A Theoretical Analysis of the Lean Startup Method

Abstract

The lean startup method (LSM) advocates an iterative and adaptive product development and testing approach to innovation. It recommends firms to build test products, use them to learn about consumer preferences, and modify (or `pivot') the product design accordingly. It is less straightforward to understand how effective LSM can be however, not least because consumers' responses to the test product are influenced by its quality, price, and design -- that is, learning is endogenous to the features of the test product. This paper analyzes the build-test-learn cycle of LSM using an analytical model to understand its micro-foundation and how best to implement it. We find that an optimal test product that maximizes learning should aim either to confirm a more likely product design or to rule out a less likely product design as being the most desired by consumers; have a vertical quality that is neither too high nor too low; and have a higher quality when aiming to confirm than to rule out. We also identify the product--market conditions for which the LSM is more effective. Conceptualizing the LSM via a formal model may help to improve its implementation in practice and to advance further academic research.

Keywords: Lean Startup method, entrepreneurship, customer discovery, new product development, Bayesian learning