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Strategic Communications Advisory For Visionary Founders
13 Data Technology Founders
Product Market Fit Lessons
Let Customer Language Redefine Your Core Problem Statement
Alation launched with a clear hypothesis: customers needed a data catalog to find their data. Early customer conversations killed that assumption quickly. Satyen described what prospects actually said: “I don’t necessarily have a hard time finding their data. They didn’t even think about that problem. They didn’t realize that was even a thing. What they kept on saying was they had a hard time writing SQL queries because they had these databases and they didn’t know how to use the databases or how to query them.” Rather than defending the original thesis, the team pulled on that thread. A year of user interviews, POCs, and trials followed, all aimed at understanding the real shape of the problem before committing to a solution. Satyen described “doing a ton of user interviews, talking to customers, doing a whole bunch of POCs and trials, just trying to figure out the shape of what the product would be.” The problem your customers can articulate clearly is a more reliable signal than the problem you assumed they had when you started.
Treat Underwhelming Traction as a Signal Worth Investigating
Ajay Kulkarni found that moderate success was harder to navigate than outright failure. “A year into the IoT platform, we’re like, Why isn’t this thing more successful? I think bad ideas are easy to kill, great ideas are easy to recognize. But when you have a good idea, you’re like, Why isn’t it great? Or should we kill it or not?” The ambiguity of good-but-not-great traction forced a more rigorous diagnosis. Rather than accepting the numbers and moving on, Ajay used the feeling that “something feels off about the traction being good, but not great” as a prompt to interrogate the fundamentals. He broke the question into three possibilities: “Either we’re building the wrong thing or we’re building the right thing but at the wrong time or the right thing at the right time, but we’re the wrong people to build it.” That framework gave him a structured way to act on a signal that most founders would rationalize away.
Mine Your Lost Deals to Find Your Next Market
Ariel Katz didn’t find a new buyer segment through market research. He found it by asking why deals were being lost. When he pulled a full year of pipeline data and analyzed the reasons for loss, one category stood out: “in-house build.” It accounted for roughly 25% of lost pipeline, representing multi-million dollars in deals. Digging deeper, he found a distinct technical buyer: “developers, could be also, directors of development, sometimes CTO, but it’s really technical people that wanted to build correct analytics from day one into their application, but just didn’t find the right way to do so.” These buyers had evaluated the product and walked away, concluding it was “more oriented to the business Persona and analyst Persona,” so they decided to build in-house instead. That loss pattern became the clearest signal of an underserved segment and directly informed a repositioning toward a developer-first motion.
Reduce Your Customer Profiles Until Your Wedge Gets Sharp
In a large, established market, the number of directions you could build in is itself the problem. Vinoth Chandar, CEO of Onehouse, found that operating in the data infrastructure space meant “we could be building in many different directions and targeting many different types of users.” Rather than treating that breadth as opportunity, he treated it as a focusing problem that had to be solved through direct experimentation. Onehouse started with seven customer profiles and ran calls across each segment to test which ones actually held. “We’ve distilled it down to like [three customer profiles today], but we started with something like seven and then we tried to experiment with it. We took a lot of calls in different segments to actually narrow this down more and crystallize the value proposition.” The work of finding PMF was not discovery of a single breakthrough moment but the methodical elimination of the wrong segments. The fewer profiles you carry, the sharper your wedge.
Find Where Multiple Business Urgencies Converge on Your Problem
After emerging from stealth, Roy observed demand signals that went beyond a single pain point. The urgency his team encountered came from several directions at once: “cost is top of mind for probably every organization in the world today with this macroeconomic, we see a lot of urgency of teams, migrating their jobs into new platforms, obviously AI readiness, and the entire effort and initiative around that is making the underlying data even more important.” Each of those pressures pointed back to the same underlying need Roy was solving. He also described a broader shift in how teams were thinking: “we’re seeing a huge shift in mindset on how [to become more proactive]. How do I enable my data engineering team to focus on the right things?” When multiple independent business pressures all point to the same gap, you are not selling a nice-to-have.
Test PMF With Buyers Who Have No Reason to Trust You
Early customers from your network will often give you money out of goodwill, which makes it hard to know if the product is actually working. Ian set a harder standard: the signal that mattered was whether a complete stranger would pay. “We had a product that someone who had no familiarity with us and had no reason to trust us at all would give us money for.” When that happened, roughly six to nine months in, the team felt they had crossed a real threshold: “That’s the point where we sort of felt like we could look someone in the eye and ask for money, and it wasn’t unreasonable.” Network-driven revenue is a starting point, but it is not proof. Cold demand from buyers with no prior relationship is the cleaner signal.
Follow the Pain to Find Your Strongest Market Position
Sarah launched Seek with messaging aimed squarely at data scientists, built entirely from her own experience in that role. “When I was starting Seek out, I knew the pain point really well from my perspective, which was the data person perspective. So I was angling Seek more as just to solve some of this frustration you have as a data scientist, let AI do some of this work for you, that kind of messaging.” Talking with more customers shifted her understanding of where the real pain lived. “The more I talked with them, the more I was like, wow, their pain is just as important and they can’t even do their job without waiting for the data team to help them with this work.” The messaging evolved directly from those conversations, not from an internal strategy session. “Every customer we talk to, we get a little more clarity on this kind of messaging.” Your founding hypothesis about who feels the pain most is often wrong, and only sustained customer conversations will correct it.
Study the Individual Buyer's Motivations Before Targeting Any Account
With no inside connections and no startup experience, Rina and her co-founder went door knocking on LinkedIn and email to find their first customers. Rather than stopping at understanding the company-level pain, they went a layer deeper. “We spent a lot of time digging into the problem domain and understanding the pain of not only our potential clients, as in the companies, but specifically the buyer in the company, what drives and motivates him, what sets him back, and so on.” That individual-level clarity had a direct payoff: “It really helped us target our ideal customer profile very well and lend our first clients.” The company feels the pain, but a specific person inside that company decides whether to act on it. Understanding what moves that person is what turns outbound effort into pipeline.
Measure Prospect Emotional Reaction to Find the Problem Worth Solving
Barr tested multiple company ideas simultaneously and used cold outreach to gauge which problem generated enough pull to build a business around. The method was direct: “I would literally cold call people and say, hey, do you have this issue? Hey, is this a problem for you?” Some ideas went nowhere. The data reliability problem was different. “That problem had such a strong pull and such a strong reaction from people,” she said, describing how prospects immediately connected with questions like why they were always the last to hear about bad data and why their entire team was consumed fixing it. After “speaking with hundreds of people, it became clear that this is a problem that exists across industries, across stages.” The signal she trusted was not a survey score or a conversion rate. It was the emotional intensity of the response.
Put the Product in Front of Buyers Before You Spend on Marketing
Sean described winning customers as less about hype or brand spending and more about direct product exposure. “Get the product in the hands of customers and we win,” he said. “The vast majority of the time once somebody puts their hands on the product, they fall in love with it because they see the productivity gains that they get.” The gains were specific. Customers realized “they may not have to work 10, 12 hour days to achieve their team’s goals” and that “they’re not going to get paged at three in the morning because some pipeline broke.” “For them the real world tangible benefits of being faster, more productive and having more stable systems just wins the day.” When buyers experience concrete productivity improvements immediately, the product itself drives adoption.
Watch for Unsolicited Repeat Usage Before You Commit to a Product
Grid started as an internal tool with no commercial intention behind it. The commercialization decision came from a specific behavioral signal: consistent, unprompted usage by people who had no obligation to use it. That pattern of regular use was what triggered the conversation about whether this could become a real company. “When people started using that really basic MVP regularly, that’s when David and I kind of put our heads together and be like, I bet if we incubated this and actually turned this into a data platform, this could be a real company.” If people keep coming back to something you built for yourself, that behavior is worth paying attention to before you spend anything on a formal product.
Build a Feedback Loop With Users Before They Become Customers
Collate’s open source community gave Suresh direct, continuous access to the practitioners who would eventually buy. Those users weren’t paying customers, but Suresh was explicit about their value: “These are not paying customers, but very valuable users that are using our product, giving us feedback, asking us for features, providing inputs that shapes our project, shapes the product that we are building.” The volume and quality of those interactions was significant. “It’s a product manager’s dream. You get to interact with your customers on a daily basis. We have around 100 plus conversations that we have with our open source community contributors, participants. That shapes our project.” Continuous input from real users building with your product tells you what actually needs to be fixed, far more reliably than internal assumptions or occasional customer calls.
Keep Booking Customer Conversations Until the Positioning Clicks
For nearly 20 months, Datalogz had interest but no clear product-market fit, and Logan kept the team in motion by treating every conversation as a data point rather than a sales opportunity. The team stayed in the market consistently: “we know there’s a problem out there in the market, we just need to figure out what our positioning is to solve it.” The questions Logan brought to those conversations were deliberately investigative: “what’s the problem you’re trying to solve here? How much would you pay for it?” That discipline of asking and listening, rather than pitching, is what surfaced the pivot that eventually worked. As Logan put it, “we would have never gotten there without that longer grind of listening to customers and hearing feedback.” Positioning does not come from internal debate. It comes from enough customer conversations to hear the pattern.