Product direction
CB Insights analyzed over 100 startup post-mortems and found that 35% of startups fail because there is no market need for what they built. Not because they ran out of money. Not because the team was wrong. Because they built the wrong thing. The root cause is almost always the same: the founder jumped to features before understanding the actual problem worth solving. They built what felt right instead of what was needed.
Why founders consistently build first and discover second.
Solution-first thinkingmeans starting with “Let’s build X” and then searching for people who might want it. The opposite, problem-first thinking, starts with “Who has a painful, frequent, urgent problem?” and then designs the minimal solution that addresses it.
Most founders fall into solution-first thinking for understandable reasons. Builder’s bias: technical founders are rewarded for shipping, and building feels like progress while research feels like stalling. Confirmation bias: once you have a solution in mind, you unconsciously seek evidence supporting it and dismiss evidence against it. The sunk cost trap: once you have started building, stopping feels like waste rather than wisdom.
As Melissa Perri describes in Escaping the Build Trap, organizations measure success by output (“We shipped 47 features this quarter”) instead of outcomes (“We reduced churn by 12%”). Shipping features feels like progress but is meaningless if those features don’t solve a real problem. The same dynamic plays out in a two-person startup, just at a smaller scale and higher personal cost.
Not scrappy startups. Well-funded companies with experienced teams that still got the problem wrong.
Jeffrey Katzenberg and Meg Whitman assumed people wanted premium 10-minute video content for "in-between moments" like commuting. They spent $1.75 billion building a mobile-only streaming platform with a novel Turnstyle feature that switched between portrait and landscape.
The job of "entertain me for 5-10 minutes" was already being done by TikTok, YouTube, and Instagram, all for free. Quibi shut down six months after launch. The problem was not execution. It was building an elaborate solution to a job the market had already solved.
Juicero built a $400 Wi-Fi-connected countertop juicer with DRM-locked produce packs, QR-code scanning, and IoT connectivity. The founders were in love with the hardware.
Bloomberg reporters demonstrated that squeezing the packs by hand produced the same juice. The job was "I want fresh juice conveniently." The solution required was a pair of hands. $120 million in venture capital was spent solving a problem that did not need solving the way they imagined.
Google Wave combined email, instant messaging, wiki editing, and social networking into one real-time collaborative platform. It was demoed in a 90-minute keynote that left audiences confused.
Users could not articulate what it was for. The product answered "What is technically possible?" instead of "What do people struggle with?" It shut down roughly one year after launch. The technology was remarkable. The problem definition was absent.
Amazon's Fire Phone featured "Dynamic Perspective," a 3D-like display using head-tracking cameras. The team spent years perfecting it. Consumers did not care about 3D gimmicks. They wanted a better phone.
Amazon wrote off $170 million in unsold inventory. Even the most customer-obsessed company in the world can fail when a product is driven by a technology vision rather than a validated customer need.
Jobs-to-be-Done reframes the question from “What should we build?” to “What is the customer trying to accomplish?”
The Jobs-to-be-Done framework, developed by Clayton Christensen at Harvard Business School, starts from a simple insight: people do not buy products. They hire products to do a job.Theodore Levitt captured it decades earlier: “People don’t want a quarter-inch drill. They want a quarter-inch hole.”
Christensen’s canonical example: a fast-food chain wanted to sell more milkshakes. Traditional market research segmented by demographics and asked what customers wanted. Nothing worked. Christensen’s team observed when people bought milkshakes. A large segment bought them in the morning for the commute. The job was “give me something interesting during a boring 30-minute drive that keeps me full until lunch.” The competition was not other milkshakes. It was bananas, bagels, and boredom. Once they understood the job, they could design for it.
Teresa Torres, in Continuous Discovery Habits, builds on this with the Opportunity Solution Tree: a visual map from desired outcome to opportunities (customer needs) to solutions to experiments. The critical layer most teams skip is the opportunity space. They jump from “we want to increase retention” straight to “let’s build a notification system” without asking what unmet need would drive retention.
Marty Cagan frames it as four risks: value (will customers use it?), usability (can they figure it out?), feasibility (can we build it?), and viability (does it work for the business?). Most teams focus on feasibility and ignore value. The result: a technically excellent product that solves the wrong problem.
Not a suggestion to do problem-first thinking. An enforced sequence that prevents solution-first mistakes.
You describe your venture. The AI generates specific customer personas from your context. Not market segments, but people with specific frustrations. You choose one. You cannot proceed to opportunities without a defined customer.
From your chosen persona, Bandos surfaces Jobs-to-be-Done: the specific outcomes your customer is trying to achieve. You vote on which opportunity is worth solving. The ideation step does not open until the opportunity is defined.
AI stakeholders challenge your direction from marketing, engineering, UX, and product angles. You hear the objections now, not after three months of building. Weak spots surface while changing direction is still free.
Every idea stays locked to the specific customer and problem that created it. You can compare three different product directions side by side and see exactly which customer need each one addresses. Context never dilutes.
Describe your idea. Bandos finds the customer, the opportunity, and the direction before you write a single line of code.
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