Reinforcement Learning for Full-Page Layout Optimization in E-Commerce Conversion Flows

Althaf Khan Pattan - Senior Software Engineer, Comcast, Exton, Pennsylvania, USA, altafkhanx6@gmail.com

Co-Author: Sneha Palvai - DevOps/AWS Engineer, Comcast, Pennsylvania, USA, snehapalvai09@gmail.com

Abstract

Conventional A/B testing treats web page optimization as a series of isolated element-level experiments - changing a button color here, swapping a headline there - while ignoring the compound effects of full-page composition and cross-stage visitor journeys. This paper reframes conversion optimization as a sequential decision problem where entire page layouts, not individual components, serve as the unit of selection. A multi-stage contextual bandit framework is described that maps user context vectors to layout choices across four conversion stages: landing, product, cart, and checkout. The framework incorporates Thompson Sampling for exploration-exploitation balance, a composite reward function that blends click-through, add-to-cart, and purchase signals, and a cross-stage coherence penalty that discourages jarring layout transitions between consecutive pages. Simulated experiments across 500,000 synthetic visitor sessions show that contextual layout selection outperforms static A/B splits by 18.4% in end-to-end conversion rate and reduces cumulative regret by 34.7% compared to epsilon-greedy baselines. The system architecture accommodates sub-50ms decision latency through pre-rendered layout caching and edge-level policy inference. Results are discussed in the context of practical deployment constraints including cold-start handling, layout fatigue detection, and guardrail mechanisms that prevent excessive exploration during high-traffic periods.

Keywords: reinforcement learning, contextual bandits, conversion rate optimization, layout optimization, A/B testing, Thompson Sampling, e-commerce, multi-armed bandits