Survival Analysis of Customer Lifetime and Churn Prediction in the Telecom Industry

Abstract: Customer churn poses a significant concern for the telecom industry, as it directly affects both revenue generation and the efficiency of operations. To better understand and address this issue, the present analysis applies survival analysis methods to study customer tenure and the likelihood of churn. Specifically, the Kaplan-Meier estimator is utilized to estimate the survival function of telecom customers over time, while the Cox Proportional Hazards model is used to assess the influence of various customer attributes on the risk of churn. The study highlights that several customer-related factors play a crucial role in determining the probability of churn. Among these, the type of contract (e.g., month-to-month vs. long-term), mode of payment (e.g., electronic check, credit card), and access to additional services (like internet or tech support) emerged as statistically significant determinants. For instance, customers on short-term contracts or using certain payment methods exhibited higher churn probabilities compared to those with long-term commitments or bundled services.

The findings emphasize the importance for telecom companies to tailor their retention strategies by focusing on at-risk customer segments. By understanding the survival patterns and the variables most strongly associated with early churn, service providers can design targeted interventions—such as loyalty programs, contract incentives, or personalized communication—to extend customer relationships and improve overall Customer Lifetime Value (CLV). Ultimately, this evidence-based approach can support telecom firms in minimizing customer loss and maintaining long-term profitability.

Survival Analysis, Customer Churn, Kaplan-Meier Estimator, Cox Proportional Hazards Model, Retention Strategies

1. Kleinbaum, D. G., & Klein, M. (2012). Survival analysis: A self-learning text (3rd ed.). Springer. [Google Scholar] [Crossref]

2. Cleves, M. A., Gould, W. W., Gutierrez, R. G., & Marchenko, Y. V. (2010). An introduction to survival analysis using Stata (3rd ed.). Stata Press. [Google Scholar] [Crossref]

3. Song, L., & Xu, D. (2016). The impact of customer satisfaction on customer loyalty in the telecommunications industry: A comparison of rural and urban areas. International Journal of Services and Operations Management, 24(1), 1–22. https://doi.org/10.1504/IJSOM.2016.076285 [Google Scholar] [Crossref]

4. Mishra, A. (2021). Why customer lifetime value matters for your business. Harvard Business Review. Retrieved from https://hbr.org [Google Scholar] [Crossref]

5. McKinsey & Company. (2021). The future of customer loyalty: A new strategy for a new era. Retrieved from https://www.mckinsey.com [Google Scholar] [Crossref]

6. Telco Customer Churn Dataset. (n.d.). Kaggle. Retrieved from https://www.kaggle.com/blastchar/telco-customer-churn [Google Scholar] [Crossref]

7. Hosmer, D. W., Lemeshow, S., & May, S. (2008). Applied survival analysis: Regression modeling of time-to-event data (2nd ed.). Wiley-Interscience. [Google Scholar] [Crossref]

8. Coussement, K., & Van den Poel, D. (2008). Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques. Expert Systems with Applications, 34(1), 313–327. https://doi.org/10.1016/j.eswa.2006.09.038 [Google Scholar] [Crossref]

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