One way to think of companies is that they fall into three phases depending on what it is they need to kill.
To be successful, a large company needs to kill what the customers don’t want. Clayton Christensen mentions this in The Innovator’s Dilemma. He says that when you examine very successful companies, what you see are these very effective execution methods for exactly this. Not hard to see why – a large company with significant resources can easily head off into tangents, spending money on things that either don’t add value or that add value for small customers whose trade can’t move the revenue or profitability needle.
Small, growing companies, in contrast, need to kill whatever distracts them from their goals. When Rodney Perkins founded Resound, he started with a revolutionary invention that substantially improved the functionality of hearing aids. Instead of amplifying all sounds, the first Resound hearing aids amplified selectively, so that people heard voice more clearly, while background noises faded into the background. Perkins was (and is) a true innovator, and just as Resound’s products were gaining traction, he came up with an even more revolutionary idea – miniaturized hearing aids that would fit entirely inside the ear. This innovation would not only keep resound ahead of the market, it would vastly expand the market, to people with moderate hearing loss who didn’t want to suffer the stigma of wearing a visible hearing aid. Eugene Kleiner, on Resound’s board, nixed the idea. His view was that the company had traction toward becoming the market leader in an existing market, and that combining traction and focus would lead to success.
In an important way, startups aren’t really companies at all, they are, in the famous phrase, temporary organizations designed to discovery a valid business model. What startups need to kill is lies. If you can’t prove it, it’s not true. Try like hell to prove it, but if you can’t move away from it asap. Or if you can’t move away, then accept that you’re living with a lie (aka a risk) and bound it as best you can. When you’re trying to discovery customers, look at it this way – if the customer moves away from it, it ain’t true. True product market fit means a product that the customers cannot not buy. Customer discovery is a bit like Michelangelo’s line about chipping away all of the pieces of the block of stone that weren’t David. What remains, is what you can sell.
Startups die easily when they act like small growing companies and relentlessly kill distractions. Small growing companies die when they kill everything the customer doesn’t want. Resound had a huge potential market with in-ear hearing aids. If they had already been the market leader, it would have made perfect sense to go after that market. But Kleiner was right that the distraction would probably have killed them.
And here’s where we come full circle to Clay Christensen: What ultimately kills large successful companies is that they can’t act like startups. For a variety of structural reasons, it’s incredibly hard for them to force themselves to move away from what ain’t true. If you know from the certainty of quarter after quarter of sales, that your customers want desktop computers, and the minnows making small, expensive laptops are a niche that won’t move the needle, then you’ll avoid that niche. Avoid it even when a smart startup would hone right in on it. And you’ll die.
Who wouldn’t want to be an angel? Apart from the ridiculously loaded word, investing in startups offers a direct line to many of the comforts of the human soul. If you’re an angel, you get to share your hard earned wisdom, give back to the community, hang out with exciting, smart young people who would otherwise never invite you to their parties, be seen as wise, rich, and magnanimous. And in addition to all that, there’s the thrill of high-stakes gambling. Buried, way down in the list, is the idea of making money. For me, at least, it took a while for that motivation to rise to the surface.
Here’s how angel’s typically work: Instead of going to the track, they go to demo days. A handful of startups catch their eye and they think “I like the way this entrepreneur paws the ground, and I’ve been looking for a horse of this color to add to my stable…” Then they do some due diligence and filter out startups that, on a closer look, appear to be losers (the market’s too small, the people don’t know the domain or aren’t competent to build the product). Then they decide to own a piece of some fine-looking horse.
Once that’s done, they do three things to help the startup succeed: mentor, provide connections, and, sometimes, provide more investment. None of which are particularly effective.
Providing more money doesn’t seem to work – angels who double down appear to do worse, on the whole, than others (at least according to this study). Unless you’re a big enough shot that all your acquaintances are either scared of you or owe you big time, providing connections isn’t very productive. (Try this thought experiment: Imagine all the people on your linkedin list. If you were to reach out to every one of them and ask if they’d talk to a young entrepreneur of your acquaintance, how many would? Now of those, how many would be likely to go into business with, or buy from, your entrepreneur? Just because you decided to bet on a long shot doesn’t mean they’ll like the same horse.)
Mentoring does, on the whole, appear to increase payouts, but there’s huge variation in what the word means, and most of what’s called “mentoring” has little economic value, especially for the mentors themselves.
Instead, if you want to be an angel, and also want to make money, try focusing on these three things:
1) Systematically help your portfolio companies de-risk their businesses. Don’t think of mentoring as making yourself available to offer wisdom and connections. Instead, help portfolio companies do the structured work they need to do in customer discovery and in building a valid business model, in order to de-risk. There’s a fair amount of knowledge now about how startups should operate to minimize stupid, expensive failure. So don’t helicopter in with miscellaneous advice; support the process.
2) Focus on raising the value of equity over the length of the runway. If “lean” means anything, it means that startups need to maximize every unit of value created per dollar invested. This idea often gets lost in talk about iterating and pivoting and embracing failure, because all that stuff often smells like inefficient, unproductive exploration. It isn’t. Startups simply cannot maximize value by charging ahead. The less risky ones iterate and pivot and fail while their burn rates are low, in order to avoid flailing around when burn rates and stakes are high. That is efficient use of capital. That’s the most important thing an angel can do to make money, because in a few short months, when the startup needs more money, the angel and the entrepreneur will be sitting on the same side of the table.
3) Design for scale. Take a lesson from electronics manufacturers. When these guys come up with new devices, they build and test prototypes at an astronomical per-unit cost. But once they’re ready to scale, they focus on DfM, or Design for Manufacturing. Maybe three twenty-cent components will do the work of a single one-dollar component, maybe you can save on a software license by adding another chip, maybe a different layout will reduce the failure rate of populated boards by 10%. Design for Manufacturing frequently reduces the per-unit cost of electronics by 20%, even over good original designs, a metric that doesn’t matter before ramp-up, but makes all the difference for overall success.
Similarly, designing for scale can make the difference between a startup that moves sideways, always just barely justifying more investment, while the earlier investors get diluted to hell, and the startup with a sharp enough upward trajectory to retain value for seed investors. There’s no simple rule for when a company should shift to more scalable systems. Managing this issue has gotten easier, with cloud and just-in-time scaling solutions (see Eric Ries’s somewhat optimistic take here). The point for angels is that intervening with your wisdom and experience to help entrepreneurs manage the scaling trade-offs, is one of the best things you can do to maximize your investment
So, what else? If you’re an angel, what are your motivations? What interventions have maximized value for you? If you’re a at an angel-funded company, what have your investors done to make themselves, and you, rich?
— Matt
This is a long piece, examining biases and heuristics that undermine customer discovery. More like an essay than a blog post, but, I hope, useful for people who are trying to get the most out of talking with potential customers.
“There are no facts inside your building, so get the heck outside”
Steve Blank, in The Startup Owner’s Manual
“Gentlemen, our resources are limited….at present these resources are undertaking to speak to local people, and to act on what they find. This has led us to confusion.”
“Confusion?” says Marland.
“Confusion sir. By relying on the testimony of individuals, we have exposed ourselves to the prejudices of those individuals, and we have thus been chasing our tails.
Lloyd Shepard, in The English Monster
Talking to customers early and often is the lean startup movement’s prescription to cure the disease of founder fantasy. The disease is real enough. Creative people tend to believe their ideas; insightful people have faith in their insights. Entrepreneurs are generally insightful and creative, so it’s normal for these people to believe they are right and that their product will be the next big thing. Too often, this leads to startups based on plausible but mistaken ideas about who customers are and what they want – a fatal flaw if not rapidly corrected.
How does talking to lots of customers address this problem? Three main ways:
- If enough customers tell you you’re wrong, you might start to believe them.
- With enough data, anyone can do a better job at pattern matching.
- If you have to talk with scores of people, you’ll be forced to move beyond your circle of friends and their friends, which will expose you to responses from more objective strangers and therefore to the real level of interest potential customers may initially have in your ideas.
All of that is helpful, just not terribly helpful. It fails to address some of the key reasons behind “false positives” – validations of hypotheses that are actually invalid. Behavioral economists have done a fairly thorough, science-based examination of how people make decisions, and have concluded that many of the tools we use for decision making simply and demonstrably lead us astray. In the customer discovery context, customer interviews will frequently seem to validate wrong hypotheses. Customers’ predictions about their future buying behavior will be wrong; customer’s insights into their own decision process will be flawed; customer’s inferences about how other people may act will be fictional. Worse, this is not a peripheral phenomenon. You can interview fifty people, and forty of them will tell you they will buy when it’s ready; but only one will, or none. Worse still, you, the entrepreneur, are subject to the same errors and decision-making weaknesses, which compound the errors made by your potential customers.
In behavioral economics, the rules of thumb that we habitually use in making decisions are known as “heuristics.” Some heuristics lead to good decisions in some situations, bad decisions in others; some nearly always lead to bad decisions, and a few usually improve decision making. We’re commonly aware of many of these heuristics, such as overconfidence, over-valuing sunk costs, being paralyzed by too many choices, using hindsight, ignoring the importance of base rates, etcetera. The science of behavioral economics has focused on identifying and defining these heuristics, unveiling where they lead to mistakes, measuring the biases they inject into decisions, and pointing decision-makers toward strategies that minimize errors. This article focuses on five heuristics that are less well known but that play a big role in undermining the value of customer discovery:
- The Availability Heuristic
- The Curse of Knowledge
- Confirmation Bias
- The Halo Effect
- Representativeness, and it’s subset, the Conjunction Fallacy
Availability Heuristic
It’s not news that many mistakes get made because people are mentally lazy.[1] Amos Tversky and Daniel Kahneman were pioneers in elaborating, refining, and measuring this phenomenon, which leads us toward certain conclusions, not because they are based on the preponderance of the evidence, but instead because they are based on the evidence that comes most easily to mind. For example, we think robberies are much more frequent if we read about one yesterday, or if for some reason we have a vivid memory of one. We overestimate the frequency of terrorist attacks and celebrity divorces because examples come so easily to mind.
This heuristic effects, and can even be used to manipulate, how people see themselves. In a classic study, researchers examined their subject’s views about how assertive they were. Think for a moment how you would answer the question: “Are you an assertive person?” To answer in an unbiased way, you would have to review a long series of interactions you’ve had with people, over many years, assess them for assertiveness, and come up with some kind of tally and scoring mechanism. Instead of doing all that, most people, when faced with this question, substitute an easier one: “What examples come to mind of me being assertive or not, and what can I conclude from them?” If you happened to have stayed quiet at two business meetings this week, and waited patiently for a table at a restaurant, and backed down in an argument with your spouse, then you would be likely to conclude that you are, in fact, a meek person – even if you were Donald Trump.
Conversely, if you have a harder time recalling something, you will underestimate it. In the study, two groups of people were asked to recall examples of being assertive, and then to assess whether they were assertive people. The first group was asked for four examples, the second group for twelve examples. The first group found it easy to remember four good examples, and they generally concluded that they were assertive. People in the second group found it difficult to come up with twelve examples, and generally concluded that they were meek.[2]
In customer discovery, both the entrepreneur and potential customers will judge value propositions, features, and the likelihood of success based on examples that spring to mind. One entrepreneur we know was trying to support a manager who was moving to a different role. The manager said he wanted a way to pass on operational information. The entrepreneur asked the manager to recall circumstances where he had passed on this kind of information. Through the discussion they realized that the ability to make and capture checklists would be very useful – so useful that the manager said he would buy a product like that. Based on this and similar interviews, the entrepreneur went ahead and developed the software. But when he went back to the potential customers, they weren’t interested in buying. The fact that potential customers could recall instances where the product would have been useful biased them toward believing that they would use it frequently. Unfortunately, those instances were too rare to justify buying and learning to use the software.
The Curse of Knowledge
While it’s easy to make mistakes because you can only remember a few examples of something, the opposite is also true – knowing a lot about their area of expertise can lead entrepreneurs into error. Psychologist Elizabeth Newton’s doctoral thesis, “Overconfidence in the Communication of Intent: Heard and Unheard Melodies”[3] has spurred two decades of research, shedding light on everything from education techniques to advertising, to relationship counseling. Her work was based on a simple, easily repeatable experiment: Two people are given a list of popular songs. One person chooses a song, and attempts to communicate it by tapping on the table between them, and the second person attempts to guess the song. In consistent tests, the tapper predicts that the listener will identify the song about half the time – a 50% accuracy rate. In fact, listeners correctly identify the song at a rate of 2.5% – twenty times less often than predicted. The disparity arises because the tapper hears the song – without the song in his head, he couldn’t tap it out accurately. But once it’s there, he cannot unlearn it. He loses the ability to listen to the taps as the listener hears them, as simply taps, so he loses the ability predict how accurate the listener will be in identifying the song, and wildly overestimates how predictable the song is from the taps. This has broad implications.
When an entrepreneur talks about a prospective product she has it firmly in mind. She can’t unlearn what she “knows” about the product, and will overestimate, by a factor of 20, the hypothetical customer’s likelihood of understanding what it is. This problem may be even worse, because while the listener in Dr. Newton’s experiments were trying to match the tapping to a song thy are already familiar with, the customer is trying to match an unknown product to an unmet need that is itself difficult to pin down. In customer discovery interviews, the curse of knowledge leads entrepreneurs to extreme overconfidence about their how well they have communicated their plans. It is very difficult for entrepreneurs to discount a customer’s comments for the likelihood that those comments are based on misunderstandings.
Confirmation Bias
In a seminal 1991 article, the psychologist Daniel Gilbert founded modern study into the mechanics of belief.[4] Gilbert brought current psychological research to bear on an argument between the philosophers Descartes and Spinoza. Descartes believed that people first perceive and/or comprehend something (an object or an idea) and then assess whether or not they believe it. Spinoza argued that the act of perceiving/comprehending involves believing, and that rejecting an assertion or questioning a perception only comes later.
The research strongly supports Spinoza. When you hear something, you automatically, unconsciously, see it as true. Critical systems, which judge the truth of statements, operate more consciously and only go into action after the words are understood and believed – comprehension and belief arrive first and together; judgment and disbelief follow. Some consequences are that if people are told something and then distracted, the distraction will interfere with critical thinking and they will be more likely to believe it. Considerable evidence shows, for example, that people are more likely to believe the claims in commercials if they are tired or distracted.
In customer discovery interviews, customers are working to understand what the entrepreneur is describing. That work involves believing that what the entrepreneur says is true. If the use scenario has multiple steps, or the product has multiple features, then the customer is likely to unconsciously stipulate the truth of the first step or feature, just so that she can comprehend it well enough to make sense of the next step of feature.
The Halo Effect
The Halo Effect was first described and tested in detail by the psychologist Solomon Asch in 1946.[5] Kahneman describes the effect as “exaggerated emotional coherence.” Asch’s experiments showed that we use first impressions, or primary impressions, as guiding principles to interpret additional information, striving to create a coherent “story” about whoever or whatever we are trying to comprehend. If you appreciate Lance Armstrong for his work with cancer patients, you will tend to interpret doping allegations as sour grapes from his lesser rivals. Conversely, if you think of him as egotistical or coldly mechanical, you’ll be more likely to think he cheated. This effect manifests itself almost instantly. In one of Asch’s key experiments, he gave subjects descriptions of two people, and asked them to write short descriptions of each one. Here are the people:
Alan: intelligent – industrious – impulsive – critical – stubborn – envious
Ban: envious – stubborn – critical – impulsive – industrious – intelligent.
The descriptions are identical, but the order of presentation makes all the difference. Subjects had much more positive impressions of Alan than Ben just because they were exposed to the good traits first. Interestingly, Alan’s negative traits were interpreted as less negative in the light of the positive ones. “Well, of course Alan is critical; he’s smarter and works harder than other people, so it’s natural.” The same effect operates in reverse for Ben “Yes he’s industrious, he has to work hard to be better than everyone else.”
In customer discovery, the Halo Effect distorts reactions both overall and in respect to features or aspects of the plan. Potential customers can think “This entrepreneur seems really smart, her idea must be good.” The can also let a positive impression of one part of the business model color the others: “Customers will love this, so partners should be found easily.
The Conjunction Fallacy
Tversky and Kahneman’s most famous experiment was called the “Linda Experiment.” In one version, large groups of people are presented with a description of Linda and a question. Here’s the description: “Linda is thirty-one years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in antinuclear demonstrations.”
Here’s the question:
Which alternative is more probable: 1) Linda is a bank teller. 2) Linda is a bank teller and is active in the feminist movement.
Respondents overwhelming pick #2 – it fits the description so much better. Of course, it is logically impossible. #2 is a subset of #1, so there is no way #2 is more probable.
Twenty five years of experiments that follow up on the Linda experiment have yielded some important conclusions. From a customer discovery perspective, maybe the most important one is that adding details to a story has a contradictory effect – on one hand, it makes the story seem more likely to be true. On the other hand, it makes the story LESS likely to be true. Describing a fantasy product in detail “It’ll do this, this and this, people will buy it here, and take it out to use when this happens…” conjures up a coherent, reasonable scenario. The better an entrepreneur elaborates this kind of story, the more likely potential customers will buy into it. Not because it is true, but because people naturally jettison the hard question “Are each one of these assertions true?” and substitute an easier question “Does this story as a whole make sense?” Avoiding the conjunction fallacy requires keeping things as simple as possible.
Conclusion
Steve Blank’s definition of a startup is a “temporary organization designed to search for a valid business model under conditions of extreme uncertainty.” It’s the “extreme uncertainty” part that amplifies the effect of every one of these fallacies and heuristics. Uncertainty is why entrepreneurs need to do customer discovery in the first place. But customers are uncertain too. They may have very powerful needs that will ultimately catapult the business to greatness, but those needs are probably poorly defined, because if they were well-mapped, then established companies would already be filling them.
Customer discovery is designed to map new territory, and new territory is exactly where people are most likely to misunderstand themselves, substitute easier questions for harder ones, buy into false but coherent stories, and leap to conclusions based on inadequate evidence. Talking to lots of customers is a start, but only a start, down the path toward a valid business model.
[1] Of course, there are good reasons for mental laziness. Perhaps the most important one is that using less effort means that people can make decisions more quickly. In numerous situations, coming to a quick decision, even if it may not be right, is preferable to coming to no decision at all.
Availability: A Heuristic for Judging Frequency and Probability,” Amos Tversky and Daniel Kahneman 1973.
[2] Norbert Schwartz et al., Ease of Retrieval as Information. http://sitemaker.umich.edu/norbert.schwarz/files/91_jpsp_schwarz_et_al_ease.pdf
[4] How mental systems believe. Gilbert, Daniel T. American Psychologist, Vol 46(2), Feb 1991, 107-119. doi: 10.1037/0003-066X.46.2.107
[5] Forming Impressions of Personality,” Solomon E Asch, Journal of Abnormal and Social Psychology 41 (1946) http://www.all-about-psychology.com/solomon-asch.html
Throw out your science fiction and start reading mysteries. A good science fiction novel is the very model of the failed startup, but a good mystery shows how startups succeed.
Why? Science fiction moves from vision (space colonies, genetically engineered children…) to a world incorporating the vision (maybe clones living on Mars) to a problem endemic to the setting (maybe one clone comes out different…). Science fiction writers succeed by constructing a plausible world around their innovations. The more detail they make up and fit together, the more real it looks. Writers like Tolkien and Herbert spent decades constructing and fitting together imaginary details, and as a result, you read their books and feel like you could step right into Middle Earth or Dune.
The same thing happens in real life. An entrepreneur can fit together lots of painstaking details in the form of a scenario (“Here’s how marketing managers will use my software to analyze the success of their campaigns….”) Even more wastefully, he can fit together details in the form of features on an actual product. The details make the vision more plausible, even compelling, to the founder, and to potential customers. But this is the conjunction fallacy. As Kahneman and Tversky famously demonstrated with the Linda experiment, the more details you add, the less likely it is that a story is true. Even if you started out with a critical eye toward your product idea, the process of answering objections and filling in details leads you toward belief, at the same time leading your idea away from the truth. So stay away from science fiction.
Mysteries, however, are perfect. They move from “problem” (the old lady was stabbed! Who did it?) to “motive” (turns out the housekeeper was in love with the grandson and the old lady was having none of it) to means (the housekeeper noticed that the sneering young handyman was always losing track of his screwdrivers…) The “means” in a startup is the product. In a mystery, it’s usually the murder weapon.
Mysteries start with obscured means. Sometimes, they’re totally invisible (“I can’t believe it, there’s not a mark on her!). More often, the means are hiding in plain sight (“She was clearly done in by a screwdriver. Hmm, perhaps that sneering handyman? ”). But a well-written mystery almost never tracks directly back from the means to the criminal (his fingerprints are all over it! Book him!) Instead, the absolute rightness of the means doesn’t become clear until the final chapter, where motives and circumstances have been laid bare, and the murder weapon could not have been anything else but the handyman’s lost screwdriver. At that point, it’s not just a screwdriver, it’s an object that anchors a rich set of meanings. The screwdriver is proof, for example, that the crime was premeditated, and that the housekeeper was ruthless enough to frame the handyman.
Just so in a startup. Even if you come to the game with a product vision, gleaming and compelling as a bloody screwdriver, set it aside. Instead, explore your customer’s hidden motives and exact circumstances. If you do that, the shape of their need will become clearer and clearer, like the shape of a missing piece in a jigsaw puzzle. Until finally, customer discovery becomes a template for your product.
[NB: Merrick and Matt have collaborated on all the posts in this blog up till now. We don't agree on everything, though, and this piece is a case in point. From now on, any piece where there's significant breathing room between our opinions will be signed, so that the other person can disagree in print without creating confusion. If you smell blood in the water, feel free to jump in too.]
At dinner recently, a guy I hadn’t met before was talking about his girlfriend. He said something like “she’s got the trifecta – smart, beautiful, and kind.” The guy was pretty open for a new acquaintance, clearly an introspective and insightful person, and obviously in love. So it got me thinking: why was he talking such bullshit?
Whether in business or in love, the key is emotional connection. His problem was that he was talking about features. What’s a value proposition? How do you distinguish between a value proposition and a feature list? More to the point, how do you discover the value proposition that enables a product/market fit that makes a successful startup possible?
I have no doubt that the guy’s girlfriend is in fact smart, beautiful, and kind. But that’s just her feature set. Any number of women who fit that description pass through his life every day, and he probably doesn’t give them a glance. Conversely, he could easily fall in love with someone with none of those features.
Personally, I’m in love with my wife. Why? Because I need someone who sees through my bullshit and punctures it, to keep me grounded. At the same time, it has to be someone who appreciates my intellect, because I need my ego stroked in that department. I can’t do without a partner who likes my jokes and tells me good ones, because the pleasure of someone you can laugh with takes a lot of pressure off. Volatility is also important to me, and someone who withholds approval, but will offer it if I’m being true to myself. I also need someone who’s judgment I respect so much that I can force myself to go along with her decisions, even when I disagree with them, without feeling like I’m just caving in.
Here are four salient points about the last paragraph:
- It’s nowhere near the whole story, but at least it is part of the real story. It’s the beginning of a description of the value proposition I need from a life partner; not just a set of features that sound reasonable but don’t really impact on my behavior.
- It was difficult to share. I forced myself to try to be honest (thank you, James Altucher, for the example of your blogging courage). The analog is, you can’t easily discover your customers’ value proposition by asking them stuff, because they too will find it difficult to share. They’ll be happy to talk to you about features; but most people, most of the time, avoid revealing genuine needs and vulnerabilities. And a vulnerability is the shape of the hole that a value proposition has to fit into, to offer true value.
- Like I said, it’s nowhere near the whole story, and not just because I refuse to tell it. Customers are not only reluctant, but to a great extent unable to reveal their vulnerabilities even to themselves, which makes it hard for you to get at them.
- I’ll bet you found the paragraph a bit uncomfortable to read. Too much information (TMI) at work. Real vulnerabilities are painful, and if you’re interviewing someone in a customer discovery process and you’re empathetic enough to feel their pain, you’re likely to move away from it, instead of trying to discover what’s going on.
So in typical customer discovery interviews, it’s easy to dig up fool’s gold – features instead of value propositions. And for startups, features just aren’t useful. In a well-known study by Richard Nisbett and Timothy Wilson, 128 people were asked to look at different variations of a fictional portfolio, of someone applying for a job, and to assess the person’s qualifications.[1] The portfolios included a mix of supposedly relevant information, like the person’s college record, and irrelevant information, like a comment that they had spilled a cup of coffee on the director’s desk. By correlating the different variations of the portfolio with the subject’s assessments, Nisbett and Wilson gained insight into what actually influenced the assessments. By asking questions afterward, they gained insight into what the subjects thought had influenced them. As you can guess, there was virtually no correlation between the two. The subjects were not influenced by many of the features they said had influenced them, and were heavily influenced by features they swore made no difference.
If you tell a prospective customer about a feature set, their response, positive or negative, is unlikely to predict their buying behavior. Your only chance is to expose a prospective customer to a value proposition. They may move toward it, or be repelled, or be indifferent to it. But in each of these cases, it is possible to gather information that will be useful in your search for value proposition that really provides value.
This is just as true if your startup is in the enterprise software space as it is for personal care items. Buyers of enterprise software have real jobs to do that matter to them. They are as likely to have hidden guilt and frustration and fears of inadequacy about their enterprise software needs, as drugstore customers in the incontinence aisle, and understanding their genuine needs makes it possible to sell to them.
The real distinction isn’t between enterprise and consumer customers, it is between established companies and products, on the one hand, and startups on the other. Established products, by definition, have found a product/market fit. Their products have authentic value propositions. So for them, features matter a lot. Cheaper, more pleasing, smaller, more convenient, more luxurious, UIs that “pop” and doors that close with a solid “thud.” These sorts of things are almost always features – crucial for existing products in established markets, distractions for startups that need to discover new value propositions.
The marketplace is a weird phenomenon. Looked at one way, it’s complete and impenetrable. Companies provide every imaginable product or service; people buy according to their needs, preferences, and resources. To make room for itself, your startup has to push and shove and make a space in the established web of commerce. But look at it another way, and you can see an infinite variety of unmet human need, frustration, yearning, and fear. Much of it has little to do with commerce, but the part that does is plenty big enough for your startup to discover a real problem to solve.
- Matt
Geoffrey Moore work on the “chasm” was one of the first, and still most insightful peeks into the mechanics of startups. Moore’s key point was that startups face a lull in activity when they’ve saturated the early adopter market, but can’t get traction in the mainstream market because that’s dominated by pragmatists, and pragmatists resist buying until a disruptive product is vetted and vouched for by other pragmatists.
More recent work by Steve Blank and Eric Ries focuses on startups in an earlier stage of development – before and during the process of figuring out how to sell something to those early adopters. Through two cohorts at Flashpoint, and through watching accelerators, we’ve seen that this earlier stage has its own version of a chasm – what I’m calling The Pothole. Potholes are smaller than chasms, but companies at this stage are smaller too, and more fragile, so even a pothole can be fatal.
The pothole appears at the end of a 3-4 month accelerator stint. Companies that go through these programs work hard at something through the first months of their accelerator journey. If it’s a Steve Blank-inspired accelerator they work on customer discovery or validating other parts of their business model. If it’s another accelerator, they work hard building their product. For the last few weeks or month, they lose focus on either of those issues and turn to crafting an impressive presentation or demo for investors. Then demo day comes and they get their two or three or seven minutes in the limelight. Then comes the pot hole.
1) The support structure or mentorships they were leveraging diminish or go away
2) They have to focus on raising money, usually for at least three months
3) During the fundraising period customer discovery moves backward – with all their time and effort concentrated on polishing and telling their story, and trying to convince investor’s it’s true, the idea that they are startups searching for answers is replaced by a myth of certainty.
4) Team members get distracted, need to work at other jobs, and their commitment as a team starts to weaken – raising all kinds of flags for potential investors.
The Chasm is a real threat – it can easily swallow up millions of dollars of A and B-round professional money. The pothole isn’t such a big deal – neither the founders nor the funds associated with the accelerators have nearly as much at stake. But it’s plenty big enough to knock the wheels of a startup.
Not much has happened in this blog recently, but so much has happened with Flashpoint. http://flashpoint.gatech.edu/
The first cohort of companies graduated in January, and every company that wanted to raise money did, in many cases from top VC firms. The second cohort graduated this month, and they’re seeing a lot of interest. We don’t actually believe that “percentage funded” is a good measure of progress, because it’s dependent on VC’s insight into early stage companies, which is just as flawed as every else’s insight, and on the state of the market, which is capricious. Still, pending better metrics, validation for Flashpoint is good, and investment for the graduates is better.
Merrick has raised the bar in customer discovery – refining what it means to discover a true product/market fit, and identifying and addressing the many cognitive illusions and pitfalls of motivated thinking that lead people to misunderstand where they are going and what they find when they get there. More on that in the coming posts. And welcome back.
