Personalized AI-Driven Brand Loyalty Prediction for E-Commerce

Thesis Abstract

The rapid expansion of online retail platforms and the ubiquity of personalized experiences have intensified competition for customer loyalty in e-commerce, yet existing predictive models often rely on shallow engagement metrics and fail to escape the cold-start problem faced by new customers. This study addresses the gap by developing a personalized AI-driven framework for predicting brand loyalty in e-commerce that integrates behavioral data, attitudinal signals, and contextual factors across heterogeneous users. The aim is to produce accurate, explainable, and scalable predictions to inform targeted retention strategies and dynamic loyalty programs. Specific objectives include (1) identifying multifaceted predictors of brand loyalty through a hybrid data fusion approach, (2) developing a machine learning ensemble that combines gradient boosting, recurrent neural networks, and attention-based models to capture temporal consumption patterns and individual preferences, (3) articulating an explainable AI mechanism to elucidate model decisions for marketing practitioners, (4) validating the model across diverse product categories and regional markets to assess generalizability, and (5) evaluating the business value of personalized loyalty predictions in terms of churn reduction and average lifetime value (LTV). A mixed-methods research design is employed. The quantitative component analyzes a longitudinal dataset from a mid-to-large-scale e-commerce platform over 24 months, comprising 2.5 million anonymized transactions from 250,000 customers, augmented with 10,000 survey responses capturing attitudinal constructs aligned with the Theory of Planned Behavior and the Commitment-Trust Theory. Data collection instruments include event logs, clickstream data, purchase history, customer demographics, product metadata, and psychometric scales validated in prior loyalty research. The qualitative component consists of 40 in-depth interviews with marketing managers and 12 focus groups with long-term customers to contextualize model inputs and interpretability. Analytical techniques include data preprocessing with feature engineering to create behavioral sequences, recency–frequency–monetary (RFM) features, and attitudinal indices. Predictive modeling employs a stacked ensemble integrating gradient boosting machines (XGBoost), long short-term memory networks (LSTM), and transformer-based attention models to capture non-linear relationships and temporal dynamics. Model explanations are provided via SHAP (SHapley Additive exPlanations) values and counterfactual analysis to support transparent decision-making. The validation strategy uses time-split cross-validation and holdout sets by cohort, with performance metrics including AUC-ROC, F1-score, and calibration plots. Hypotheses test whether integrated behavioral-attitudinal features improve predictive accuracy over baseline behavioral models and whether explanations align with managerial intuitions about loyalty drivers. Expected findings indicate that personalized predictions substantially outperform baseline approaches, achieving an AUC-ROC above 0.82 across cohorts and improving churn prediction accuracy by 12–15% after incorporating attitudinal and contextual signals. Temporal models are anticipated to reveal peak predictive windows around promotional events and post-purchase experiences, while explicability analyses are expected to identify salient drivers such as perceived value, perceived trust, and ease of use, moderated by customer tenure and category. The study anticipates differential model performance across product categories (commodities vs. experiential goods) and markets (developed vs. emerging), underscoring the need for category- and region-specific calibration. Contributions to knowledge include (1) a robust, scalable framework for personalized loyalty prediction that fuses behavioral data with psychometric and contextual features; (2) an empirically validated, explainable AI model that delivers actionable insights for loyalty program design and real-time marketing interventions; (3) methodological guidance on integrating temporal sequence modeling with attitudinal data in consumer analytics; and (4) practical benchmarks for cross-category and cross-market generalization in e-commerce loyalty research. The study concludes that personalized AI-driven loyalty prediction enhances retention strategies and customer lifetime value when paired with transparent explanations that enable marketing teams to design targeted, fair, and compliant loyalty interventions. Recommendations include embedding the model into real-time marketing decision engines, continuously updating models with fresh data to mitigate concept drift, and conducting ongoing user research to refine the attitudinal constructs driving loyalty signals.