Behavioral Loyalty Prediction from Customer Reviews in E-Commerce: A Deep Learning and Explainable AI Approach

Understanding and predicting customer loyalty has become essential in increasingly competitive digital markets, yet conventional behavioral models, such as RFM, remain limited by their reliance on retrospective transactional data. Meanwhile, vast volumes of rich, interactive customer feedback, particularly online reviews, remain chronically underutilized for loyalty prediction. This study introduces a novel deep learning framework that predicts behavioral loyalty directly from customer review texts, eliminating the need for historical purchase records. Using a large e-commerce dataset of ۵۸,۸۴۱ samples, we combine advanced transformer-based language models (BERT, RoBERTa, ALBERT, GPT, and Qwen) with structured features and employ a genetic algorithm to optimize across ۱۹,۴۴۰ configurations of loyalty labeling schemes, neural architectures, text embeddings, and feature combinations. The resulting model achieves an accuracy of ۰.۹۲ and an F۱-score of ۰.۹۰, substantially outperforming baseline machine learning methods and non-transformer text embedding approaches. Beyond performance, the integration of SHAP-based explainability provides transparent insights into the linguistic and behavioral cues driving loyalty predictions. These findings demonstrate that customer reviews contain powerful behavioral signals that can be transformed into actionable intelligence for real-time CRM, customer retention, competitive analysis, and multi-channel personalization, particularly in contexts where RFM data are incomplete or unavailable. This work offers both a methodological advancement in loyalty prediction and a practical, scalable tool for organizations seeking to leverage interactive feedback to build stronger, more proactive customer relationships.

کلیدواژه ها:

Loyalty Prediction ، Customer Review Analysis ، Natural Language Processing ، RFM model ، Explainable AI