A Recommendation of Right Product to Right Customer, Next Best Offer: A Comparative Study

Telecom companies offer a variety of services and products to stay relevant in the competing market. However, with the vast bouquet of products/services, it becomes difficult for the customer to choose the best-fit product. The current research analyzed two recommendation frameworks i.e., An Adaptive Cognizance (AC) distance based algorithms (i.e. Cosine, Euclidean and Manhattan) and Bayesian Network (BN). Various experiments were carried out to evaluate the performance of AC and BN by comparing them with the existing recommendation system by the Telecom service provider (ES). Both AC and BN could uplift the revenue and conversion in the short term by more than 15%. Interestingly, top 10 impacting recharge products were found to be low cost and, with less validity, promising the rise in long-term revenue owing to increased daily-engagement with the customers by providing next best offers to the telecom prepaid customer.