Marketing Funnel Optimization in Mobile App Analytics: A Systematic Review

Mobile apps have revolutionized digital marketing and created a need for systematic optimization of user acquisition, engagement, retention, and monetization.However, research on mobile funnel optimization remains fragmented across marketing, information systems, and computational analytics.This systematic literature review investigates the question: What are the changes in conceptual, attribution, experimentation, machine learning, and privacy-aware approaches to support the optimization of the marketing funnel in mobile app environments?We performed a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework.Peerreviewed journal articles and conference papers published in English between 2015 and 2025 were searched in the Scopus database.The search identified 168 records, of which 27 studies met the predefined eligibility criteria and were included in the thematic synthesis.The synthesis indicates a shift from linear funnel conceptualizations toward adaptive lifecycle models encompassing awareness, consideration, acquisition, onboarding, engagement, and monetization.Attribution research is shifting from heuristic to multichannel, probabilistic, game-theoretic, and incremental approaches.The reviewed studies also show an increasing adoption of techniques such as causal experimentation, machine learning, contextual bandits, and reinforcement learning for adaptive optimization.Privacy concerns, such as App Tracking Transparency and the General Data Protection Regulation, also influence mobile measurement by restricting user-level signals and elevating the significance of privacy-preserving and probabilistic methods.The review considers mobile funnel optimization as a lifecycle, with attribution, experimentation, algorithmic optimization, and privacy governance working together throughout the funnel.The proposed literature-derived framework integrates these fragmented research streams and identifies directions for future empirical research in mobile app analytics.