The Lean Startup is a startup framework presented by Eric Ries in 2011. The book defines a process that can be summarized into a feedback loop of build-measure-learn. During the “build” phase, entrepreneurs formulate hypotheses (either of value or growth), often by introducing an MVP (minimum viable product) – a simplified and basic version of what they need to test. During the “measure” phase, they gather data in order to validate their hypotheses, and, lastly, during the “learning” phase, they are able to determine if they should pivot or preserve – they decide if that particular initial hypothesis is beneficial for the business and should be implemented or should not be implemented (thus restarting the build-measure-learn loop from a new hypothesis that applies a fundamental change in strategy).
This approach is very similar to the main focus of growth hacking, which postulates on its core to gather feedback and collect valuable insights for building a product. A growth hacker defines actionable and specific goals using data-driven moves through low-cost channels.
I need to admit though that as easy as it sounds – both approaches simply require that you don’t pretend to know everything, and instead make a small assertion and collect as much data as you can in order to verify it – in my experience working as growth consultant, I have come to see many business owners and marketers struggling with its implementation.
I have listed below some of the most common issues I have seen integrating a lean startup approach.
Testing the wrong things in the wrong order
David Collis, a professor at Harvard Business School, complains about the lack of linearity and clear rules with regards to The Lean Startup approach and warns about the fact that this might be producing “false negatives;” thus, good ideas are rejected by error, due to methodological issues. His solution is the adoption of a Lean Strategy rather than Lean Process: “Strategy is seen as the pursuit of a clearly defined path — one systematically identified in advance — through a carefully chosen set of activities. Entrepreneurship is seen as the epitome of opportunism…strategy without entrepreneurship is central planning. Entrepreneurship without strategy leads to chaos.” Every experiment starts with a hypothesis, defined as “a prediction for what you think will happen;” however, without a clear, defined starting and ending point, very few entrepreneurs correctly formulate their hypotheses and know when to validate their learnings. That lack of a clear strategy is particularly true in regards to non-startup companies jumping into this methodology. For them, the lack of clear definitions makes coordinating organizational roles more difficult and often results in companies withdrawing the method application or moving this to a small, dedicated department rather than applying it to the whole company.
Testing an idea means testing a product or a particular feature meaning testing a solution. I’m impressed to hear on the Internet and during confrontations with clients how they think the product or service is valid if this solves a problem. What is missing is realizing that every product or service solves a problem, and the real question is not “does my product solve a problem?” but instead, “does this solve a problem with a market need?”. Indeed, as it will be deeply analyzed in The Growth Patterns chapter, focusing on success and failure patterns, the number one reason why startups fail is because they are focused on solving a problem that doesn’t serve a market need. Even by applying a lean process of build-measure-learn, without clarity about where to start and where to end, you will be producing lots of waste in running experiments, but it was all wrong from the beginning! The lack of a clear starting point for many leads confusion. “Too much feedback from customers might cause the entrepreneurs to change the idea so frequently that they become disheartened.” People are pirouetting, not pivoting.
The P.I.B. framework described here removes this problem by proposing a step-by-step, linear approach where hypotheses start from the customer-problem level.