May be I don’t understand the Lean-startup lingo, but to me a MVP has always been about finding the cheapest, safest way to validate your product hypothesis. Sometimes you might need to build a miniature version of your product or service to test your hypothesis and obtain validated learning. But it is not always necessary or even desirable to build something.
Let’s zoom out for a minute. Let’s say you have an idea or a vision for a product or a service. You devise a series of possible strategies you could use to fulfill your vision. However it is important to acknowledge that each of your strategy is based on a list of hypothesis, which needs to be validated using a series of cheap, safe-fail experiments (via MVPs) to obtain validated learning. Then based on real data, we pivot or persist the direction of the vision. Either ways, you need to constantly keep running a series of experiments with real fast feedback cycles to calibrate/validate your progress/direction.
MVP is a safe-fail experiment. The best MVPs are those which give you maximum validated learning for minimum investment (time, effort & opportunity cost.)
- NeedFeed used a GreaseMonkey script to quickly validate their hypothesis about their social purchasing app on Facebook - http://vimeo.com/24749599
- At EdventureLabs we used presentations to create quick videos to test different learning techniques and their retention power with kids.
- Or we used dummy meters to validate the business model of an energy company, which wanted to build energy saving products for rural India. We visited a few farmers and small factories, explained how the device (dummy meter) would save them 50% on electricity bill each month. We quickly discovered that our business model was flawed and surprisingly we co-created a better model. Also through this process we learned about certain key concerns these folks had which required a very different conceptualization of the product.
Your ability to quickly (almost on the fly) tweak a little parameters and quickly test a corollary hypothesis is another very important characteristic of an MVP. This is extremely important because when you go out there in the field to run your experiment, in the moment, you might find new data or ideas which might need to be validated to solidify your validated learning. If you have to make code changes and deploy stuff, it might not be easy for you to test new hypothesis, right then and there. Which is very important IMHO.
Next time you think of a MVP, think about a cheap, safe-fail experiment you can run to validate your hypothesis.
Note: Its important to distinguish MVP from Features Stubs. Feature stubs are also a quick way to validate your hypothesis, however they are mostly applicable once you have a product and want to validate how useful certain feature might be.
For example: Recently, I wanted to test if liking a comment on the Agile India Submission system is a feature people would find useful.
I added a like button which would simply show an alert message saying “Coming Soon..”. Using Google Analytics, I was able to measure that out of 36,000 impressions, only 6 people clicked on the Like button. A cheap way to validate my hypothesis. But this does not affect my product strategy and hence its different from MVP.