Keywords
Customer Attrition, Customer Churn, ROC, AUC
Abstract
Customer churn prediction has become one of the crucial steps for customer retention. Telecommunication companies rely on loyal customers to make their proft. It is often very easy for customers to switch from one service provider to the other. To prevent or reduce the rate of customer attrition, there needs to be a model that can identify customers who are at risk of churning in the future in advance. Previous literature has shown that predictive models are efective in predicting customer churn. In this work, four tentative machine-learning models are built using data obtained from Kaggle on telecommunication customer attrition (data link). SAS Studio and SAS Enterprise Miner software are used in all our model-building processes. After appropriate model comparison tests, the neural network model emerged as the best among the decision tree, logistic regression, and gradient boosting models. The fnal model is then scored using the validation data set yielding an accuracy rate of 81.09%.
Semester
2024
College
College of Sciences
STARS Citation
Kongyir, Benedict; Agbemade, Emil; and Njuki, Kelvin, "Predicting Telecommunication Customer Attrition Using the Hopfeld Neural Network Model." (2024). Data Science and Data Mining. 26.
https://stars.library.ucf.edu/data-science-mining/26
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