Clairvoyant: AdaBoost with Cost-Enabled Cost-Sensitive Classifier for Customer Churn Prediction

Area of Science:

• Machine Learning
• Data Science
• Telecommunications

Background:

• Customer churn is a significant challenge in the telecommunications industry, impacting profitability due to high customer acquisition costs.
• Existing classification methods often fail to adequately address the cost implications of misclassifications in churn prediction.

Purpose of the Study:

• To propose and evaluate a novel class-dependent cost-sensitive boosting algorithm, AdaBoostWithCost, for improved customer churn prediction.
• To reduce the overall cost associated with customer churn by minimizing specific types of misclassification errors.

Main Methods:

• Development of a novel cost-sensitive boosting algorithm, AdaBoostWithCost, incorporating class-dependent costs.
• Empirical evaluation and comparison of AdaBoostWithCost against the discrete AdaBoost algorithm using telecom churn data.

Main Results:

• The proposed AdaBoostWithCost algorithm demonstrated superior performance compared to the discrete AdaBoost algorithm in telecom churn prediction.
• AdaBoostWithCost significantly reduced false-negative errors and overall misclassification costs, indicating better cost-sensitive prediction.

Conclusions:

• AdaBoostWithCost offers an effective solution for telecommunication companies to manage customer churn by focusing on cost reduction.
• The algorithm provides a more practical approach to churn prediction by prioritizing the minimization of high-cost errors over simple accuracy improvements.