Abstract for:AI Adoption in Subscription Business Model
AI and machine learning are growing more important in many businesses and enhancing an additional feedback process called a data network effect. It drives 70% of business value and venture capitalists rely on data network effects to drive growth in numerous startups. This presentation focuses on analyzing which pricing strategies are most successful in businesses with strong data network effects. In these situations, small changes in prices can lead to drastic impacts on profitability.
A system dynamics model was designed to reflect a typical subscription business model with users making choices on product usage. Data breadth and depth change over time with accumulation of users, changing the utility of the product usage by refining algorithms. Our goal is to analyze the parameters that lead to a successful business strategy taking advantage of data network effects and to design optimal pricing strategies. A series of machine learning techniques were applied to analyze ~10,000 scenarios, clustering similar scenarios and optimizing pricing strategies for each cluster.
It was found that all scenarios resulted in optimal pricing strategies with dynamic usage pricing and a $0 subscription fee. The insights from this analysis are useful for designing pricing models for businesses that rely on data network effects.