Keywords
Consumer Loyalty
Churn Prediction
Music Streaming
Personalization
KKBox
User Engagement
Perceived Value
How to Cite
Abstract
Purpose: This study investigates how Competitive Intelligence (CI) capabilities influence consumer retention and loyalty in digital music streaming platforms. Specifically, the research examines the effects of personalization quality, service continuity, pricing competitiveness, and behavioral engagement analytics on subscriber loyalty and churn reduction.
Methodology/approach: A quantitative cross-sectional research design was employed using the KKBox Music Streaming Churn Prediction dataset containing 970,960 anonymized subscriber records. Statistical analyses were conducted in Python 3.11 using descriptive statistics, reliability and validity analysis, Pearson correlation, OLS multiple regression, and bootstrapped mediation analysis. User engagement and perceived value were tested as mediating variables, while subscription tier was examined as a moderating factor.
Originality/Relevance: The study proposes the CI-Loyalty Framework (CILF), an original theoretical model that conceptualizes Competitive Intelligence as a multidimensional organizational capability embedded in behavioral analytics and strategic decision-making processes. Unlike prior studies based primarily on survey data, this research operationalizes CI constructs using large-scale administrative behavioral logs from a real-world streaming platform.
Key findings: The results demonstrate that the CI composite index was the strongest predictor of consumer loyalty. Personalization quality and perceived value also showed strong positive effects on retention outcomes. User engagement and perceived value partially mediated the relationship between CI capability and loyalty. Additionally, subscription payment methods significantly influenced churn behavior, with auto-debit users presenting substantially lower churn rates than voucher-payment subscribers.
Theoretical/methodological contributions: The study contributes theoretically by integrating Resource-Based View (RBV), Information Processing Theory, Customer Engagement Theory, and the Competitive Intelligence cycle into a unified framework for digital streaming environments. Methodologically, the research demonstrates the feasibility of operationalizing CI constructs through behavioral proxy indicators extracted from large-scale platform data, expanding the empirical application of Competitive Intelligence in digital platform ecosystems.
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