Traversing the Traction Gap

by Bruce Cleveland

  To become a market-first startup, you need to develop Market IQ.

  How? You develop Market IQ by incorporating the principles of market-first processes and promulgating the adoption of a market-first mindset across the company. Startups with a strong Market IQ understand that they need to find a market/product fit before they begin to move forward with a new product design or upgrade.

  Like all good measures of intelligence, Market IQ looks at multiple factors; in this case:

  what the market is concerned about,

  what causes those concerns, and, if possible,

  what potential solutions or products will ameliorate those concerns.

  In life, being intelligent doesn’t mean that every one of your ideas is correct. Nevertheless, the reason you want brilliant people working at your startup is that adopting a more intelligent approach to any problem increases the likelihood that you will succeed.

  Market IQ is no different for a company. Being more intelligent in how you approach the product process makes it more likely that you will succeed in developing market/product fit for a new product design and upgrade.

  “Don’t find customers for your products; find products for your customers.”

  SETH GODIN, author; Founder & CEO, Do You Zoom

  Many people find Seth Godin’s statement obvious. They agree that you should always prioritize the market before focusing on product innovations. As we all know, however, agreeing to something in principle is easier than turning that principle into action.

  In reality, very few companies use a market-first approach to product development. So what are they doing instead?

  Startups, and even existing companies, tend to focus on products that they think represent solutions to big problems, and then find minimal selective data to support the investment.

  Startups say they want to get market data, but it has always been so hard and expensive to get it quickly and accurately that they fall into the trap of finding selective data to support their preexisting perspectives. It is what statisticians call confirmation bias. And that is usually a very dangerous thing. However bad it is to realize your ignorance, it is infinitely worse to be confident in your inaccuracy. Bad questions inevitably lead to bad answers . . . and then to bad decisions.

  Existing companies with customers are typically obsessed with focusing on current customers to the exclusion of the bigger picture—noncustomers—who often better represent the true overall market. These companies often also overemphasize certain parts of the market data when it is consistent with their world view and ignore or reject any data that challenges that image.

  In short, startups—and companies in general—say they care about market input, but in practice reflect a method of decision making that prioritizes intuition and unrepresentative anecdotes about what their existing customers think.

  The result? While everyone claims to care about the market, in reality most are simply “going through the motions” because they have already made up their minds regarding what they are going to do. This mindset and behavior are rampant among technology startups, ironically the very groups that should recognize what little they know about the marketplace.

  Established companies often suffer from this disease as well. In their case it’s usually disguised as “arrogance,” and, if not attended to and addressed, it often can be a terminal disease.

  My contention is simple: if everyone agrees that we should find products for a specific market or markets, then our practices should align with that and we should adhere to the three principles of being market-first.

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  OPINIONS VS. DATA

  The problems that are outlined above don’t stem from ill intentions or bad faith. All companies—startups or otherwise—want to do the right thing. But in the end, they almost always choose to implement the same set of practices over and over again because of time pressures, inertia and habit, and a lack of knowing a better way.

  How do you prevent succumbing to the same deadly mistake?

  What you need is a real framework, not just some nebulous concepts, along with real tools that make it easy to do the right thing and perform real market research. Only then can your startup focus on collecting and interpreting market signals rather than acting on intuition alone.

  In short, your company must develop and work to cultivate its Market IQ.

  By the way, intuition is inevitable. You can’t escape it, even if you try. And just to be clear, you don’t want to. You need it! Without intuition, you would have no starting point for collecting, interpreting, or acting on data. More importantly, you simply cannot collect data on everything. Intuition is critical to providing a place to start and focus as you learn more.

  But you can’t give intuition more respect than it is due. You can’t be its slave. The problem is that for most startups, the big decisions related to product don’t just use intuition to guide a data-driven process—intuition is the process. Worse yet, the team masquerades as being data-driven by finding only the selective data that support its preexisting plans. Now the company isn’t just lying to the world, it is also lying to itself.

  By comparison, a market-first product process harnesses the power of intuition to guide and discipline a subsequent intense data-driven process that can mean life or death for a new product.

  Here’s an example of a big company that blew it. Apple.

  Apple Newton

  FIGURE 17

  The Apple Newton is a classic case of the distinction between intelligence and Market IQ. After six years and $100 million in development, the Apple Newton was released in the early 1990s with a level of functionality that was mind-blowing for the era. The technology itself was impressive. I was at Apple at the time, running one of the company’s engineering divisions, building object technology infrastructure, and I can assure you that some of Apple’s best people were involved in this project. And why not? It was going to be the Next Big Thing.

  Newton’s new features included everything from constant Internet connectivity to touch screens to handwriting recognition. The product development team had truly created a disruptive and innovative technology.

  The problem was that the intuition and brilliance that led the company to develop Newton was not combined with a Market IQ approach that might have revealed the limits of the market to support that innovation.

  I’m not claiming that a market-first process would have saved the Apple Newton. Instead, I am suggesting that being market-first might have saved Apple the money that it invested in the Newton. Instead, prioritizing innovation and disruption over market assessment proved to be devastating. As Mat Honan noted in Wired magazine, “The Newton wasn’t just killed, it was violently murdered, dragged into a closet by its hair and kicked to death in its youth by one of technology’s great men.”4

  We now know that the intuition and intelligence that led to the advanced innovations found in the Apple Newton were in fact correct. Just look at your smartphone or tablet, both direct descendants of Newton, and both gigantic global markets. After his graphic description of the Newton’s demise, Honan himself concedes: “And yet it was a remarkable device, one whose influence is still with us today. The tablet. The first computer designed to free us utterly from the desktop.”

  This comment raises an interesting question. Could a market-first process have saved Apple from making the investment in the Newton at that particular moment?

  A market-first process would have required the intuition associated with the product to first be assessed based on the data. What types of data? Imagine if the Apple Newton had been the result of a series of Discovery Interviews where consumers had complained about being chained to their desktops. That would have set in motion the product development team with some great data that validated the intuition that this was a truly disruptive technology designed to help people overcome a basic problem. But that didn’t happen. And I suspect that if this research had occ
urred, it would have found that the personal computer was still so young that this requisite frustration with desktops had not yet emerged.

  That said, Discovery Interviews alone are not enough to satisfy the market-first process. Now imagine that the Apple Newton was also obsessed with usability testing to determine the basic functionality of the device in the various environments, such as coffee shops and conference rooms. Apple likely would have quickly discovered that the dearth of Internet availability in those locations at the time challenged their assumption of constant Internet connectivity.

  Additional usability testing also would have revealed that their vaunted handwriting-recognition software was severely compromised—so much so, in fact, that cartoonist Garry Trudeau dedicated a week of Doonesbury to mocking its poor functionality. In Honan’s view, “Handwriting recognition was supposed to be Newton’s killer feature, and yet it was the feature that probably ultimately killed the product.”

  In the Hacks section of this chapter, I’ll show you how to execute four collection processes to help you determine market signal: Discovery interviews, large-scale surveys, smoke tests, and A/B tests. I’m only covering this partial set—and even then, I’m just providing a cursory overview—because covering every market signal collection process in full detail would require an entire book dedicated to the topic. In fact, learning how to collect statistically valid market signals should be an integral part of a much longer, more formal product management course.

  I hope you can see that a market-first process might have helped the product development team rely on more than the intuition that told them they were doing something truly innovative and disruptive. At a minimum, early usability testing in a Lean startup approach would have revealed that the market signals simply did not support the innovation.

  The market-first process and mindset also would have revealed that, while there may have been a basic interest in moving beyond the confines of the desktop, releasing the Apple Newton into the world before it—and the world—had both full functionality and an adequate technology architecture would mean that the $100 million technology might literally become a laughing matter.

  “You’ve got to start with the customer experience and work back toward the technology—not the other way around.”

  STEVE JOBS, Cofounder & CEO, Apple Inc.

  So, why don’t companies rely more on data for their product process? It is not because they don’t want data. It is often because they don’t want to wait on the data. They assume that it is too hard to get that data, especially from noncustomers, in a timely fashion. So, instead, they “punt” and go with their gut.

  Steve Capps was in charge of the Apple Newton’s user interface and software development teams. In an interview with Honan, Capps admitted “We barely got it functioning by ’93 when we started shipping it.” His assessment on the failure of the Apple Newton was simple: “We were just way ahead of the technology.”

  The pressure to meet an external deadline is real. Everyone reading this book knows that. That’s why a market-first approach is designed to install a process that enables you to harness the power of your intuition while simultaneously reducing the likelihood that a product fails because there is not enough data to support an intelligent decision. The most basic question that all products must face to be successful must always be: Is there a market for it right now?

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  CUSTOMER VS. MARKET

  It sounds great to say that your company is customer-driven. After all, who can object to focusing on your customers? But, like everything in business, the question is: At what expense? If you are customer-driven at the expense of the market, then you can create huge issues for yourself in the long term.

  A strong Market IQ that emanates from a market-driven mindset doesn’t ignore customers. You can never stop listening to your customers. The key is to balance that feedback against other market participants, especially noncustomers.

  “If I had asked people what they wanted, they would have said faster horses.”

  HENRY FORD, Founder, Ford Motor Company

  There are two risks associated with being too customer-driven.

  Feature Bloat—As Clayton Christensen outlines in The Innovator’s Dilemma, customers often provide feedback for incremental product improvement that can lead to feature bloat over time.

  Market Participation—Focusing too much on customers means that you miss out on all the other market participants. While influencers and analysts can also be important constituents, the big-ticket groups are the noncustomers who may have never engaged with you or even visited your website.

  Why pay so much attention to noncustomers? If you are wildly successful and have 20 percent of the market share, then that means you have four times as many noncustomers as customers. In truth, for most companies the ratio of noncustomers to customers is more like 10 to 30x.

  In the long term, your noncustomers are a main source for growth. According to Clayton Christensen, they also help you generate ideas on breakthrough (instead of incremental) products and features.

  This idea leads us to ask a very important set of questions:

  How well do you know your noncustomers? (As an early-stage startup, that might be your entire market.)

  If you’re an existing company with existing products: Why have they not heard of you?

  If they have heard of you, why have they not visited your website?

  If they did visit your website, why did they not engage?

  If they did engage, why are they not converting?

  Understanding the perspectives of your noncustomers—really, your potential customers—is an essential component of Market IQ, the market-first product process and mindset.

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  SEEING THE TREES AND THE FOREST

  A critical component of developing your Market IQ is to be data-driven—in practice. Easy to say, but what does that really mean? Some of you may think you are data-driven. If you are part of a large incumbent company, you may have millions or tens of millions of users generating petabytes of data.

  That sounds great. But, from the perspective of the market-first product process, it is not enough merely to have access to vast amounts of data. It is necessary, but not sufficient. The real question is: Do you have the right types of data? A market-first approach is designed to enhance the qualification funnel, which is essential for market fit.

  Zynga knows this firsthand. Zynga is a master at A/B testing and clickstream analysis. The company has petabytes of data and can drill down to get a very precise understanding of what is happening.

  What Zynga can’t do well, however, is know why something is happening.

  According to a former Zynga product executive, the data was so rich and so deep that the company was staring only into the bottom of the funnel. “It was like looking at your feet rather than looking at the horizon,” this executive recalls. “From the perspective of Big Data, we weren’t just focusing on the trees at the expense of the forest . . . we were analyzing the atoms that made up the bark of the trees.”

  Being truly data-driven still means acknowledging the need for the vast array of data that is now available to all of us, but also having a direction before diving in. This is a key distinction to avoid lapsing into an ad hoc product process where decisions are made based on an intense review of customer feedback without a vision for the overall market or the future of the product or its features. I will compare and contrast this ad hoc product process and the market-first product process later in this chapter.

  For now, it is important to understand that this mistake is the embodiment of the concept of shifting bottlenecks. You can use the data to fix problems with execution by looking deep into the funnel, but at some point you get diminishing returns. At that key moment, you need to be able to look up, get a view of the horizon, and seek a new direction. Once you have identified this new direction, you can dig right back into the data to make the
execution possible.

  The key, then, to being data-driven is having the flexibility to shift your focus from the forest to the trees and back again as the situation requires. In doing so, you will use market and customer signals to guide your product development, rather than simply focusing on how much functionality you can achieve at the microscopic level.

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  PROBLEM SPACE VS. SOLUTION SPACE

  “As a product manager at Intuit, I learned to write detailed product requirements that stayed in the problem space without getting into the solution space. We were trained to first focus on ‘what’ the product needed to accomplish for customers before getting into ‘how’ the product would accomplish it.”

  DAN OLSEN, Consultant, Olsen Solutions LLC; author, The Lean Product Playbook

  What does Dan Olsen mean when he refers to a “problem space” versus a “solution space”?

  A simple example can help illustrate both concepts.

  Let’s say you wake up one morning with a pain in your abdomen that is accompanied by nausea and fever. If you simply wanted fixes for each of these issues, you could reach for Pepto-Bismol for the stomach pain and the nausea, and ibuprofen for the fever. That approach, where you focus on treating the symptoms, is the solution space.

  The problem space prompts you to ask why the symptoms are occurring in the first place. Could your symptoms represent the stomach flu, food poisoning, or something much worse—such as appendicitis—that might require an emergency response?

  According to Olsen, you will eventually need to get to the solution space, but first you need to deal with the problem space. Not to do that would be like trying to cure appendicitis with Pepto-Bismol and ibuprofen—that solution can be extremely dangerous.