Early warning of telecom enterprise customer churn based on ensemble learning

Area of Science:

• Machine Learning
• Data Science
• Telecommunications Analytics

Background:

• Customer churn poses significant operational costs for telecom companies.
• Predictive analytics can enable targeted marketing and personalized services to mitigate churn.
• Existing machine learning models require evaluation for optimal customer retention strategies.

Purpose of the Study:

• To develop and evaluate advanced machine learning models for early detection of customer churn.
• To compare the performance of ensemble learning models against classical machine learning algorithms.
• To identify the most effective model for predicting customer churn in the telecommunications sector.

Main Methods:

• Preprocessing of a large telecom customer dataset (900,000 records) using Python, including data cleaning, oversampling, and standardization.
• Implementation and comparison of Back Propagation Neural Network (BPNN), Random Forest (RF), and Adaboost models.
• Development of a novel RF-Adaboost dual-ensemble model with RF as the base learner.
• Evaluation using metrics such as recall rate, precision rate, and F1 score.

Main Results:

• The RF-Adaboost dual-ensemble model achieved the highest performance across all evaluated metrics.
• Recall rates for BPNN, RF, Adaboost, and RF-Adaboost were 79%, 90%, 89%, and 93% respectively.
• Precision rates were 97%, 99%, 98%, and 99%, with F1 scores of 87%, 95%, 94%, and 96% respectively.
• The RF-Adaboost model showed improvements of 10% in recall, 1% in precision, and 6% in F1 score compared to the reference.

Conclusions:

• The RF-Adaboost dual-ensemble model demonstrates superior efficacy in predicting customer churn.
• Accurate churn prediction provides crucial data for implementing effective customer retention strategies.
• This approach enables telecom companies to proactively reduce customer attrition and associated costs.