A churn prediction dataset from the telecom sector: a new benchmark for uplift modeling
This provides a resource for researchers and practitioners in telecom and causal inference to test uplift models, though it is incremental as it adds a dataset rather than a new method.
The paper introduces a new benchmark dataset for uplift modeling focused on churn prediction from a telecom company in Belgium, addressing the lack of public datasets for evaluating causal impact in this domain.
Uplift modeling, also known as individual treatment effect (ITE) estimation, is an important approach for data-driven decision making that aims to identify the causal impact of an intervention on individuals. This paper introduces a new benchmark dataset for uplift modeling focused on churn prediction, coming from a telecom company in Belgium, Orange Belgium. Churn, in this context, refers to customers terminating their subscription to the telecom service. This is the first publicly available dataset offering the possibility to evaluate the efficiency of uplift modeling on the churn prediction problem. Moreover, its unique characteristics make it more challenging than the few other public uplift datasets.