A churn prediction dataset from the telecom sector: a new benchmark for
  uplift modeling
Théo Verhelst Denis Mercier Jeevan Shrestha Gianluca Bontempi
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.