The purpose of this paper is to explore the role of WeChat mobile-payment (m-payment)-based smart technologies in improving the retail customer experience and to develop an integrated framework of the smart retail customer experience including antecedents, consequences, and moderators. Based on the stimulus-organism-response (SOR) paradigm, we investigated the relationships among socio-technical stimuli, the smart retail customer experience, and relationship quality. We also developed hypotheses regarding the moderating role of customer lifetime value (CLV), which is considered an important customer characteristic. The proposed framework was empirically tested based on transaction and survey data of 462 WeChat m-payment retail customers. The results showed the following. (1) WeChat m-payment-based smart retail technology can enhance the customer experience by improving customer-perceived relationship orientation, employee-customer interaction, and communication effectiveness. (2) CLV has a positive moderating effect on the relationship between socio-technical stimuli and the customer experience. (3) The customer experience has a positive influence on relationship quality in the retail industry. Retail managers should make full use of smart retail technologies to improve the customer experience. In addition, they should emphasize the increase in CLV, as this increase enhances the positive relationship between socio-technical stimuli and the customer experience, making customer experience management more efficient.
Customers are important intangible assets of firms. Customer equity (CE) and customer equity sustainability ratio (CESR) cannot only provide a crucial basis for measuring the growth potential of firms but also provide managers a reference standard to allocate the marketing resource. This empirical study discussed the CE measurement of a mobile payments aggregator. With the rapid development of mobile payment in China, it is very meaningful to calculate the CE of these aggregators as an emerging business pattern because calculating CE cannot only help the mobile payments aggregator evaluate its future business development but also help it to provide value-added services and generate service fee from its clients, i.e., the retailers. The main purpose of this paper is to calculate CE of a mobile payments aggregator generated from a specific retailer from the perspective of technology diffusion. Based on the Bass model and Rogers’ theory of innovation diffusion, we calculated CE and CESR for five segments, namely innovators, early adopters, early majorities, late majorities, and laggards. The results show that it is the early adopters and the early majorities who generate most of the profit and it is also these two segments that have the greatest growth potential in the future.
As a fundamental concept of customer relationship management, customer lifetime value (CLV) serves as a crucial metric to identify profitable retail customers. Various methods are available to predict CLV in different contexts. With the development of consumer big data, modern statistics and machine learning algorithms have been gradually adopted in CLV modeling. We introduce two machine learning algorithms—the gradient boosting decision tree (GBDT) and the random forest (RF)—in retail customer CLV modeling and compare their predictive performance with two classical models—the Pareto/NBD (HB) and the Pareto/GGG. To ensure CLV prediction and customer identification robustness, we combined the predictions of the four models to determine which customers are the most—or least—profitable. Using 43 weeks of customer transaction data from a large retailer in China, we predicted customer value in the future 20 weeks. The results show that the predictive performance of GBDT and RF is generally better than that of the Pareto/NBD (HB) and Pareto/GGG models. Because the predictions are not entirely consistent, we combine them to identify profitable and unprofitable customers.
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