Indiana University Northwest

Permanent link for this communityhttps://hdl.handle.net/2022/18436

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Browsing Indiana University Northwest by Author "Cheng, Dong"

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    Measuring Customer Equity in Noncontractual Settings Using a Diffusion Model: An Empirical Study of Mobile Payments
    (Journal of Theoretical and Applied Electronic Commerce Research, 2020) Wei, Xue; Sun, Yinglu; Bandyopadhyay, Subir; Cheng, Dong
    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.
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    Profitable Retail Customer Identification Based on a Combined Prediction Strategy of Customer Lifetime Value
    (Midwest Social Sciences Journal, 2021) Wei, Xue; Sun, Yinglu; Bandyopadhyay, Subir; Cheng, Dong
    As a fundamental concept of customer relationship management (CRM), 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's robustness, we combined the predictions of the four aforementioned 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 predict customer value in the future 20 weeks. The results show that GBDT and RF's predictive performance is generally better than that of the Pareto/NBD (HB) and Pareto/GGG models. Since the predictions are not entirely consistent, we combine them to identify the profitable and unprofitable customers