Leveraging Predictive Analytics for Improving Client Retention in Service-Based Industries

  • Authors

    • Prof. Chinedu Okafor Dowen College Lagos, Nigeria. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-2018PII6B2F

    Published 10-05-2018

  • Client Retention, Predictive Analytics, Service-Based Industries, Customer Churn Prediction, Customer Segmentation, Machine Learning, Customer Lifetime Value, Data-Driven Insights, Customer Satisfaction, Business Analytics

    Issue

    Vol. 1 No. 2 (2018)

    Section

    Articles

    How to Cite

    [1]
    C. Okafor, “Leveraging Predictive Analytics for Improving Client Retention in Service-Based Industries”, IJADSMC, vol. 1, no. 2, pp. 01–16, Oct. 2018, doi: 10.67228/30713498/IJADSMC-2018PII6B2F.

  • Abstract

    Client retention is a critical factor for success in service-based industries, yet many businesses struggle with maintaining a loyal customer base. This paper explores how predictive analytics, utilizing advanced machine learning models and data-driven insights, can significantly enhance client retention strategies. By analyzing historical client data, businesses can identify patterns and early warning signs of client churn, allowing them to take proactive measures to improve customer satisfaction and loyalty. This paper discusses various predictive analytics techniques, including churn prediction, customer segmentation, and lifetime value modeling, and explores their practical applications across industries such as healthcare, hospitality, and telecommunications. Additionally, the challenges and ethical considerations of using predictive analytics in client retention are highlighted. The paper concludes with insights into future trends and best practices for businesses aiming to harness the power of predictive analytics to improve their client retention efforts.

  • References

    [1] Burez, J., & Van den Poel, D. (2009). Handling class imbalance in customer churn prediction. Expert Systems with Applications, 36(3), 4626–4636.

    [2] Chen, M., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.

    [3] 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.

    [4] Hadden, J., Tiwari, A., Roy, R., & Ruta, D. (2007). Computer assisted customer churn management: State-of-the-art and future trends. Computers & Operations Research, 34(10), 2902–2917.

    [5] Larivière, B., & Van den Poel, D. (2004). Investigating the role of product features in preventing customer churn. Journal of Service Research, 6(2), 146–160.

    [6] Ngai, E. W. T., Xiu, L., & Chau, D. C. K. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications, 36(2), 2592–2602.

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

    [8] Verbeke, W., Dejaeger, K., Martens, D., Hur, J., & Baesens, B. (2012). New insights into churn prediction in the telecommunication sector. European Journal of Operational Research, 218(1), 211–229.

    [9] Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical Machine Learning Tools and Techniques (4th ed.). Morgan Kaufmann.

    [10] Zhang, G., Hu, M. Y., Patuwo, B. E., & Indro, D. C. (1999). Artificial neural networks in business forecasting. European Journal of Operational Research, 114(1), 28–46.

  • Downloads