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Machine learning in customer churn prediction
Fridrich, Martin ; Chramcov, Bronislav (referee) ; Lenort, Radim (referee) ; Šimberová, Iveta (referee) ; Dostál, Petr (advisor)
The dissertation examines customer churn prediction in e-commerce retail settings, presenting the current research landscape, analyzing key trends, and pinpointing opportunities for further investigation. The literature review is conducted using language processing. The study aims to develop, implement, and evaluate a machine learning system for predicting customer churn in the e-commerce environment, considering the economic implications of retention efforts, and facilitating a deeper understanding of the modeled phenomenon. The solution is organized into sections covering problem definition, data comprehension and processing, model development, evaluation, interpretation, and deployment. The author extends the traditional concept of customer churn as the lack of a transaction in a future period with a novel idea of the incremental economic impact of a retention campaign. The notions are validated using two datasets. The modeling framework incorporates GLM, SVM, ANN, decision trees, and meta-algorithms. Bayesian optimization estimates external parameters related to data processing and model building. The understanding of the phenomena is enhanced using SHAP tools, which are improved in terms of computation and visual representation. From the perspective of natural prediction performance, random forests and gradient boosting dominate; in the original task, ANN also performs well. When considering the financial results of the retention campaign, the novel approach functions excellently, mainly when coupled with decision trees or meta-learning. Recency and frequency representations of interactions and transactions are identified as key features; the feature importance of customer value emerges in the novel approach. Identifying and comprehending customer segments to target directly supports subsequent retention initiatives. In summary, the thesis offers an extensive overview of novel methods and tools for predicting customer churn, which can be valuable for future research and practical applications in business or educational settings.
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