@article{dumitrache2020churn,
  title = {Churn Prediction in Telecommunication Industry: Model Interpretability},
  author = {Andreea DUMITRACHE and Alexandra A. M. NASTU and Stelian STANCU},
  year = 2020,
  url = {https://ibimapublishing.com/articles/JEERBE/2020/241442/},
  journal = {Journal of Eastern Europe Research in Business and Economics},
  volume = 2020,
  pages = 11,
  doi = 10.5171/2020.241442,
  abstract = {The large number of studies published in the last ten years on the problem of customers migrating from one telecommunications service provider to another competing provider proves that this problem has become a major concern for this industry and beyond. The purpose of this paper is to detect which variables from the multitude presented in the data set for postpaid clients, represents an important driver in the problem of migrating customers to another Romanian mobile telecommunications company. To enable us to understand and solve the problem of churn in telecommunications, we need tools that can interpret the results. Thus, we use a Balanced Random Forest for the churn model and three feature selection tools: Permutation Importance, Partial Dependence Plot and SHAP. Applying them to the churn model, we classify the predictive indicators according to their importance, their predictive power and the distribution of the impact that each characteristic has in the model. According to the Permutation Importance, the drivers regarding churn issue are: the number of months since the last offer was changed from the account, the number of minutes consumed outside the company, the value of the invoice, the age of the customer and his time at this telecommunications operator. Partially Dependence Plot determinates the churn risk areas faced by the Romanian telecommunications company for each of the indicators listed, such as: clients with younger ages or with outdated offers (unchanged for almost two years). SHAP also shows that many months since the last offer, a significant percentage of minutes received from competing networks or a small age in the network, increases the estimated churn per customer.},
  keywords = {churn, feature importance, model agnostic features, churn risk area},
  note = Article ID: 241442
}