is wise to jump on it immediately and wrap your heads around it.

Don’t wait. Put a stake in the ground. Get something up, even if it’s not as good as it ultimately could be.

Learn from your users – adopt a rapid cadence of experimentation and improvement.

We’d Love to Help.

If you think you’d like to dip your toes into the space and start trying some things, we’d love to help you make that happen.

DI has a full service team that can help you execute from start to finish. We can help craft the narrative. We can design and build conversational interfaces inside of native app experiences. We can build your bot “brain” and connect it to the various messaging platforms. We can handle the integration work with your backend platforms. And we love to work inside of an iterative process to maximize the likelihood of success.

To learn more about how DI can help with your digital innovation needs, contact Sean Johnson at 312-213-0498 or sean@digintent.com

When pursuing innovation initiatives, one of the most important considerations is the makeup of your “innovation portfolio.” Just as an improperly balanced portfolio can increase risk and minimize gains in investing, so too can the wrong makeup of innovation initiatives create unnecessary risk or reduce return on investment.

But what is the best approach when considering the makeup of an organization’s innovation portfolio? There are several approaches, but the framework we’ve found most useful is the Three Horizon’s of Innovation, pioneered by Steve Coley, Mehrdad Baghai and David White.

The idea is that to effectively innovate, and achieve consistent organizational growth, an organization needs to simultaneously think and invest at three levels.

Defend the Base

The first horizon is where mature businesses live. These are businesses that already have a proven, repeatable business model and have achieved scale. The objective at this horizon is to protect and defend the business line, improve profitability – basically execute on what’s already working.

This is the domain of incremental innovation. Incremental innovations aren’t seeking to transform a business, but rather to optimize its profitability, efficiency, or uncover new markets.

Incremental innovation can happen entirely internally, and usually represents the least risky type of innovation. As such internal team members at larger organizations are very comfortable at this horizon of focus.

Double Down on What’s Working

The second horizon is “rapidly growing businesses” – businesses that have identified a repeatable, proven business model but haven’t necessarily reached scale yet. These businesses usually have plenty of opportunity available to them, and their job is to double down on what’s working. Horizon two is where organizations will capture the most value and create the business of the next 10 years.

Internal team members and processes might have difficulty here, as a rapidly scaling organization is subject to different dynamics from a mature or stable organization. However, rapidly growing businesses likely have some alignment in terms of financial reporting, etc. They also represent much less risk for team members, since the model has effectively been proven, and plenty of people will want to ride on a rocket ship.

This stage of business is usually where the buy decision is made in terms of strategic acquisitions, as an external startup has removed considerable risk but not yet fully captured the value of the underlying business.

Make Lots of Little Bets on the Future

The third horizon is “emerging businesses”. This is where the brand new stuff lives, and this is what most people talk about when they talk about innovation.

It’s critical to understand that the rules of the game at this horizon are dramatically different. At this horizon you are trying to uncover new problems worth solving, and placing small bets (ideally several at a time) on potential solutions to those problems.

This horizon is where the least amount of alignment with the existing company lies. These types of opportunities usually need to be staffed with different team members who possess different competencies, and are measured and rewarded differently. They should be subject to “innovation accounting” which prizes the rate of learning above all else. They represent the most potential risk for most team members, since many of them end up failing and failure in big co is usually a bad thing.

To successfully execute at this horizon, the organization needs a very solid understanding at all levels of decision making about the goals of these innovations (identify problems worth solving and viable solutions, not a business generating tons of cash at scale.) This horizon is usually where external consultants can add the most value, as they can more effectively deal with the ambiguity and thrashing that can occur for potential businesses at this stage.

All Three Horizons Matter

Smart companies looking to innovate effectively need to consider all three horizons and devote appropriate resources to each. Incremental innovation work on horizon one should represent the lion’s share of the innovation output in an organization, but does not usually require the most resources.

Effective training of internal team members on how to identify and validate incremental innovations, coupled with some solid processes (and incentives) for moving those innovations through the organization are usually sufficient. A company looking to execute here should seriously consider making incremental innovation an essential part of goal setting and compensation processes.

Horizons two and three must have sufficient resources to be viable. If an organization already has successful innovations in horizon two, they should aggressively try to capture as much value as quickly as possible. Unfortunately, many organizations simply don’t have a business or innovation that accurately fit this description, and must look outside their walls.

Considerable effort should be spent on going outside the building and looking for disruptive businesses that have already found product/market fit, partnering or acquiring them and working hard to give the necessary access to capital, distribution and other resources that make big co desirable without bogging them down in the red tape of the larger organizational culture.

The third horizon of innovation should also receive considerable investment, building up its own portfolio within the larger portfolio. Rather than putting all of their eggs behind a single innovation opportunity, they should spread out their risk by identifying 10-20 potential innovations, give them a small amount of resources each, and use strict innovation accounting standards and stage-gate review processes to let the winners rise to the top.

This will require a heavy amount of buy in at the top to let these fledgling portfolio opportunities thrash—it will look to the untrained eye like a lot of failure and wasted money. But as the venture capital world has already learned, predicting which innovations are going to be successful at the outset is extremely difficult. Very often only one of the innovations will be a winner, but it’s victory will be able to provide more than enough return to justify the approach.

As a rule of thumb, we advocate for devoting 50% of resources on incremental horizon one innovation and 25% on each of horizons two and three. But ultimately it depends on the particulars of your business or industry. The primary goal of such a rule is to avoid the common mistake of investing entirely in the first horizon and little to none in the second or third.

It’s worth taking the time the do an audit of existing initiative at each horizon and determine the allocation to each in terms of talent and capital. The right innovation portfolio can ensure the company is protecting and expanding its existing business while also maximizing its likelihood of success years into the future.

I had breakfast last week with a founder working on a mobile app. Unlike many founders who, when asked about their marketing plans, mumble something about TechCrunch and word of mouth, this guy had done a great job laying the ground work. He had focused like a laser on figuring out Facebook ads, and was driving installs for less than $1 per.

His retention numbers weren’t great. He had no referral loop. He was deferring monetization. But based on the great results he was getting with acquisition, he was inferring some level of product-market fit and was planning to go raise.
Cheap acquisition can be intoxicating. After testing dozens of targeting options and hundreds of creative approaches, it’s not unusual to stumble upon a combination that drives clicks, users, or downloads inexpensively.

Cheap acquisition can be amazing if you have a freemium product or engage in ecommerce. But if you’re deferring monetization or have an app with low ARPU, the rules are drastically different.

Success requires more than users or downloads. It requires engaged users, over an extended period of time, who tell all their friends, and who eventually engage in some sort of revenue generating activity. Even if a startup is operating on an ad model, they need enough impressions to have sufficient ad inventory, which still means users who stick around.

But solving retention and referral are hard. Much harder than solving acquisition.

You have to identify bottlenecks in your analytics that can sometimes be hard to spot. You have to ask your customers why they did or didn’t engage in certain behavior, which can be difficult to get concrete answers out of. And you have to test approaches to addressing those issues, which take considerably longer than simply spinning up some new creative.

And so the temptation is always there. Why waste the time fixing a leaky funnel when you can get installs for less than a dollar? You know your retention curve is terrible, but you figure you’ll make up for it in volume.

A few reasons why that logic is unwise.

Scaling Channels is Hard

When you find a winning approach in a channel, you can ride it for a while. There’s usually plenty of low hanging fruit, and you can acquire users inexpensively for some time — sometimes weeks, sometimes months.

But it always ends. Eventually users become blind to your approach, your acquisition costs balloon up, your growth curve stalls out.

This is less of an issue with paid search, since it is intent-based. You’re not peppering them with ads until they’re looking for you. But (partially for that reason) paid search is generally a more expensive channel. For display and paid channels like Facebook, banner blindness is legitimate.

Channels Change or Become Saturated

Users don’t just become blind to your creative. They become blind to the ad format in general. It’s why you can buy thousands of banner ads for pennies — because nobody clicks on them.

When Facebook app install ads first came out, it was trivial to acquire users for $0.50. The almost frictionless download experience, coupled with the novelty of the format, meant users embraced the ad format as a way to discover new apps.

But as more competitors started advertising, and as users become more used to the ad units, they started to ignore them. It’s much less common (from what I’ve seen) for people to consistently drive sub $1 installs from FB — a trend that’s unlikely to change.

To combat blindness, companies will often experiment with changes to ad formats. Some are beneficial, some aren’t. But all of them can mess with the effectiveness of your campaign. If you have a great single image creative, but the format moves in favor of carousel, multiple image units, your campaign has to change or be abandoned. This pace of change continues to accelerate, which makes milking a great campaign harder to do.

You’re not Building a Sustainable Asset

Having a great acquisition playbook can certainly be valuable. If the relationship between acquisition cost and lifetime value makes sense, you can and should pump as much money into paid acquisition as you can. But smart acquirers look for more than cheap acquisition strategies that dump everyone out on the other end.

Products that can supplement paid acquisition with a repeatable, self priming approach to acquisition have a greater chance of success long term. This usually means some combination of organic search (a valuable tactic for user generated content sites and marketplace businesses) and virality (meaning the product is compelling enough for people to tell their friends).

Focusing on strategies to improve each of those areas, along with aggressively working to minimize customer churn will usually be more important for your long term success than getting a bunch of cheap users in the door.

Cracking the code on cheap acquisition is fantastic. But it’s not enough.

Entrepreneurs regularly overlook the role friction plays when getting users to change behavior. An idea can often make sense in the abstract, but when looked at through the lens of friction can quickly become untenable.

Friction is anything in your product that represents a barrier to adoption or growth. It can be micro, like a piece of interface copy that doesn’t make sense or a page where the CTA isn’t obvious. It can also be macro, like a marketplace business that lacks a clear plan for overcoming the chicken-and-egg problem.

At every step of the product and business development process, DI tries to identify friction and address it. We’ve learned that it takes way less friction than most entrepreneurs think before people bail.

A framework for identifying friction

DI is not above using other people’s frameworks – we try to leverage the thinking of smart people whenever we can. And one of the best frameworks we’ve leveraged for thinking about friction is the Fogg Behavior Model.

BJ Fogg runs the Stanford Persuasion Lab and studies how to influence human behavior using technology. His simplified Fogg Behavior Model is one we reference often when talking with clients about how to compel users to change behavior.

On one axis you have a user’s motivation – how motivated are they to change their behavior? On the other, you have their ability – how easy or difficult is it to change their behavior? Finally you have a trigger – given sufficient motiviation and sufficient ability, all you have to do is place the appropriate trigger at the appropriate time, and the user will likely take action.

Sounds simple, but it’s actually pretty profound. Teams constantly over-estimate how much motivation their users have to adopt a new behavior, and usually simtaneously under-estimate how difficult it is for them to take action using the solution the team has created.

Identifying Motivation Friction

One form of friction comes from the user. Something about your offering is not sufficiently compelling to them – perhaps they don’t believe you can pull it off, or perhaps pulling it off, even if it works, wouldn’t represent a big enough gain in their life to warrant investing the time.

There are two disciplines involved in reducing motivation friction. The first is leveraging a customer develpment framework (we like to use the Jobs To Be Done framework) to better understand user needs. Often the team has failed to get out of the building and talk to customers to truly determine if the product solves a meaningful need. If they do get out, they try too hard to sell the idea rather than getting honest feedback. Very often teams will solve problems that customers don’t actually care about (or care about enough to pay for).

The other discipline is branding. Taking what you learn and encapsulating it in a brand promise, backed up by appropriate imagery and compelling copy, can take an underlying user need and create the necessary desire to actually take action to fulfill the need. Knowing how to take that brand promise and communicate it in a persuasive way to increase desire and drive action is essential.

Identifying Ability Friction

On the other side, ability friction is primarily about product functionality (what it does) and usability (how it does it).

If a user is motivated to take action but your product doesn’t provide the utility to facilitate that action, you’re in trouble. If you’ve done the requisite customer development, the risk of this is admittedly low.

Far more common is a poorly implemented solution. People are busy, and their exposure to your solution rarely is in a vacuum of uninterrupted attention. They’re looking at your product in one browser tab while 20 others stay open. They keep getting interrupted by colleagues at work and kids at home, and their phone buzzes with notifications every 10 minutes.

If your product isn’t painless to use and gets users to the “aha moment” when they internalize its benefits almost immediately, you’re going to have difficulty.

Usability testing can be a great tool in addressing issues with ability friction. If you have a huge bottleneck in a funnel your analytics can tell you the bottleneck exists but can’t tell you why. Bringing 5 people in and watching them use your site can uncover dozens of opportunities for reducing friction – opportunities your team is too close to see otherwise.

Heuristic analysis can help as well. Heuristics give you a series of “lenses” to look at your funnel through. We’re a fan of WiderFunnel’s LIFT model, which analyzes pages or flows through the following lenses:

• Value Proposition – What is the promise you’re making?
• Clarity – Is the value proposition being communicated clearly?
• Relevance – Does the value proposition speak to the needs of the user?
• Urgency – Are you leveraging internal urgency and/or creating external urgency to incite action?
• Anxiety – Is there anything on the site that is giving users pause or forcing them to reconsider taking action?
• Distraction – Are you inadvertently leading users away from the primary actions you’d like them to take?

Going through each page and looking at it through each lens can uncover many opportunities for improvement. Coupled with user tests, you can usually find plenty of ways to increase your user’s ability.

Common Friction Pitfalls

Over the years, some common pitfalls have emerged on a regular basis. These antipatterns are prevalent on the web, but usually represent friction for the user and barriers to adoption. If your product or service is using any of these patterns, strongly consider removing them.

Hard sell, with no plan B

Many sites don’t invest enough energy in considering the buyer lifecycle. They assume a user will have sufficient motivation to take action based on very little information (a single landing page, for example), and offer no secondary call to action.

Particularly for SaaS businesses or ones with high price points, creating content that more accurately maps to the buyer journey is wise. That usually means creating informational content to drive awareness, having secondary CTAs (white papers, etc.) to capture leads, and nurturing them through the consideration and buying process.

Most of your visitors won’t be sufficiently motivated to sign up yet. Make sure you have a mechanism for nurturing them.

Ghost towns

For marketplace businesses, the chicken and egg problem is very real. If you don’t have sufficient supply on both sides of the business (employers and candidates for a job site, both sides of a dating site, etc.) it can be extremely difficult to succeed. New users come in, see there are no jobs or apartments or whatever, and leave.

The answer in the very beginning is usually to “cheat” – to seed one side of the market with enough content to attract and retain the other side. There are a variety of ways to do this:

• Paypal bought products on eBay, and asked to pay via Paypal
• Grubhub loaded menus and sent orders for free to restaurants for months before converting them to paid customers.
• Reddit seeded the site with their own content, making sure there was always a steady stream of new interesting content on the site.
• Rentoid put products on the site for rent, and when someone rented one went to the store, bought it and shipped it themselves.

Homepage carousels.

Carousels are rotating panels on the top of a page, designed to surface multiple products or value propositions. They are usually a compromise between internal teams who all want to tell their story.

People rarely click on the second or third panels, and often ignore the first panel as well (if it looks too much like an advertisement.) Given that scrolling is not the evil it used to be, consider using your prime real estate to hammer your primary value proposition, discussing your secondary points further down the page.

Email confirmation.

When a user has agreed to sign up and take action, the last thing you want to do is force them to leave your site. If your product doesn’t capture sensitive data, consider removing email confirm entirely. Even if it does, you can usually send them the confirm email but still let users begin their onboarding process.

Blank slates.

When a user completes a registration process, never dump them into a blank screen and make them figure out how to use the product. Provide a demo of some kind, ideally an interactive one that assists in the creation of new content and increases the likelihood they get to that aha moment during first use.

Geni used to ask you for your father and mother’s name and get you immediately into building your family tree. Basecamp has a sample project to show you the features of the product that you can archive as soon as you understand the product. Quora has you select a couple topics to build an initial dashboard, and shows you the most interesting content from each (rather than the most recent, which would be a crapshoot.)

Perhaps my favorite example of nailing the onboarding experience is Pandora. Ask a user for an artist, play a song, explain how it’s related to the artist, done. I get what Pandora is in less than 5 minutes.

At DI we spend considerable time and effort iterating on onboarding and first time UX, and it’s money well spent. Whatever you do, avoid the blank slate at all costs.

Start attacking friction

Identifying and removing friction in a systematic way can dramatically improve your product and the underlying metrics you’re trying to move. If you see bottlenecks in conversion, activation, retention or referral, start by asking yourself whether you’ve sufficiently accounted for friction.

Any other patterns you’ve seen that lead to increased friction? I’d love to hear about them – send me a tweet at @intentionally.

I was in a meeting a few weeks ago, and Apple watch came up. Without thinking I blurted out something I never thought I’d hear myself saying.

“I don’t get the Apple watch.”

As the words left my mouth, I immediately realized my error. I apologized to the team, and told them I never want to hear that from anyone in our office.

The error has nothing to do with the Apple Watch specifically. It’s fine if you or I never become an Apple Watch user. But the error is in refusing to explore it.

I’m terrified of phrases like that infecting our company. If phrases like that become the norm, our company would die. If that kind of thinking became pervasive in my life, I’d be finished.

If you find yourself saying things like that regularly, odds are your career isn’t taking off the way it should.

Creativity Comes From Curiosity

Being innovative is largely a function of having a wide antenna — being curious, trying new things, working hard to understand them.

Creativity at it’s core is often nothing more than combining multiple disparate ideas in a new way. It follows that in order to be creative you have to have a lot of inputs.

Dismissing a new technology, refusing to kick the tires of a new product, or choosing not to read about new ideas drastically limits your creative potential, your ability to find those unique connections, your ability to leverage insights from other products or industries to solve a problem you’re currently tackling.

Great Ideas Usually Look Stupid Early On

People are notoriously bad at predicting success. Many of the best startups looked like stupid ideas initially. AirBnb was completely ignored in the beginning. Snapchat was laughed at by non-millenials.

Just because an idea seems stupid to you doesn’t mean it’s stupid to everyone. There’s often a group of people who will find the new idea is the answer to a huge problem of theirs. They become the products early evangelists, helping improve the product, telling everyone they know about it, helping increasingly larger groups of people to become acclimated to the new idea.

Ideas that look silly but are given the opportunity to germinate often become transformative, and sometimes even feel obvious in hindsight. Be careful about writing them off too quickly.

Huge Competitive Advantage Comes From Early Distribution Opportunities

The other argument for trying things relates to distribution. There are huge opportunities to leverage platforms and products if you can figure them out before everyone else.

Andrew Chen talks about the “Law of Shitty Clickthroughs”. A new channel or platform emerges, some smart curious people deconstruct it and figure out how to leverage it to their advantage, and reap tremendous results. Other companies notice, jump in, take advantage, an saturate the platform. Over time, the effectiveness of the platform decreases.

If you aren’t curious and willing to invest some time trying things, the likelihood you’ll identify these opportunities is very low.

Create a Kick The Tires Habit

As a partner of a fund that plays an active role in its investments, it’s even more important for me to have a wide antenna. So I’ve tried to bake it into my life in a more systematic way.

One of the habits I’ve developed is to spend 1 hour a week trying new products. Product Hunt is my primary source for this and it’s organizational structure is perfect for a weekly batched task like this.

Any app that lands at the top of the stack for the day deserves a look, regardless of how silly it seems or how much you understand it. There’s plenty you can learn by doing this:

• Do they communicate their value proposition in a unique way?
• Are they doing anything clever with your marketing? With landing pages? With their social strategy?
• Any clever approaches to onboarding? How effective are they at getting you to the aha moment?
• Do they deliver their core experience in a unique way?
• Do they do anything interesting over the weeks following to try to keep you engaged?
• Do they have novel approaches to driving referral?
• If they have a revenue model, how do they try to monetize?

This hour a week has proven invaluable as we look for ways to help our fund companies get better at what they do.

Don’t Make Statements — Ask Questions

The other thing I try to do is replace statements with questions. Whenever you catch yourself making a statement, particularly one pronouncing judgment or an opinion of a product, try to reframe it as a question instead.

• Rather than saying “I’d never use this”, ask yourself “Who would use this?”
• Instead of saying “There’s no way this gets as many users as X”, ask yourself “How many people would love this?”
• Instead of saying “This will never work”, ask yourself “How could they make this work?”
• Instead of saying “This seems stupid”, ask yourself “Is there an opportunity here I’m not thinking of?”

It doesn’t matter what you think

It’s natural to use your own life as the litmus test for determining what you think of a product. But you have a limited point of view, and without stepping outside of yourself you short circuit your opportunities for learning.

Try hard to cultivate a bigger antenna. Try new things. Explore new ideas. Dig into them and really kick the tires. And ignore the question of whether you’d use the product yourself, instead asking why other people would.

Software is not a static thing, or at least it shouldn’t be. The beauty of web and mobile applications is you can rapidly iterate and improve them to increase user satisfaction and accomplish business goals.

Sadly most companies don’t take advantage of this. The first version is the only version. The opportunities for gathering robust, actionable user data aren’t leveraged. The product doesn’t accomplish its intended goals and often the project is abandoned.

By embracing an iterative, data-oriented process the odds of success with software projects increases dramatically. The following explains how Digital Intent leverages these ideas once a product goes live.

Walk Before You Run – Avoiding the Big Launch

While Digital Intent uses customer development prior to launch to uncover user needs and test early iterations of the solution, the first version of a product in production will still have areas for improvement – areas that are frankly impossible to uncover beforehand. These opportunities take several forms:

• Usability issues that prevent users from accomplishing critical tasks.
• Features that sound good on paper but rarely get use due to lack of motivation or lack of awareness.
• Features that either had been tabled or weren’t previously identified, that users think would increase utility.

Because of this, Digital Intent advocates for a series of releases to progressively wider circles rather than doing “the big launch.” Ideally a product gets the breathing room to identify and implement simple improvement opportunities. Similarly any serious barriers to adoption are best identified at a small scale.

As you solve these problems and make product improvements you increase your user population, through users who have signed up for beta access, increased paid acquisition efforts, or increasing the number of internal stakeholders.

Start With Early Evangelists

The staged launch process usually begins with a small launch to your most excited prospective users – people who indicated the highest level of interest during customer development.

While it might seem better to solve problems for the most skeptical, the reality is they’re considerably less likely to use the app for any useful length of time and are less likely to provide feedback that is genuinely useful. This can lead to distraction with product priorities and decreased motivation with the product team.

It is made clear to early evangelists that their job is to use the software on an ongoing basis, providing feedback on what works and what doesn’t, and advising on how to make it better.

DI works with the client during this initial phase to capture qualitative feedback from these users in the form of micro surveys baked into the experience, more formal surveys, and face-to-face usability and customer development interviews.

At the same time, DI leverages quantitative data of actual user behavior. Often what people say and what they actually do are different. Having the right analytics tools with an ability to track actions on a per user basis allows us to see how individuals are actually using the software – not just an average of what the typical user is doing.

Collect The Right Data

It’s imperative that the product team is armed with the right data to make informed product decisions. Avoiding “vanity metrics” like signups, page views, etc is immensely important – these numbers might make the team feel good and sound good to internal stakeholders, but don’t give you actionable information for improving the products.

The right data depends on the type of product, but tends to have the following characteristics:

Not Averaged
Expressing data in averages is dangerous, as people tend to interpret such data as a normal distribution or bell curve. The reality is usually some form of a reverse power-law curve. While many people will use it once and never again, you’ll find some users whose level of adoption is off the charts.

Not averaged: Expressing data in averages is dangerous, as people tend to interpret such data as a normal distribution or bell curve. The reality is usually some form of a reverse power-law curve. While many people will use it once and never again, you’ll find some users whose level of adoption is off the charts.

Real time views allow you to see how specific users are engaging with your product. 
The goal is to identify who those people are, what makes them tick, and what they did or thought differently that caused the “aha moment” where they internalize the benefits of the product and habituate into their life. This information is immensely valuable in architecting the sign up, on boarding and retention experiences to maximize the likelihood more users reach that moment.

Action-Based

As previously discussed, data should be tracked at the individual action level, particularly early on. While knowing aggregate numbers is helpful, being able to drill down and see what Dave is doing on your site is extremely helpful.

Action-based metrics also show you how people are using specific features of your site, rather than simply tracking page views. You want to be able to see what % of people accomplished different tasks within an interface, not simply whether they visited it.

Focused on Retention

Retention is ultimately what matters in determining product adoption. Good product teams make retention reports the north star for driving product choices. Retention means different things to different organizations, but the focus usually revolves around the % of users who continue to use the product one day later, one week later, one month later, etc.

Tracking retention in aggregate is helpful, but tracking by cohort is even more useful. If you’re iterating on your product, the product users experience one month from now will fundamentally be different than the one they use today. As such, retention patterns should be different for new users of the revised product. Breaking user populations out by week, month or release can tell you whether your product enhancements are working.

Organize Product Optimization Sprints

The purpose of capturing this data is of course to identify areas for improvement. While some percentage of development can go to new feature development, it’s rarely the case that adding features is the difference between success and failure.

If the core experience remains flawed, users won’t adopt by bolting on additional stuff. This is particularly true for mobile applications. DI advocates for a 70/30 split, where 70% of the effort goes toward improving the core experience, and 30% goes to new product efforts.

Optimization sprints are usually organized around improving a single critical metric, mostly in the activation or retention phases of a user lifecycle. By organizing the team around a single metric, speed of iteration is increased and ideas for improvement are more rapidly surfaced, connected and implemented.

Optimization Sprints (as opposed to new feature sprints) can be anywhere from one week to four weeks in nature depending on complexity. iOS apps have a mandatory one week review process with the app store. This can be mitigated through mobile A/B testing (although limited), or through the use of web views (which have a performance hit and limited access to the phone’s functionality but benefit from instantly being live for users without being subject to the review process.)

Commit to a cadence of rapid iteration

What happens post-launch is arguably more important than what happens beforehand. By gathering data, and using it to inform your development process, you maximize the likelihood of getting to a place where your product is successful.

Whether you have a product already or you’re looking to build a new one, DI’s digital product development team can help you turn it into something amazing.

Great growth people are extremely hard to come by. The combination of skills necessary to be effective is rarely resident in a team, much less a single person. A true growth person is the new unicorn.

At Founder Equity and Digital Intent we’ve attempted to grow rather than hire. Over the last 18 months we’ve taken several novices and turn them into productive growth team members who can point to meaningful results for their respective companies.

Individuals who a year ago didn’t know what LTV or CAC meant are now acquiring users at scale for less than a dollar, consistently growing communities by 7-10% week over week, improving conversion rates by orders of magnitude and effectively iterating on referral loops.

The framework combines extensive training on a broad range of skills (statistics, UX principles, copywriting, etc), deep dives into distribution strategies, and exercises to give them reps, both inside and outside of our fund companies.

We still have a ton of room for improvement (primarily because of my weaknesses, not theirs). But overall we’ve been thrilled with the result.

But it doesn’t work for everyone.

The right knowledge, skills and tools aren’t enough. In order for them to become effective, there are competencies they have to possess – aspects of their makeup that aren’t specific to a particular tool or job function.

We’ve identified 6 competencies that are highly correlated with success in our growth program. If a candidate has them, the odds are high they become successful in a growth role. If not, their likelihood for success drops considerably.

Below are the six competencies we look for, and what we think an A-player looks like. If you’re looking to identify your own A-players, hopefully this can get you started in what to look for.

Outcomes Focused:

A-players are relentlessly focused on achieving tangible results, not simply being efficient. They don’t shy away from accountability – they relish it. They love having targets and striving to hit them. They tend to think like salespeople – they are hunters, aggressively making efforts to achieve meaningful improvements at each stage of the funnel.

An A-player probably has projects on their resume that they’ve quantified in some way. Revenue generated, sales made, customers signed up, growth rates achieved, etc. If they don’t, ask them to tell you what results they created for their companies, clients, etc. A-players should be able to tell you.

Data Driven:

A-players are outcomes focused, so naturally data is their best friend. They know data is their lifeline to reality, telling them whether their experiments are working or not. They routinely rely on quantitative and qualitative information to make intelligent, informed decisions. Data becomes the key they use gain insight, guide decision-making, and hold themselves accountable for results.

Curiosity:

A-players have insatiable curiosity. They have a wide antennae – they’re always reading books or articles to get better. They look outside of their own industries for new ideas, and treat every conversation as a chance to learn something new. They love leveraging best practices and testing ideas that have worked successfully for others rather than reinventing the wheel every time.

Asking someone about the most interesting book they’re reading right now can be great shorthand. A-players always have 2 or 3 books they’re in the middle of, at least one of which is relevant to their career. True, there are plenty of curious people who don’t read books, and you might miss some. But it’s a virtual certainty that the people who are constantly reading are looking to get better.

Resourcefulness:

This is the uber-competency, as it’s able to make up for many problems. Gaps in knowledge, missing skills and subpar tools can often be overcome by someone who’s willing to take initiative and solve the problem despite the constraints.

A-players are expert Google searchers. If they don’t know how to use a tool, they pore over the documentation to figure it out. If they read something interesting but have a follow-up question, they track down the author’s information and reach out directly. If they can’t accomplish something using their current tools, they figure out a way to create an 80% solution using the resources they do have. They understand that good enough is better than perfect but unimplemented.

Resilience:

Most of your ideas are going to fail, which can be discouraging. Just like successful salespeople, success is largely a function of sticking it out, continuing to stay positive as you run test after test in search of the 10x boost.

A-players know this and mentally prepare for it. They know that a failed experiment still represents learning, bringing them that much closer to a solution that works. They don’t let failure get them down, but evaluate the experiment and try again. And again. And again.

Asking for a story of when they failed can be telling. If someone doesn’t have a story, odds are they’re either lying or aren’t willing to take enough risks. If they do have a story, you want to hear how they bounced back, dusted themselves off and either tried again or moved on to something better.

A Sense of Urgency:

A startup is an exercise in jumping out of a plane and trying to build a parachute before you land. There’s no time to waste – every day must be taken advantage of before you run out of money or are outflanked by a competitor.

A-players get this. Because they know most of their experiments will fail, they constantly try to optimize their build-measure-learn loop to run more experiments than their competitors. They make the extra call, send the extra email.

A-players push the envelope. They aren’t afraid of challenging the status quo. They are willing to ask the question “is this something we don’t do just because that’s the way it’s always been done?” A-players stretch the rest of the team, and often find the CEO reining them in. That’s okay – if the CEO has to be the one pushing the team to be more aggressive there’s a problem.

They have the competencies – now what?

If you find someone with these 6 competencies, you’re in luck. You have the raw material you need to create a unicorn.

What do you do now? We’ve made the outline of our growth training program available to you at the link below. It outlines the 15 skills we focus on, what we expect out of team members, how long it should take to create a unicorn, and a ton of resources you can leverage to get them there. Hopefully you find it valuable.

If you have any questions about the above, or if you think you have these competencies and would like to talk about what working at DI is like, I’d love to talk.

Startups don’t fail because they can’t get the technology built. They fail because they can’t get customers.

DI spends a lot of time helping clients actually get traction for their ideas. And after 4 years of trial and error, I’ve managed to cobble together what we’ve learned into a 2 hour course on Udemy called The Ultimate Guide to Funnel Optimization.

At the conclusion of this course, you’ll have a solid understanding of the techniques and tools leveraged by top startups, as well as over 150 specific ideas you can immediately use to get more customers and keep them coming back.

Knowing how to systematically and reliably drive user growth is a tremendous source of competitive advantage. Whether you’re a founder looking to jump start your growth efforts, or an employee looking to acquire these skills, this course will give you a huge increase in effectiveness.

During this course, we’ll discuss:

• What the customer funnel is, and why it’s important.
• How to organize yourself and your team to prepare to grow.
• Strategies and tactics for getting your first 1000 customers.
• Over 75 ideas for acquiring new customers.
• How to create compelling content targeted to your niche.
• What really matters when optimizing your site for search engines.
• How to avoid wasting time and efficiently market on social media.
• How to do public relations without hiring an agency.
• When to use paid acquisition (and more importantly, when not to.)
• How to get prospective customers to take action once you’ve driven them to your site.
• The most important factors that drive landing page conversion.
• How to leverage email to nurture leads and convert them over time.
• How to design your onboarding process to ensure a pleasant first time experience.
• The most important tool in your toolkit for retaining users.
• How to build effective referral loops into your product.

You can grab the course for $129 here. I’d love to hear your thoughts.