Abstract
Customer retention is nowadays a challenge that requires concrete and personalized actions. Traditional data mining studies focused on predictive analytics, neglecting the business domain. This work aims to present an actionable knowledge discovery based on specific, actionable attributes and measuring of their effects. It is common to use matching, and propensity score approaches in healthcare to evaluate causality. After performing matching using the actionable attributes in this analysis, the causal effect is quantified. This work concludes that the difference between having a yearly contract versus having a monthly contract affects the churn of around 34%.
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Pinheiro, P., Cavique, L. (2022). Telco Customer Churn Analysis: Measuring the Effect of Different Contracts. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F. (eds) Information Systems and Technologies. WorldCIST 2022. Lecture Notes in Networks and Systems, vol 469. Springer, Cham. https://doi.org/10.1007/978-3-031-04819-7_12
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