Machine Learning Methods for Churn Prediction and Infrastructure Resilience
Name
Agrawal-shreea97-mba-mgt-2025-thesis.pdf
Description
Thesis PDF
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4.69 MB
Format
Adobe PDF
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Author(s)
Agrawal, Shreeansh
Advisor(s)
Zarandi, Mohammad Fazel
Amin, Saurabh
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
Abstract
This thesis investigates how advanced machine learning methods can effectively address two critical business challenges facing the telecommunications industry: short-term customer churn prediction and long-term infrastructure resilience to climate-driven disruptions.
In the first part of this work, I develop an upgrades-informed churn forecasting model tailored specifically for marketing operations. Recognizing limitations in the existing aggregate forecasting methodologies, I create a cohort-based cascade model that explicitly integrates customer upgrade behavior across various contract tenures. To address data sparsity and longitudinal gaps in newer contract types, I employ synthetic data generation and imputation techniques, such as regression-based methods and Multivariate Imputation by Chained Equations (MICE). For forecasting churn and upgrade rates, I prioritize interpretability by applying linear regression enhanced with time-series forecasting techniques and macroeconomic indicators, including the Consumer Price Index. This approach significantly improves forecasting accuracy, aligns internal stakeholder objectives, and supports strategic decision-making around customer retention and promotional offers.
The second part focuses on building predictive models and strategic frameworks for long-term infrastructure resilience in the face of increasing climate risks. Leveraging spatial-temporal clustering methods (DBSCAN) and advanced neural network architectures, I develop a model to attribute historical outages to extreme weather events. Further, I integrate this model with future climate scenarios from CMIP5 projections using Monte Carlo simulations, providing actionable insights into future infrastructure vulnerabilities. Employing SHapley Additive exPlanations (SHAP), I interpret model predictions, highlighting critical factors such as precipitation, windspeed, and atmospheric pressure. Additionally, I propose frameworks for quantifying financial impacts of future outages and recommend optimization strategies for proactive infrastructure hardening and emergency response.
Collectively, these applications demonstrate the value of strategically employing interpretable and robust machine learning methodologies to enhance short-term operational decisions and long-term strategic planning within telecom organizations.
MIT Department
Sloan School of Management
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
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