پیوند دادن سبک زندگی به ارزش طول عمر مشتری
Abstract
1. Introduction
2. Theoretical background
3. Research framework
4. Methodology
5. Data and lifestyle measurements
6. Results
7. Discussion
8. Conclusion
Acknowledgement
Appendix A. Supplementary data
References
Abstract
This study explores how lifestyle can explain the heterogeneous customer lifetime values (CLVs) among various market segments. We develop a latent class model of purchase frequency, lifetime duration, and purchase amount to infer segment-level CLV. Customers’ membership to each segment is presumed to depend on their lifestyle patterns. The proposed model is then applied to the transaction and lifestyle data of customers in an online fashion retail market. The empirical analysis reveals four customer segments that each has a unique lifestyle pattern: Individualistic Innovators, Rational Followers, Self-actualized Experts, and Integrated Shoppers. These segments differ in their magnitude of average CLV, partially explainable by segment members’ lifestyle characteristics. The paper finally discusses some implications for improving customer relationships and raising revenues.
Introduction
Customer lifetime value (CLV) is a core metric in customer relationship management. It can be useful to improve market segmentation and resource allocation, evaluate competitor firms, customize marketing communication, optimize the timing of product offerings, and determine a firm’s market value (Gupta, Lehmann, & Stuart, 2004; Kumar, Lemon, & Parasuraman, 2006; Kumar, Ramani, & Bohling, 2004). Given the critical role of CLV, numerous studies aim to elucidate its drivers, which are broadly divisible into organizational and customer antecedents (Kumar et al., 2006). The latter have gained empirical generalization; Blattberg, Malthouse, and Neslin (2009) point out that customer satisfaction, cross-buying, and multichannel purchasing can increase CLV through their direct impacts on customer purchase frequency, spending, and retention. However, despite the large body of research on this topic, studies of how lifestyle influences CLV are still scarce, primarily because data encompassing both the purchase history and the lifestyle of the same customers are not readily available. We argue that understanding the influence of lifestyle on CLV should be critical for marketers because it can explain customers’ motivation to engage in certain behaviors (Plummer, 1974) and therefore clarify why some customers are profitable, whereas others are not.