It seemed like a good idea at the time.
We were staring at what looked like a golden startup opportunity: an esoteric market dominated by an old-school incumbent making staggering amounts of money, but who seemed incapable of anything resembling modern software development. Alteryx was on track towards a billion dollars in annual revenue, almost entirely on the backs of a Windows-only, clunky desktop application for which each user paid over $5,000 a year. We thought that if we could bring a similar experience into the cloud, make it deeply collaborative and price it so that it could spread, we would have more demand than we knew what to do with. The fight had the shape of Figma vs Illustrator, and we were excited to take it on.
Alteryx grew up in the 2000s as a way to do spacial analysis without code, but growth really accelerated in the mid-2010s as a data prep and blending tool used to feed Tableau dashboards. Even after Tableau’s launch of its own competitor and later its acquisition by Salesforce, Alteryx remained the preferred way for business analysts to do their own work. The desktop application was expensive but loved by its users who had few alternatives, and the company had racked up several failed attempts to deliver a cloud-based offering. We knew that their customers had been putting pressure on them, so we thought we had a good shot at stealing those customers if we could deliver a competing solution faster than they could get their act together.
You’ll 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.
It didn’t help that Alteryx is the Rorschach test of the data space: as a general-purpose tool, it handled thousands of different use cases. Any data problem you had, Alteryx could morph to fit that need. The dispersion in use cases was enormous, making it extremely difficult to find patterns and pockets large enough to focus on and gain a foothold. Instead, our marketing as an Alteryx alternative caused us to spend resources saying no to most of the leads we encountered. And for prospects we could tell a compelling story for, Cascade only kind of worked. We had enough hatches and workarounds that we could do the job, but it was far from elegant and clear we weren’t purpose-built for their problem. We had a lot of ground to cover to get true feature parity with Alteryx, and even if we got there, it was becoming less and less clear what portion of their customers would jump ship.
We did know at the time that we were slowly becoming a solution looking for a problem. In being willing to change our target audience, we swapped our understanding of a customer for an understanding of a competitor. Buoyed by the existence of a massive, ripe (we believed) market, we allowed Alteryx to become a proxy for customer feedback and parity for parity’s sake to become north star. Unsurprisingly, as a small team we were spread pretty thin.
When it came time to raise more money, we stared at the amount of product we had to deliver in order to capture a meaningful market share. By that point we had already signed some major, marquee customers (including some of Alteryx’s largest accounts), but true penetration within those accounts was still a long way off. As a result, contract sizes were low enough that we had to question the sales engineering and feature work we were doing for each new account. We were making real process, but the road to breakout growth seemed long and treacherous. We decided to pursue other options.
Even considering our inability to dethrone them, our view is that Alteryx is sitting on a melting island. Squeezed between increasingly capable spreadsheets on one end and new data transformation tools like dbt on the other, it’s unclear that the modern data world needs the no-code paradigm that the company pioneered. However, their competition won’t look anything like Alteryx itself. Instead, its market will fracture into an archipelago of more specialized, more use case-focused fragments. As with many industries in transition, the next generation of this type of tool will look nothing like prior generations.
