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Linking lifestyle to customer lifetime value: An exploratory study in an online fashion retail market

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  • Dahana, Wirawan Dony
  • Miwa, Yukihiro
  • Morisada, Makoto

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.

Suggested Citation

  • Dahana, Wirawan Dony & Miwa, Yukihiro & Morisada, Makoto, 2019. "Linking lifestyle to customer lifetime value: An exploratory study in an online fashion retail market," Journal of Business Research, Elsevier, vol. 99(C), pages 319-331.
  • Handle: RePEc:eee:jbrese:v:99:y:2019:i:c:p:319-331
    DOI: 10.1016/j.jbusres.2019.02.049
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    2. Adela-Laura POPA & Dinu Vlad SASU & Teodora Mihaela TARCZA, 2021. "Investigating The Importance Of Customer Lifetime Value In Modern Marketing - A Literature Review," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 30(2), pages 410-416, December.
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    4. Hitesh Sood & Rajendra Prasad Sharma, 2021. "Customer Digital Engagement and Lifetime Value: An Empirical Study of Telecom Services in India," FIIB Business Review, , vol. 12(4), pages 415-424, December.
    5. Zobi Khan & Yongzhong Yang & Mohsin Shafi & Ruo Yang, 2019. "Role of Social Media Marketing Activities (SMMAs) in Apparel Brands Customer Response: A Moderated Mediation Analysis," Sustainability, MDPI, vol. 11(19), pages 1-19, September.
    6. Tchai Tavor & Limor Dina Gonen & Uriel Spiegel, 2023. "Customer Segmentation as a Revenue Generator for Profit Purposes," Mathematics, MDPI, vol. 11(21), pages 1-20, October.
    7. Joon-ho Kim & Seung-hye Jung & Bong-ihn Seok & Hyun-ju Choi, 2022. "The Relationship among Four Lifestyles of Workers amid the COVID-19 Pandemic (Work–Life Balance, YOLO, Minimal Life, and Staycation) and Organizational Effectiveness: With a Focus on Four Countries," Sustainability, MDPI, vol. 14(21), pages 1-31, October.
    8. Mehrdad Memarpour & Erfan Hassannayebi & Navid Fattahi Miab & Ali Farjad, 2021. "Dynamic allocation of promotional budgets based on maximizing customer equity," Operational Research, Springer, vol. 21(4), pages 2365-2389, December.
    9. Muhammad Naeem & Wilson Ozuem, 2021. "Understanding the social consumer fashion brand engagement journey: insights about reputed fashion brands," Journal of Brand Management, Palgrave Macmillan, vol. 28(5), pages 510-525, September.
    10. Li, Xi & Dahana, Wirawan Dony & Ye, Qiongwei & Peng, Luluo & Zhou, Jiaying, 2021. "How does shopping duration evolve and influence buying behavior? The role of marketing and shopping environment," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    11. Maria Kubacka, 2020. "Review and Analysis of Selected Customer Value Measurement Methods (Przeglad i analiza wybranych metod pomiaru wartosci klienta)," Research Reports, University of Warsaw, Faculty of Management, vol. 1(32), pages 34-46.
    12. Kohsuke Matsuoka, 2020. "Exploring the interface between management accounting and marketing: a literature review of customer accounting," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 31(3), pages 157-208, September.
    13. Ng, Felicity Zi-Xuan & Yap, Hui-Yee & Tan, Garry Wei-Han & Lo, Pei-San & Ooi, Keng-Boon, 2022. "Fashion shopping on the go: A Dual-stage predictive-analytics SEM-ANN analysis on usage behaviour, experience response and cross-category usage," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
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