Validate it B4 you Create it

Validated learning seems so simple. And by definition it is. By gaining information, one can either confirm or deny her assumptions. This is the basis of validated learning. By validating these initial assumptions, the company is able to garner traction to move forward in the same direction. It creates a stronger basis for a startup than jumping into assumptions that could quite possibly be incorrect. However, the value and analysis behind validated learning is much more complex. Stemming from how you decide to attain the information, the means of data you are using for analysis, and what type of assumptions you’re planning on testing, the conclusions garnered from the learning process can be applied a number of ways to your startup.

Real validated learning is used not only for investors, but it is integral for the founders. While investors may insist on seeing traction in a company prior to lending funds, if the founders do not take the time to validate before kickstarting the company, it’ll ultimately be a flop with or without the influx of Monday from investors. The best type of validated learning should not show that you’re company can make x-amount of money from a specific endeavor, because those means of analysis are not dependable and will change as the company evolves from one initiative or product to the next. The valuable validated learning will come from testing assumptions that are not going to change about your company, product, and/or customer base.

In the example we saw with CarrotSticks, the lack of Validated Learning they did prior to demoing their product led them to develop the product surrounding a set of false assumptions about both the teachers and their students. They wasted valuable time, money, and man-power to something ended as a flop before it even began. Speaking to Pete Koomen, co-founder of both the failed CarrotSticks and successful Optimizely, he cited validated customer learning as an integral part of early and late startup success. It approaching Optimizely, Pete, made sure to validate all the team’s assumptions through very simple tests before actually creating the actual product.

In our startup, we need to start with the prototype and use that to garner some real validated learning. Since so much depends on the interest, ease of use, and propagation of our product by word of mouth, it is integral to get out there with students as survey interest to cover our assumption of demand. Then use the prototype and customer interviews to figure out if what we are anticipating as being extremely helpful will actually come off that way. Lastly, asking customers whether or not they would “recommend” Welc’Home to a friend, is the pivoting point for our product because we can only be the catalyst spreading the word for so long. If users will tell even one person about the product we will have a positive test to our assumption that this could be sustained.

Validated learning is simple in concept difficult in execution and analysis. I am looking forward to test out the validated learning techniques that Pete mentioned in our videochat as we move forward with Welc’Home.