What do you do when you’re sure people will love your product, but they just don’t know it yet? This is a frequent challenge when your product falls on the early end of the adoption curve of your market segment. Or even more so if you’re doing something brand new that nobody else is doing.
I had a conversation with a friend recently who is thinking about starting a new business. She was getting frustrated that for all of her genius ideas “somebody was doing it already.” I told her that from my perspective, it’s actually much easier to build a better version of a product in a market segment that already exists rather than having to invent a brand new category.
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If you’ve built a better version of the board game Mouse Trap, for example, there’s already shelf space dedicated to board games, people know what board games are, some people are even board game super fans with blogs, Twitter feeds, and in-person game nights. In other words, a lot of the infrastructure you need to get your product off the ground already exists.
If you decide to invent some new way for people to have fun (bubble soccer?) you have to start from scratch to figure out how that fits into people’s lives, where they’d hear about it, what they’d be willing to pay for it, etc.
We did some market research in our space recently and discovered that it was only *after* buying our product that people really *got* the value proposition of the product. This was a fascinating discovery given that we were being successful selling the product already.
Our assumption had been that we had found product market fit and people understood the value, which caused them to make a buying decision. In reality, it was more like they suspected there was value there and were willing to give us a shot even though they’d never had a product like ours before and weren’t 100 pecent sure at the outset.
This is changing how we think about user adoption and onboarding as well as all aspects of sales and marketing. For example the theme of our annual Schoolrunner Data Institute is “Learning to Love Data” rather than something like, “Doing More with Data Since You Already Know You Love It So Much.”
The key to learning is to never stop validating your assumptions—you never know what you might learn.
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