AI-powered Predictive Analytics for Customer Retention Mediated by the Average Basket Volume Size and Digital Generation Adoption
Keywords:
Predictive Analytics, Artificial Intelligence, Customer Retention, Basket Volume, Digital Generation Adoption, Mediation AnalysisAbstract
This study examines the effectiveness of AI-powered predictive analytics for customer retention with two theoretically grounded parallel mediators: average basket volume size (ABVS) and digital generation adoption (DGA). Drawing on behavioural economics and generational theory, we develop a dual-mediation model in which an AI predictive score (derived from an ensemble of machine-learning models) influences retention both directly and indirectly through transactional intensity (ABVS) and the propensity to adopt digital touchpoints (DGA). We analyze 1.2 million anonymized transactions from a Saudi omnichannel retailer (2022–2024) and compare Gradient Boosting Machines (GBM) and a fully connected Neural Network against a logistic-regression baseline. Using 5,000-sample bias-corrected bootstrap mediation tests, we observe that the AI score significantly predicts retention (AUC≈0.91 for the neural network). ABVS mediates approximately 21% of the effect and DGA mediates 13%; jointly, indirect effects account for 34% of the variance explained in retention outcomes. We provide robustness checks (out-of-time validation, alternative retention windows, and class-imbalance treatments) and practical guidance for real-time CRM deployment. The findings yield theoretical contributions to customer-analytics research by integrating transactional and generational mechanisms and offer actionable implications for segmented engagement strategies in the Saudi retail context.
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