In lean startup, the point of building a prototype, running experiments with your intended audience, and measuring what happens is to LEARN.
- Did you identify the correct audience?
- Did you choose a problem to solve that is both real and significant – is it a problem worth solving?
- Does your solution work, at least in part, at a cost that audience is willing to pay (cost being defined not just as money, but potentially as time or effort)?
That is, are you on the right track?
If you are, great! It’s time to persevere, to make your next investment of resources in running your next round of experiments as you work your way towards a fully fleshed out, marketable program, product, or service.
If you’re not, it’s time to revisit your assumptions about audience, problem, and solution, and use the data you gathered in the measure stage to see where you were off. Then you pivot your understanding of ONE of those elements, put together another prototype, and try again.
What if you learn you’re REALLY off? It’s OK to decide that you need to kill the project.
In fact, in the American Association of Veterinary State Boards case study related in Lean at 10: Culture Eats Methodology for Lunch, that’s exactly what the AAVSB did in their first iteration through the lean startup process. They learned that a product that had already been launched into the marketplace before starting to use the methodology was not salvageable, and they were able to make the evidence-based decision to kill it.
As I wrote about recently, remember that lean startup doesn’t guarantee success, it guarantees insight.
That insight proved valuable at the AAVSB, as they were then able to make a clear-eyed decision to terminate a failing program without casting blame on the board members who originated the idea or the staff members who had been charged with executing it. Staff wasn’t telling the board, “Your baby is ugly” (or vice versa), the data was saying, “You chose a problem that, while real for your target audience, was not significant for them.” That is, it wasn’t a problem worth solving. Terminating that program then freed up resources – money, volunteer attention, staff time – to work on something more promising.
That’s where the magic happens. You make a small investment and only make subsequent larger investments if the data supports them. You don’t waste significant resources going the wrong way.
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