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Customer Lifetime Value — The Path to Profitability

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  • Kumar, V.

Abstract

This survey talks about Customer Lifetime Value (CLV) as a metric that would help managers make informed usiness decisions. While there have been prior articles that take an extensive and in-depth look at Customer Equity (CE)(Villanueva and Hanssens, 2007), this survey reviews the CLV metric in particular. The definition and approaches to compute CLV and the concept of customer equity are discussed in detail. Specifically, this survey provides methods for measuring CLV, the strategies for developing customer-centric strategies, the implementation of CLV strategies in a B2B and B2C setting, and the challenges faced by an organization in implementing a CLV-based framework. This survey details the importance of CLV as a metric in a marketer's toolkit and how it is relevant to managing customers.

Suggested Citation

  • Kumar, V., 2008. "Customer Lifetime Value — The Path to Profitability," Foundations and Trends(R) in Marketing, now publishers, vol. 2(1), pages 1-96, August.
    • Handle: RePEc:now:fntmkt:1700000004
    DOI: 10.1561/1700000004
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    Cited by:

    1. Cherkashin, Alexander & Sakhadzhi, Vladislav & Guliev, Ruslan & Bolshunova, Elena, 2024. "Practical Methods for Predicting Customer Retention," MPRA Paper 122400, University Library of Munich, Germany.
    2. Kumar, V. & Pozza, Ilaria Dalla & Petersen, J. Andrew & Shah, Denish, 2009. "Reversing the Logic: The Path to Profitability through Relationship Marketing," Journal of Interactive Marketing, Elsevier, vol. 23(2), pages 147-156.
    3. Yao Zhang & Eric T. Bradlow & Dylan S. Small, 2015. "Predicting Customer Value Using Clumpiness: From RFM to RFMC," Marketing Science, INFORMS, vol. 34(2), pages 195-208, March.
    4. Markovitch, Dmitri G. & Huang, Dongling & Ye, Pengfei, 2020. "Marketing intensity and firm performance: Contrasting the insights based on actual marketing expenditure and its SG&A proxy," Journal of Business Research, Elsevier, vol. 118(C), pages 223-239.
    5. Petersen, J. Andrew & McAlister, Leigh & Reibstein, David J. & Winer, Russell S. & Kumar, V. & Atkinson, Geoff, 2009. "Choosing the Right Metrics to Maximize Profitability and Shareholder Value," Journal of Retailing, Elsevier, vol. 85(1), pages 95-111.
    6. Sridhar N. Ramaswami & S. Arunachalam, 2016. "Divided attitudinal loyalty and customer value: role of dealers in an indirect channel," Journal of the Academy of Marketing Science, Springer, vol. 44(6), pages 770-790, November.
    7. Rocío G. Martínez & Ramon A. Carrasco & Cristina Sanchez-Figueroa & Diana Gavilan, 2021. "An RFM Model Customizable to Product Catalogues and Marketing Criteria Using Fuzzy Linguistic Models: Case Study of a Retail Business," Mathematics, MDPI, vol. 9(16), pages 1-31, August.
    8. Sreedhar Madhavaram & Andrew C. Gross & Radha Appan, 2014. "Knowledge needs of firms: the know-x framework for marketing strategy," AMS Review, Springer;Academy of Marketing Science, vol. 4(3), pages 63-77, December.
    9. Черкашин, Александр & Сахаджи, Владислав & Гулиев, Руслан & Большунова, Елена, 2024. "Практические Методы Прогнозирования Сохранения Клиентской Базы (Перевод На Русский Язык) [Practical Methods for Predicting Customer Retention]," MPRA Paper 122483, University Library of Munich, Germany.
    10. Gupta, Shaphali & Ramachandran, Divya, 2021. "Emerging Market Retail: Transitioning from a Product-Centric to a Customer-Centric Approach," Journal of Retailing, Elsevier, vol. 97(4), pages 597-620.
    11. Grewal, Dhruv & Levy, Michael & Kumar, V., 2009. "Customer Experience Management in Retailing: An Organizing Framework," Journal of Retailing, Elsevier, vol. 85(1), pages 1-14.
    12. Michael Platzer & Thomas Reutterer, 2016. "Ticking Away the Moments: Timing Regularity Helps to Better Predict Customer Activity," Marketing Science, INFORMS, vol. 35(5), pages 779-799, September.

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