Markov Chain Modeling OF Customer Journeys: A Telco Churn Case Study
Abstract
Customer churn remains one of the most critical challenges for service providers, particularly in competitive sectors such as telecommunications. This paper develops a Markov chain framework to model customer journeys and analyze churn behavior using the Telco Customer Churn dataset. Each stage of the customer lifecycle, active, complaint, resolved, loyalty, and churn, is represented as a state in the chain, with transition probabilities estimated from observed data and industry knowledge. We construct the transition matrix, compute multi-step probabilities, and analyze steady-state distributions and absorbing states to estimate churn likelihood and expected customer lifetime. Results illustrate that unresolved complaints strongly increase churn probability, while improvements in resolution rates substantially extend retention. The study highlights how probabilistic modeling can guide strategic decisions on complaint handling, service quality, and loyalty programs. Beyond telecommunications, the Markov chain approach provides a generalizable method for modeling customer experience in diverse industries.