From free to fee for digital content through utility-based business analytics

Abstract

This study proposes a novel framework for designing business analytics to assist digital content providers in effectively converting free-only users (FOUs) into fee-paying cus-tomers. To achieve the objective of the seller’s profit maximization, the traditional frequency-driven rule analytics was expanded by integrating three business-relevant factors (potential demand, conversion profit, and conversion likelihood) into the process of generating recom-mendations for FOUs in digital content markets. The framework was tested using two differ-ent types of empirical analyses. An analysis of data regarding over 300,000 transactions col-lected from an e-book seller was carried out to examine the effectiveness of the proposed ap-proach. In addition, a field experiment with real data from a national e-book store was con-ducted to determine how FOUs responded to the recommendations generated by the frame-work. The results from the analyses indicate that the proposed framework can benefit sellers both on and after free-to-paid conversion. In addition, some implications were obtained re-garding the factors proposed in this study. The findings suggest that utility-based business analytics can significantly enhance the business performance of digital content providers.