About six years ago, before “Deep Learning” was hot, I was approached by a startup (let’s call them AnonAI) that had invented a powerful new technology.
The founder’s pedigree was impressive. He had built a very successful AI company in the past, which had led to a very public and valuable exit. Since then, he had invested his time in developing a new and improved machine learning algorithm, which found human-readable rules to describe patterns in very noisy data and make fast and accurate predictions. You could see the potential application in many markets — advertising, security, scheduling optimization, finance. The potential use cases seemed infinite.
But when you’ve developed an interesting new technology that has many potential use cases, it’s especially difficult to find the right one. Starting with a solution, rather than with a problem, will inevitably bias your customer discovery process. You’ll find yourself involuntarily pointing out the use cases to prospective customers, rather than listening to their problems. The prospect may even agree that there is room for improvement! But you risk discovering problems that, while real, are not particularly valued.. A real pain point is only Validated when it is both Verified and Valued.
Going back to AnonAI, advertising was one potential use case — AnonAI could offer machine learning as a SaaS tool to ad agencies to help them optimize for Click Through Rates (CTR). AnonAI even found a paying ad agency as a customer for that use case.
After spending time exploring this opportunity, it turned out that we couldn’t find a viable, repeatable business model, even though we had found a paying customer. The larger, more lucrative campaigns at the agency didn’t focus on CTR optimization at all — so optimizing for CTR, while it yielded results, wasn’t actually valued by the agency or their customers. Other digital agencies that focused on CTR optimization didn’t have high enough margins for the SaaS offering to provide and capture significant value.
While the technology worked exactly as it was supposed to, and while the problem it solved definitely existed, the pain point simply wasn’t Valued by potential customers.
AnonAI learned the hard way that, even when you have a paying customer for a technology solution that seems to solve the problem at hand, you aren’t done validating your real pain points. Unless you have especially deep experience in an industry, it takes disciplined, unbiased research to understand the valuable trends and business drivers in an industry.
It’s especially hard to keep such an unbiased view in markets where you already have a paying customer — even more so if your team has already put in a lot of work under tight deadlines into customizing your tech for that customer’s use case. In this case, it can be deeply demoralizing to suddenly find out that the use case for your technology is not widely valued in the industry.
This, unfortunately, was what happened with AnonAI. There was palpable slowing of momentum and lower morale after spending significant time exploring and abandoning the advertising use case. It ultimately led to the decision that the startup should pivot from a product to a services company.
In the past we’ve seen several unsuccessful big data startups that prioritized technology over pain point — we’ve seen a similar trend in the AI-hype cycle with “AI-first” startups and are now seeing the same technology focused approach with cryptocurrency startups.
Of course, this use case problem isn’t only faced by startups. I recently spoke to an ex-Product Manager at Motorola, who shared his experiences of the R&D team handing over exciting new technology and tasking him with finding applications for it. He described similar struggles of trying to force-fit solutions for new technologies.
Instead of starting with use cases, we recommend a vision-driven approach to bringing a product to market — that is, starting with a clear statement about the pain point you are solving and the change you want to see in the world. The team is guided by that vision to work on figuring out a solution. When there are setbacks in achieving your vision, the team is more resilient because they know the ultimate goal and why they are working on it.
Share your stories and experiences of trying to find applications for new technology. For a vision-driven approach to product development, you can download the toolkit for free from Radical Product. We look forward to hearing from you!