Predicting Customer Complaints in Mobile Telecom Industry Using Machine Learning Algorithms

Chiyoung Choi, Purdue University


Mobile telecom industry competition has been fierce for decades, therefore increasing the importance of customer retention. Most mobile operators consider customer complaints as a key factor of customer retention. We implement machine learning algorithms to predict the customer complaints of a Korean mobile telecom company. We used four machine learning algorithms ANN (Artificial Neural Network), SVM (Support Vector Machine), KNN (K-Nearest Neighbors) and DT (Decision Tree). Our experiment utilized a database of 10,000 Korean mobile market subscribers and the variables of gender, age, device manufacturer, service quality, and complaint status. We found that ANN’s prediction performance outperformed other algorithms. We also propose the segmented-prediction model for better accuracy and practical usage. Segments of the customer group are examined by gender, age, and device manufacturer. Prediction power is better for female, older customers, and the non-iPhone groups than other segment groups. The highest accuracy s ANN’s 87.3% prediction for the 60s group.




Lee, Purdue University.

Subject Area

Information Technology|Industrial engineering|Artificial intelligence

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