From Clicks to Commitment: Consumer Behavior Metrics and Their Impact on Online Purchase Decisions

Abstract

As online shopping continues to mature and gain popularity, it is necessary to analyzebehavioral metrics for developers and consumers. In these digital commerce settings, an understanding of purchase decisions is dependent upon behavioral metrics, a single quantifiable variable that is the composite of consumer activity (e.g. click frequency, dwell time, cart abandonment, session length, etc.), instead of demographically-based or survey-based approach to capture real interaction (i.e. performance). The goal of this study (From Clicks to Commitment: Consumer Behavior Metrics and Their Influence on Online Purchase Decisions) was to investigate the role of interaction patterns (moments of purchase intention). Using predictive modeling and clustering technique, and factoring all contextual elements and interactions (device type, timing, source of purchase (e.g. CPA, and trust signals the retailer has on the web site (e.g. secure payment method, easy or free returns)), interventions were able to prevent carrot drops on a consistent basis. The study confirmed that predictive/segmentation models with behavioral metrics plus contextual input provided a greater chance of the model being correct to forecast buyer choices and segmentation analysis provides some actionable methods for creating personalized marketing programs to retain and continue to attract impulse buyers, bargain buyers, and other consumer buyer behaviors and decision moment. The results indicated that their findings represent a more holistic view of online purchase decisions instead of incidental interactions; a moment that considered the interaction between behavioral response and contextual variables as determinants for purchase behavior. Through the understanding and deconstruction of these patterns/actions in digital commerce, the e-commerce industry will have the desired outcome of increased sales and increased likelihood of the ongoing consumer use.This research underscores the strategic importance of combining behavioral analytics with predictive modeling to move beyond descriptive insights toward actionable foresight in digital commerce.