Context-Aware Onboarding Flow Adaptation Using First-Run Prediction Models in E-Commerce Applications

User onboarding is a decisive phase in mobile e-commerce applications, where early friction or cognitive overload can significantly impact user retention and conversion. Despite its importance, onboarding flows are typically static and uniform, ignoring contextual signals available during the first application run. This paper proposes a context-aware onboarding flow adaptation framework that leverages first-run prediction models to dynamically tailor onboarding experiences in real time. The proposed approach observes lightweight contextual, behavioral, and system-level signals—such as acquisition source, device performance tier, network quality, and early interaction velocity—to infer user intent and friction tolerance during the first session. Based on probabilistic predictions, onboarding components including authentication prompts, permission requests, tutorials, and feature discovery are reordered, deferred, or suppressed. The framework is designed for Android-based e-commerce applications and integrates with modern architectures using Jetpack Compose, Kotlin coroutines, and experimentation platforms. Experimental evaluation under simulated production-scale workloads demonstrates improvements in onboarding completion rate, time-to-first-action, and early-session conversion, without measurable regressions in application startup performance. The results suggest that adaptive onboarding driven by first-run pre-diction models offers a scalable and performance-safe alternative to static onboarding designs.