Detection and Resolution of Personalization Conflicts and Fatigue in B2C Digital Journeys Using Unified Customer 360 Behavioral and Preference Data

Abstract

Personalization in business to consumer digital channels has become widespread as organizations integrate behavioral, transactional, and preference data into unified customer profiles. Many journeys now involve multiple orchestrated touchpoints across web, mobile, email, messaging, and in product interfaces. As the density and adaptivity of these journeys increase, unintended personalization conflicts and user fatigue become more frequent. Conflicts can appear as inconsistent messages, competing calls to action, or contradictory offers, while fatigue can arise when the volume, frequency, or repetitiveness of personalized content exceeds individual tolerance thresholds. Unified customer 360 data provides a basis to reason about such phenomena at the level of individual users and sessions, but practical detection and resolution mechanisms remain relatively under formalized. This work studies how to use unified behavioral streams, preference declarations, and inferred latent signals to detect and resolve conflicts and fatigue in B2C digital journeys. The paper conceptualizes personalization conflicts and fatigue as properties of sequences of actions and responses over time rather than isolated events. It proposes a set of quantitative representations and model families that operate on customer 360 data and produce interpretable scores and policy recommendations. The emphasis is on connecting modeling constructs to operational decision points such as eligibility, ranking, throttling, and escalation rules. The discussion remains neutral regarding deployment strategies and focuses on outlining design options, possible detection pipelines, and trade offs between sensitivity, precision, and user experience stability.