Refining subscription pricing: a framework for benchmarked pricing strategies for B2C subscription apps

Abstract

This thesis will examine how early-stage B2C mobile application startups/companies can evolve from preliminary pricing tests to a robust, strategically proven pricing model. It will develop a conceptual framework that leverages existing data, such as historical churn, CPA (cost per acquisition), LTV (lifetime value), and market benchmarks, to fill the data gaps, to refine subscription pricing strategies specifically for each company. By integrating competitor analysis, internal performance metrics, and structured A/B testing, the framework will act as a guide in establishing the pricing that resonates the most with target personas. A case study will illustrate the practical application of this approach. Analysis of the practical application will be conducted with an outcome of clear recommendations and suggestions for further research.

Research questions: - How can pricing be modelled to optimise for key metrics in B2C early-stage subscription apps, and how should startups select these key metrics based on their context? - How do marketing channels and localisation affect early pricing experiments? - How can competitor benchmarking and internal historical data refine initial pricing into a robust model?