Model for Predicting Customer Desertion of Telephony Service using Machine Learning

In the present study, it is observed that many people are affected by the services provided by telephony, who leave the service for different reasons, for which the use of a model based on decision trees is proposed, which allows predicting potential dropouts from Customers of a telecommunications company for telephone service. To verify the results, several algorithms were used such as neural networks, support vector machine and decision trees, for the design of the predictive models the KNIME software was used, and the quality was evaluated as the percentage of correct answers in the predicted variable. The results of the model will allow acting proactively in the retention of clients and improves the services provided. A data set with 21 predictor variables that influence customer churn was used. A dependent variable (churn) was used, which is an identifier that determines if the customer left = 1, did not leave = 0 the company's service. The results with a test data set reach a precision of 91.7%, which indicates that decision trees turn out to be an attractive alternative to develop prediction models of customer attrition in this type of data, due to the simplicity of interpretation of the results.

@article{Acero-Charaña2021,
  title     = {Model for Predicting Customer Desertion of Telephony Service using Machine Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {3},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Carlos Acero-Charaña and Erbert Osco-Mamani and Tito Ale-Nieto},
  doi       = {10.14569/IJACSA.2021.0120320},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120320}
}