Not enough stories get told about startups that don't make it. @jakefuentes and @brelig recently shared a bunch of hard-won and incredibly insightful lessons from their attempt at building @Cascadeio (a data logic and analysis tool). Some of my favorite takeaways: On taking on an entrenched incumbent: "The only major player that occupied our category was Alteryx, a $10B+ legacy incumbent. The Alteryx product is both extraordinarily old-school and massive: a Windows-only, desktop product that grew up in the 2000s and was far from modern. But we underestimated two things: the amount of product we would need to build to start repeatably winning, and how high switching costs would be for many of their customers. We didn’t have a clearly identifiable market force at our back, other than broad trends around collaboration and cloud-based solutions. Those trends helped enormously, but we needed more than that to overcome the hurdles we faced." "You’ll also notice a subtle flaw in our logic that later became clear: we had just defined our audience as whoever Alteryx’s customers are, and our strategy was now to get feature parity with the incumbent so that we could go head-to-head with them. What we lacked was a clear bead on customer pull: most of the end users we talked to said that they wanted the improvements we proposed, but answers got a lot fuzzier when we tried to figure out what they would pay or what it would take to switch. We thought that we just needed to find the segment of their customer base with the largest need for an upgrade." On GTM for a general-purpose tool: "We underestimated two things. First, the more use cases a product can tackle, but blunter the overall value proposition is. The sales and marketing motion for blunt products needs to be incredibly strong, probably stronger than most early-stage companies with founder-led motions can assemble. Each customer cares only about their use case(s), not all the rest of the things a product can do." On the value of "why now:" "We overestimated our 'why now'. Even with comps like Figma, we weren’t as clear as we should have been about why the world needed a cloud-based version of Alteryx now. We could have answered that question in a number of ways, but that would have forced us to examine larger market forces more closely, establish allies and try to ride a wave rather than swim the distance." On the importance of a focused ICP: "We allowed our ICP to fray, replaced by a focus on a competitor. In our fight to prove that we could win against Alteryx, we over-rotated on any customer that might be interested in switching or considering alternatives. While being responsive to customers sounds good, being responsive to any customer creates a slow death of a thousand cuts. In the pursuit of breakout traction, we were too responsive to any sign of that traction, which pulled us in a million different directions at once." On trapings of a second-time founder: "There is a long list of things reasons that repeat founders do well: prioritization is clearer, more scenarios are anticipated and gaining the trust of the team, customers and investors is all easier. In total, the company can move a lot faster with experienced leadership. We're incredibly grateful for a broad group of supporters rooting for us along the way, some of whom were customers or were instrumental in getting deals done. But we overlooked an important nuance: deals done on the back of personal relationship equity is not the same as market signal. Founders can sell almost anything, and we did. We took those early wins and strode forward, using our ample cash to try to build our way to breakout growth. No product-focused founder is immune from the instinct to build, but repeat founders especially are given the runway to indulge that instinct. We must be especially careful to see the market clearly." On building in the data and analytics market: "A big reason we sold our product that way because of precedent: BI and analytics tools have typically been sold without presupposing the specific analysis being produced. Companies from Looker, Tableau, Mode, Metabase and many others are sold as general-purpose data tooling, in contrast with products like Amplitude which are more use case-specific. The problem was that we were trying to sell a data tool to business teams. In fact (and I know this sounds obvious), data teams buy data tools, and business teams buy business solutions. We were selling data tooling to business teams. We knew we were breaking that rule, with the assumption that business teams needed to expand their own data capabilities. We knew that data teams did not feel the pain of the “analytics breadline” as much as business teams did, who had to wait weeks for report adjustments or ad-hoc analysis. For their part, business teams had far more context on what they wanted, and we thought that they would jump at the opportunity to take on some of that work themselves. We found a few prospects to back up the conclusion we had already come to." See thread for links to the full posts. Massive props to @jakefuentes and @brelig for taking the time to share these lessons, and for taking a shot at building something great.