Get rich or die trying… finding revenue model fit using machine learning and multiple cases

Abstract

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-performingrevenue model-activity system configurations. Our core insight is thefitbetween 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. Revenue models are critical for product performance. Yet, the high-performing choice is often unclear. We combine machine learning with multiple-case deep-dives to unpack optimal revenue model choice for a wide range of products on the App Store, a significant setting in the digital economy. Our primary insight is that high-performing productsfitvalue capture (revenue models) and value creation (activity systems) to form coherent business models. Contrastingly, low-performing products avoid complex value capture (i.e., freemium) and misunderstand value creation (e.g., overweight effort and price). We also identify the importance ofuser resources,marketing,offline brand, andproduct complexityfor specific revenue models. Overall, we contribute a framework for the optimal choice of revenue model and spotlight the revenue model-activity system configurations of successful business models.