Validating the Intelligent Preload Engine in Production: An A/B Testing Methodology and Deployment Playbook for ML-Driven Prefetching in SPA Funnels

Abstract

The Intelligent Preload Engine (IPE), described in prior work [1], is a browser-resident machine learning framework that watches in-session behavioral signals and uses them to predict which funnel stage a user will navigate to next, then preloads the relevant route bundle and API data before the navigation occurs. Simulation results in [1] showed a 41% reduction in Largest Contentful Paint on predicted transitions and an estimated 2.1 percentage point improvement in funnel completion rate. Those numbers come from synthetic sessions, not real traffic. This paper describes how to validate them in a live environment. The framework covers A/B experiment design with session-level traffic splitting, metric selection, and sample size derivation; a 13-field OpenTelemetry instrumentation schema built across three telemetry sources; statistical test selection and multiple-comparison correction strategy; a five-stage canary rollout schedule with explicit gate criteria and automated rollback triggers; a confidence threshold sensitivity grid showing how the default 0.60 gate performs relative to alternatives; and a step-by-step deployment playbook from pre-deploy checklist through post-experiment model refresh.