Stanford University


Get Rich or Die Trying: Finding Revenue Model Fit Using Machine Learning and Multiple Cases

Strategic Management Journal
March 2020

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While revenue models are strategically important, research is incomplete. Thus, we ask: “What is the optimal choice of revenue model?” Using a novel theory-building method combining machine learning and multi-case theory building, we unpack optimal revenue model choice for a wide range of products on the App Store. Our primary theoretical contribution is a framework of high-performing revenue model-activity system configurations. Our core insight is the fit between value capture (revenue models) and value creation (activities) at the heart of successful business models. Contrastingly, low-performing products avoid complex value capture (i.e., freemium) and misunderstand value creation (e.g., overweight effort). Overall, we contribute a theoretically accurate and empirically grounded view of successful business models using a pioneering method for theory building using large, quantitative data sets.

Stanford University