The Role of Customer Lifetime Value (CLV) Models in Client Engagement Strategies

  • Authors

    • Dr. Valentine University of Chile, Chile Author
    • Ruban Riguel Roger University of Chile, Chile Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-2019PI3C8Z

    Published 11-04-2019

  • Customer Lifetime Value (CLV), Client Engagement Strategies, Customer Retention, Customer Segmentation, Predictive Modeling, Personalized Marketing, Customer Acquisition Costs, CRM (Customer Relationship Management), AI And Machine Learning In CLV, Business Strategy

    Issue

    Vol. 2 No. 2 (2019)

    Section

    Articles

    How to Cite

    [1]
    V. Dr. and R. R. Roger, “The Role of Customer Lifetime Value (CLV) Models in Client Engagement Strategies”, IJADSMC, vol. 2, no. 2, pp. 01–14, Nov. 2019, doi: 10.67228/30713498/IJADSMC-2019PI3C8Z.

  • Abstract

    Customer Lifetime Value (CLV) is a key metric used by businesses to predict and optimize the long-term value of their customer relationships. CLV models are critical tools in shaping client engagement strategies, as they provide insights into customer behavior, segmentation, and future value. This paper explores the role of CLV models in client engagement strategies, highlighting their impact on customer acquisition, retention, and personalized marketing. By examining various CLV models and their application across different industries, the paper identifies best practices and challenges associated with their implementation. The paper also looks at the future of CLV models, including emerging trends such as AI and machine learning, and their potential to revolutionize customer engagement. Ultimately, this research emphasizes the significance of CLV models in fostering long-term customer relationships and driving business success.

  • References

    [1] Gupta, S., & Lehmann, D. R. (2003). Customers as assets. Journal of Interactive Marketing, 17(1), 9–24.

    [2] Fader, P. S., Hardie, B. G. S., & Lee, K. L. (2005). RFM and CLV: Using iso-value curves for customer base analysis. Journal of Marketing Research, 42(4), 415–430.

    [3] Kumar, V., & Reinartz, W. (2016). Creating enduring customer value. Journal of Marketing, 80(6), 36–68.

    [4] Rust, R. T., Lemon, K. N., & Zeithaml, V. A. (2004). Return on marketing: Using customer equity to focus marketing strategy. Journal of Marketing, 68(1), 109–127.

    [5] Payne, A., & Frow, P. (2005). A strategic framework for customer relationship management. Journal of Marketing, 69(4), 167–176.

    [6] Berger, P. D., & Nasr, N. I. (1998). Customer lifetime value: Marketing models and applications. Journal of Interactive Marketing, 12(1), 17–30.

    [7] Blattberg, R. C., Malthouse, E. C., & Neslin, S. A. (2009). Customer lifetime value: Empirical generalizations and some conceptual questions. Journal of Interactive Marketing, 23(2), 157–168.

    [8] Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97–121.

    [9] McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68

  • Downloads