In-App Personalization as a Customer Retention Mechanism: A Qualitative Case Study of Spotify

Abstract

In highly competitive digital subscription markets, personalization has emerged as a central strategy for sustaining user engagement and reducing churn. This study examines how Spotify deploys in-app personalization as a customer retention mechanism. Applying a qualitative, theory-driven single-case study design grounded in secondary data, the analysis compares Spotify's observable personalization features with established frameworks from marketing, information systems, and cognitive psychology. The findings show that algorithmically generated playlists, adaptive recommendation systems, and reflective formats such as Spotify Wrapped form a multi-touchpoint strategy that aligns closely with engagement-based retention theory. Personalized content supports habit formation, reduces information overload, and fosters emotional brand attachment. At the same time, the study identifies critical limitations, including risks of hyper-personalization, user fatigue, and data privacy concerns that may erode long-term trust. The paper contributes a structured qualitative analysis of personalization as a retention mechanism in a subscription context and proposes avenues for future primary research and cross-platform comparison.