Science innovators looking into the world of customer discovery can sometimes feel like someone has slightly miscalibrated their diopter. The book Talking to Humans by Giff Constable is one example. The book is a gift to all innovators trying to figure out if their product has value for customers. It takes the theory of customer discovery and explains how to actually conduct it with real people in the world. But it starts with a fundamental and unacknowledged assumption.
The book is based around what I assume is a fictional case study of a team of two researchers who have invented artificial down (as in feathers). They have shown that their substance has “heightened levels of insulation” and “a better resilience/ resistance quotient”. They want to use it to make pillows that will help people sleep better. The unacknowledged assumption is that this is the best application for this innovation in polymer chemistry.
I have worked with hundreds of university researchers, the majority of them chemists, delivering training pioneered by my colleague Dr. Judy Giordan. In this work I have seen research scientists often make the sort of unacknowledged assumption in Talking to Humans. But it’s not their fault. Unfortunately the people they look to guide them in the commercialization process — university entrepreneurship faculty, investors, other mentors — are themselves blind to how problematic this type of assumption can be.
Talking to Humans is about how to identify and validate the key elements of a business model, highlighting the actual “get out of the building” practice. But the book leaps right over the question of whether the market in which the business model is being tested is the right market in which to start. What if the researchers profiled in the book have done all this work on a business model in a wrong or sub-optimal market? What if their innovation in polymer chemistry would be better suited to some other application? They may learn that potential customers don’t value their innovation and that there isn’t a business model to be had with those customers in that market. In this case, how do they choose another market? At best they’ve lucked out and targeted the right market. But they may also end up down a path that is sub-optimal for their science. If there are multiple markets for a product doing the deep dive of customer discovery into each market is a tremendous amount of work and one for which research scientists are unlikely to have the time. Our fear is they give up and never find the right application-market pair, as we call it, for their science.
This blind spot in the lean startup world comes, I believe, from a bias based in a lens trained on software. Unlike a software product or a purpose built engineered device, the example used in the book appears to be a more fundamental science breakthrough, potentially a platform technology. With software and engineered devices, the market for which the product is designed is usually the right market. For example, an app designed to help someone track and improve their sleep probably can’t be used for something else. An engineered device to treat sleep apnea is unlikely to have a wholly different application. The question for innovators in these cases is whether customers really care and whether there’s a business model that can be developed for a profitable company. Sometimes the potential customers initially identified aren’t the right customers but other customers in that same market— for example, customers for a sleep apnea device might not be end-user patients but perhaps sleep clinics. But an innovation such as described in Talking to Humans is not the same as a sleep-aid app or a sleep apnea device. It’s a more fundamental innovation that might apply to wildly different markets, not simply different customers within the same market. The process of that initial market screening is essential for many science innovations. My colleague Judy Giordan is the only person I know who has demonstrated how to teach this to scientists. We’re working with research teams such as those at the Center for Sustainable Materials Chemistry at Oregon State to apply these practices. This advance work for customer discovery still requires getting out of the building. It allows researchers to gather data on where they might best do a “deep dive” into a business model. Often they discover they have to go back to the lab and further the science because what they thought they knew about customers and market changes with the data collection process. In the future we’ll be writing more details about this work.
Again, Talking to Humans is a great book. But science researchers should realize that they likely have steps to take in advance of the customer discovery process, especially if their innovation has broad applications across multiple markets.