The analogy at work here is that if you are on a warship at sea fighting another ship – and you have limited gunpowder – it is better to fire a few bullets first to calibrate your shot. This way, you can determine the correct firing range and increase the odds of success with your larger cannonball shot.
The business research conducted for Great by Choice
Collins and Hansen determined that a bullet is an empirical test aimed at learning about what works, meeting three criteria:
- Low cost. Cost is relative; one company’s million-dollar cannonball can be a larger company’s bullet.
- Low risk. Low risk equates to minimal consequences, not a sure-fire path to success; whether or not a bullet will be successful has yet to be determined.
- Low distraction. It doesn’t divert attention of the overall organization; it impacts only one or a few.
- Progress as it charts a course through uncertain territory.
- That it has discovered truths about present and future business prospects.
- Qualitative: what customers like and don’t like.
- Quantitative: how many people use it and find it valuable.
A minimum viable product can be something as simple as a prototype or some minimalist implementation that allows you to start the process of learning from your potential customers as early as possible. Consider the story of Andrew Mason, founder of Groupon:
“We took a WordPress Blog and we skinned it to say Groupon and then every day we would do a new post. It was totally ghetto. We would sell T-shirts on the first version of Groupon. We’d say in the write-up, ‘This T-shirt will come in the color red, size large. If you want a different color or size, e-mil that to us.’ We didn’t have a form to add that stuff. It was just cobbled together.”As you can see, while a minimum viable product helps start the process of learning as quickly as possible, it will most likely lack many features that will prove to be essential later on. The Lean Startup
Ries also make another great observation about how we measure productivity while we are navigating uncertain terrain using validated learning: “Don’t think about productivity in terms of how much stuff we are building but in terms of how much validated learning we’re getting for our efforts.” After all, Ries reminds us, “If we’re building something that nobody wants, it doesn’t much matter if we’re doing it on time and on budget.”

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