Customer segment transition through the customer loyalty program

Purpose: This study presents the applicability of a model-based approach for loyalty program forecasting using smartphone app in the digital strategy of the retail industry. Design/methodology/approach: The authors develop a dynamic model with the cyclical structure of customer segments through customer experience. They use time-series data on the number of members of the loyalty program, “Seven Mile Program” and confirm the validity of the approximate calculation of customer segment share, customer segment sales share and aggregate sales performance. The authors present three medium-term forecast scenarios after the launch of a smartphone payment service linked with the loyalty program. Findings: The sum of the two customer segment shares for forecasting (the sum of the quasi-excellent and excellent customer ratios) is about 30% in each scenario, consistent with an essential customer loyalty (true loyalty) share obtained in the existing empirical study. Research limitations/implications: Digital strategy in the retail industry should focus more on estimating and forecasting average amounts of customer segments and the number of aggregated customers through the digitalization on the customer side than on individual customer journeys and responses. Practical implications: Multi-scenario evaluation through simulation of dynamic models from a systemic view can be used for decision-making in retailing digital strategies. Originality/value: This study builds a model that integrates the cyclicality of customer segment transition through customer experiences into a loyalty matrix framework, which is a method that has previously been used in the hospitality industry.