University of Twente Student Theses


Forecasting Customer Lifetime Value through Risk Prediction : An Explainable Machine Learning Approach for the Telecommunication Industry

Firmansyah, Edo Belva (2023) Forecasting Customer Lifetime Value through Risk Prediction : An Explainable Machine Learning Approach for the Telecommunication Industry.

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Embargo date:31 August 2024
Abstract:As businesses focus more on understanding Customer Lifetime Value (CLV) to guide customer strategies, a gap has been identified: limited consideration of customer risk in CLV calculations, particularly in the telecommunications sector. This gap is more apparent as this sector has yet to explore risk-adjusted CLV predictions using machine learning (ML). This thesis aims to address this gap by predicting risk-adjusted CLV in the telecommunications industry using ML techniques. The study introduces a Risk-Adjusted Return (RAR) metric to incorporate customer risk into CLV calculations. Employing Design Science Research Methodology (DSRM) and Cross-Industry Standard Process for Data Mining (CRISP-DM), the study constructs ML models. Customer risk for RAR includes churn probability and beta value, adjusting the discount rate for risk. Four RAR calculation approaches are proposed, and ML models (Logistic Regression, XGBoost, CatBoost, and Random Forest) predict churn probability and RAR. Validation employs eXplainable AI (XAI) techniques like feature importance, SHAP global explanation, SHAP local explanation, and LIME. Results reveal statistical differences in RAR approaches, validating their distinctiveness. Churn model accuracy is 85%, and RAR models exhibit strong performance (R^2=0.92, MAPE≈20%). XGBoost excels in churn prediction, while CatBoost excels in RAR prediction. Prominent features for RAR include loyalty points, average revenue, revenue variability, churn probability, and beta value. These align with traditional RAR calculations, enhancing model validity. The study concludes that proposed RAR is statistically significant, and models exhibit robustness, with feature importance aligning with traditional methods. Future research could explore different risks, dissect revenue components for individual RAR, employ advanced ML algorithms, hyperparameter tuning, and further integrate ML in XAI.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:02 science and culture in general
Programme:Business Information Technology MSc (60025)
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