Learning to Reduce Cart Abandonment: A Data-Driven Personalization Framework Based on Customer 360 and Journey Friction Analytics

Abstract

Online retail environments expose shoppers to a wide variety of products, promotions, and interface patterns, yet a large fraction of initiated baskets never reach checkout completion. Cart abandonment arises from a combination of intent volatility, perceived risk, cognitive load, and friction in the interaction flow. As digital commerce platforms evolve toward real-time decisioning, there is growing attention on learning-based personalization strategies that adapt the interface and messaging to each visitor. At the same time, many organizations are assembling Customer 360 assets that aggregate multi-source data into unified profiles spanning browsing, transactions, service interactions, and marketing responses. These assets remain underexploited for fine-grained modeling of abandonment dynamics. Parallel advances in instrumentation provide detailed telemetry on micro-frictions along the journey, including latency spikes, validation errors, and micro-patterns of hesitancy. This paper develops a technical framework that combines Customer 360 representation learning with journey friction analytics and policy optimization for intervention selection. The framework casts cart abandonment reduction as a sequential decision problem over states that encode both long-term customer attributes and short-term friction signals. It supports offline learning from large historical logs under practical constraints such as delayed outcomes, partial observability, and action-dependent exposure. The presentation emphasizes formal problem definitions, model structures, and training objectives rather than any specific deployment context. The resulting formulation is intended to guide the design of data pipelines, feature learning modules, and policy optimization layers in personalization systems targeting reductions in cart abandonment.