Validated Learning vs Waste: How do you know in advance if your efforts are a waste?
Validated Learning vs. Waste
Validated learning is a scientific approach to conduct a series of experiments backed by empirical data collected from real customers. This is done so that the entrepreneur doesn’t make a product that nobody wants. Anything that is done that does not contribute to learning is a form of waste. By continually validating what matters to customers, the startup is more likely to make positive improvements in the core metrics.
How do we know in advance whether our efforts for making a product are of any value or a complete waste
The question is how do we know in advance whether our efforts for making a product are of any value or a complete waste? The answer lies in the process of the lean startup build-measure-learn loop. You make a minimum viable product (MVP) having the basic features, let the customers use it, get feedback and implement that feedback in the subsequent versions. You don’t necessarily have to make all features together to their perfection and waste time on it, instead you just build the basic version and market it so that if you want to get feedback, you get it faster before making any efforts on making possibly something which is going to be a waste.
I would say that is a similar strategy as fail fast which says that if you are uncertain about something, just let it out, see the results and if you want to fail, just fail fast instead of waiting for a long time. This way you will get feedback faster and you are more likely to find the correct direction and path which will lead you to another iteration.
As Ries explains in his book The Lean Startup, an experiment is the first product. The experiment can be conducted in weeks instead of months like it happens in traditional management planning process. The experiments will give positive or negative results. Based on the results, you decide to pivot or to persevere.
If you cannot fail, you cannot learn
We should not be afraid to fail, because you won’t know what works and what doesn’t work if you don’t fail. As Thomas Edison says
“I have not failed. I’ve just found 10,000 ways that won’t work!” (Brainy Quotes, n.p)1
The benefit here is that the effort that is not necessary for learning about the customers’ wants can be prevented. This is called validated learning which is backed up by empirical data collected from real customers.
I started off with one my product with an MVP and launched it on Android first to get feedback and see the performance of my mobile app. I wanted to fail fast. I wanted to know whether there is a need for such app and I got a huge amount of feedback on various platforms because I provided a way in my mobile app to listen to the needs and wants of my customers. I got feedback on various aspects such as usability, features, data, and aesthetics. In fact, I got to hear from the domain experts which is a valuable feedback and was only possible after releasing an MVP because those were my real customers.