Revisiting Customer Value by Forecasting E-Service Usage

Abstract

In the past, firms focused on stimulating consumption and acquiring customers while neglecting the significance of customer retention. This study delaminated customer value based on previous literature and blended the evolution of the CRM concept to build a customer value model. This work applied a Markov Chain and Bayesian theorem to forecast and recommend appropriate CRM e-services to customers, using Apple iTunes as a case study. Findings revealed that precision was the highest for 52 samples of customer behavior. The precision of a typical student or addicted worker can reach 70.6%, 60.4% for an addicted student, and 50% for a typical worker. The performance of Bayesian theorem is insignificantly influenced by customer type and sample. The adequacy was as high as 90%. The proposed framework not only revisits the value on needs, but also helps firms recommend appropriate services.

• E-Service
• CRM
• Markov Chain
• Bayesian Approach