Reducing Friction in the B2C Digital Purchase Funnel: A Data-Driven Customer Journey Modeling and Intervention Framework for Revenue Optimization

Abstract

Digital consumer purchase journeys increasingly unfold within dense, multi-touch environments where channel proliferation, device fragmentation, and rich merchandising content create opportunities for both engagement and friction. At each stage, small operational or cognitive impediments alter the balance between continuation and abandonment, shaping revenue and customer experience in ways that are measurable but often left unstructured. Friction emerges from latency, information overload, verification demands, input complexity, navigation inconsistency, and perceived risk, interacting with individual sensitivity, intentions, and context. Many organizations deploy localized user interface changes or incentives without a unifying modeling framework that links observed frictions, interventions, and revenue outcomes over heterogeneous paths. This paper presents an integrated, data-driven framework for modeling the B2C digital purchase funnel as a controlled stochastic process combined with causal estimation and constrained optimization. The framework formalizes friction as parameterized costs embedded in state transitions, incorporates behavioral regularization, and supports heterogeneous treatment effect estimation for candidate interventions such as content adjustments, sequencing rules, and incentive strategies. It is designed to operate under typical data limitations, including incomplete identity resolution, sparse conversions, seasonality, and noisy attributions. The resulting structure enables calibrated, uncertainty-aware policies for reducing friction and reallocating intervention budgets across segments, channels, and devices without overstating precision. The discussion emphasizes internal coherence between measurement, modeling, decision rules, and governance, providing a neutral basis on which organizations can align engineering, design, and marketing actions with empirically grounded expectations of incremental revenue impact.