Abstract
PurposeThis 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/approachThe 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.FindingsThe 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/implicationsDigital 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 implicationsMulti-scenario evaluation through simulation of dynamic models from a systemic view can be used for decision-making in retailing digital strategies.Originality/valueThis 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.
Highlights
The conceptualization of the customer journey during the pre-purchase, purchase and postpurchase stages has been shown based on the theory of market or business strategy (Handarkho, 2020; Lemon and Verhoef, 2016; Grewal and Roggeveen, 2020)
After setting the parameter values attained in the first step as initial values, the sales ratio data are used to optimize the average purchase amounts for each customer segment
Of the medium-term forecasts shown in Table 5, the worst-case scenario corresponds to the 7iD forecast for FY2020, presented by Seven and i Holdings
Summary
The conceptualization of the customer journey during the pre-purchase, purchase and postpurchase stages has been shown based on the theory of market or business strategy (Handarkho, 2020; Lemon and Verhoef, 2016; Grewal and Roggeveen, 2020). Using a single case analysis, we select a loyalty program (“Seven Mile Program”) launched by Seven and i Holdings (2019a), the largest retailer in Japan, in 2018 as a smartphone app for customers of the physical stores. Through the approximate calculation of the number of Seven Mile Program members and the overall business performance using the published data, we attempt to estimate the purchase amounts by customer segment and further forecast the performance as the business scenarios. The diffusion of loyalty programs by smartphone apps in the retail business may transform the customer experience in all stages, change the customer journey (de Haan et al, 2018; Handarkho, 2020) and result in profound customer engagement (Pansari and Kumar, 2017). The definition formula of new entry using the time term t is as follows: Stationary model: New entryðtÞ 1⁄4 Average enrollments
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