Go to Market Strategy: The Behavioral Economics of Your First 100 Customers

In the summer of 1999, Louis Borders had a vision so grand it required its own infrastructure. The co-founder of the Borders bookstore chain was building Webvan, an online grocery delivery service that would do for food what Amazon was doing for books. He placed a $1 billion order with Bechtel to construct a network of automated distribution centers. The first one, in Oakland, California, covered 330,000 square feet and contained five miles of conveyor belts capable of transporting 10,000 totes. He planned to open 26 of these warehouses across 26 cities within two years.

In November 1999, Webvan went public at a valuation of $4.8 billion. At the time, the company had cumulative revenue of $395,000 and cumulative losses exceeding $50 million. Nineteen months later, Webvan had burned through more than $800 million, laid off 2,000 employees, and filed for bankruptcy. The warehouses sat at one-third capacity. The fleet of custom delivery trucks sat idle.

Half a world away, a Swedish oat milk company called Oatly was about to attempt its own American market entry, and the contrast in approach would become one of the cleanest case studies in modern go-to-market strategy. In 2016, CEO Toni Petersson didn't build warehouses. He shipped cases of Oatly's Barista Edition to exactly ten coffee shops.

A go to market strategy is the plan for how you'll reach your first customers. Most founders get it wrong not because the plan is poorly constructed but because the brain constructs the wrong plan by default. The behavioral economics of market entry (prediction error, loss aversion, and the gravitational pull of the status quo) explain why so many launches look like Webvan and so few look like Oatly. The difference isn't ambition. It's understanding how the human brain evaluates a new product it has never encountered before.

Why Your Brain Builds the Wrong Go-to-Market Strategy

In 2006, Harvard Business School professor John Gourville published a paper in Harvard Business Review called "Eager Sellers and Stony Buyers" that quantified something every founder senses but few can name. Gourville had spent years studying why new products fail at rates that would terrify anyone preparing to launch, and his conclusion was deceptively simple: the problem isn't the product. The problem is the math inside the customer's head.

Gourville drew on Daniel Kahneman and Amos Tversky's prospect theory to explain what happens when a customer encounters something new. The brain doesn't evaluate a product on its merits. It evaluates the product relative to whatever it's currently using, and it weights losses roughly twice as heavily as equivalent gains. Giving up what you have feels about twice as painful as acquiring something new feels good. This is loss aversion, and it operates below conscious awareness.

The result is what Gourville called the 9x effect. Founders overvalue their own product by about three times, thanks to the endowment effect and the curse of knowledge. Customers overvalue what they already have by about three times, thanks to loss aversion and status quo bias. Multiply those together and you get a nine-to-one mismatch between what the founder believes the market wants and what the market's brain actually computes when faced with a new option.

This mismatch doesn't just affect whether people buy. It affects where founders launch, who they target, and how they allocate resources. The brain's prediction engine generates a market entry plan based on the founder's own model of value, a model that is, by definition, nine times more optimistic than the customer's. Webvan's plan made perfect sense inside a brain that could already see the future of grocery delivery. To the residents of Oakland in 1999, ordering groceries online was a behavior change so large it registered as loss, not gain. Every dollar Borders spent on infrastructure was a bet that his mental model of the customer was accurate. It was off by roughly a factor of nine.

The Beachhead and the Bowling Alley

Geoffrey Moore saw this problem play out across hundreds of technology companies before he gave it a framework in his 1991 book Crossing the Chasm. Moore noticed that products routinely gained traction with early adopters and then stalled. The gap between early adopters and the mainstream market wasn't a marketing problem. It was a neurological one. Early adopters and pragmatists evaluate products using different computational models.

Early adopters are driven by novelty. Their dopamine response fires on prediction error, the surprise of encountering something unexpected. A product that works differently than expected is exciting to them. Pragmatists are driven by risk reduction. Their evaluation weights the potential losses more heavily than the potential gains. A product that works differently than expected is threatening to them.

Moore's solution was the beachhead strategy. Instead of launching into the broadest possible market (which is what the founder's optimism bias always suggests), you pick a single, narrow segment where the switching cost is lowest and the pain of the status quo is highest. You dominate that segment so thoroughly that the product becomes the new status quo within it. Then you use that segment as a reference point to enter the next adjacent segment, and the next. Moore called this the bowling alley: knock down one pin, and its momentum carries into the pins beside it.

The beachhead works because it changes the math inside the customer's brain. In a narrow segment where the current solution is genuinely painful, loss aversion actually works in your favor. The customer's brain computes the cost of staying with the status quo as higher than the cost of switching. You don't need to overcome a 9x gap when the customer is already losing.

Oatly's go to market strategy was a beachhead executed with surgical precision. Petersson's team spent months identifying exactly ten specialty coffee shops in New York City and other major markets. Not any coffee shops, but shops that roasted their own beans, had passionate baristas, and attracted customers who cared deeply about the quality of what went into their cup. Intelligentsia Coffee was the first to sign on. Within months, drinks made with Oatly comprised 13% of all beverages ordered across Intelligentsia's ten locations.

The baristas became the bowling pins. When a customer at Intelligentsia tried an oat milk latte and experienced something unexpectedly good (creamy, neutral, better-frothing than soy or almond), that was a prediction error. The brain had predicted one experience and received a better one. Dopamine neurons in the midbrain fire when reality exceeds expectation, encoding the surprise as a signal worth repeating. The customer came back. They told someone. By the end of 2016, Oatly's entire New York City supply was sold out. Not because the company had spent millions on advertising, but because ten coffee shops had generated enough prediction errors to rewrite the model for what a minimum viable product launch could look like.

What Webvan Got Wrong That Instacart Got Right

The Webvan story isn't interesting because it failed. Plenty of companies fail. It's interesting because the same market, online grocery delivery, produced a company worth $39 billion fifteen years later. Instacart succeeded in the exact category Webvan proved was impossible. The difference was entirely in the go to market strategy.

Webvan built the infrastructure first and then went looking for customers. The company spent $35 million per warehouse, signed a billion-dollar construction deal, and expanded to multiple cities before proving that customers in a single city would change their grocery buying behavior. The mental model was: "If we build it well enough and big enough, they will come." That model treated customer acquisition as an engineering problem. Build the conveyor belts, optimize the routes, scale the fleet, and demand will materialize.

Instacart, founded by Apoorva Mehta in 2012, built nothing. Mehta used existing grocery stores, existing inventory, and contract shoppers. The first version of Instacart was, functionally, someone else shopping at the store you already trusted and delivering the groceries to your door. The switching cost was almost zero. You didn't have to trust a new brand. You didn't have to learn a new product catalog. You didn't have to abandon your existing grocery preferences. You just had to let someone else do the driving.

This is the behavioral economics of market entry distilled to its core. Webvan asked customers to change everything: where they shopped, what brands they trusted, how they evaluated freshness, how they planned meals. Every one of those changes registered as a loss in the customer's brain. Instacart asked customers to change almost nothing. The only thing that changed was who carried the bags. One company fought the 9x gap by trying to be so good it wouldn't matter. The other company eliminated the gap by removing the losses from the equation.

Your unique value proposition isn't what you think makes your product special. It's the gap between the customer's current experience and the experience you provide, measured in the customer's currency, not yours. Webvan's value proposition made sense on a whiteboard. Instacart's value proposition made sense inside the loss-aversion computation that actually determines whether someone changes their behavior.

How Do You Find Your First 100 Customers?

The first 100 customers are the most expensive customers you'll ever acquire, and not because of advertising spend. They're expensive because they're the ones who have to switch without social proof, without a track record, without the comfort of knowing other people have already made the same decision. Their brains are running the loss-aversion calculation at maximum sensitivity.

This is why the beachhead matters so much. Your first 100 customers shouldn't be representative of the broader market. They should be the people for whom the status quo is most painful and the switching cost is lowest. They're the people whose brains will compute the smallest possible 9x gap.

Oatly didn't try to convince the average American milk drinker to switch to oat milk. The average American milk drinker had no reason to switch and every reason not to: familiarity, taste expectations, price, habit. Oatly found baristas at specialty coffee shops, people who were already frustrated with the way soy milk curdled in espresso and the way almond milk produced thin, unsatisfying foam. For those baristas, the status quo was already losing. Oatly didn't have to ask them to change. It only had to offer a solution to a problem they were actively experiencing.

By the end of 2017, Oatly was in 650 cafes across America. By 2018, the brand had expanded to over 1,000 coffee shops and debuted in grocery stores like Wegmans, Fairway, and ShopRite, generating $110 million in revenue. The grocery store entry worked because the beachhead had already done its job. Consumers who had tried oat milk at their local cafe were now looking for it on shelves. The prediction model had been rewritten. Oat milk had become the thing they'd been enjoying every Tuesday morning at the coffee shop on the corner.

The napkin math is simple: find the people who are already losing, and give them a way to stop. That's your beachhead. Everything else is the bowling alley.