User and Store Behavior Analytics for Improving E-Commerce Business Decisions: A Design-Science Behavior-to-Decision Framework
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V7I4P101Keywords:
Behavior Analytics, Clickstream Analytics, Customer Journey, E-Commerce, Store Analytics, Purchase Propensity, Customer Lifetime Value, Causal Inference, Recommender Systems, Experimentation, Decision SupportAbstract
E-commerce platforms capture fine-grained evidence of how customers discover, search, compare, purchase, return, and seek service for products, while also generating store-level evidence on assortment, price, promotion, inventory, fulfillment, margin, and service conditions. Many organizations, however, lack an integrated method for translating these observations into economically justified and governable decisions. This study develops the Behavior-to-Decision Analytics Framework (BDAF) as a conceptual design-science artifact for user and store behavior analytics in e-commerce. The method synthesizes prior work in clickstream prediction, customer-journey analysis, customer-base modeling, recommender systems, causal inference, online experimentation, and responsible analytics to define a canonical data and journey representation, analytical progression, decision logic, governance controls, and implementation architecture. The resulting BDAF integrates consent-aware event contracts, identity and session management, multi-resolution journeys, store-context features, descriptive and diagnostic analytics, predictive and survival models, recommendation and uplift methods, experimentation, and constrained decision optimization. Its design outputs include a unified data model, method-selection logic, KPI and validation frameworks, a phased enterprise adoption roadmap, and eight testable research propositions. A worked scenario illustrates application across acquisition, search, checkout, retention, merchandising, and fulfillment. The paper reports conceptual design results rather than empirical estimates of business lift; empirical validation is specified as future research.
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