Role of Temporal, Demographic, and Behavioral Factors in Customer Conversion Through Dynamic Creative Optimization in the Consumer-Packaged Goods Setting

Abstract

This study investigates the application of Dynamic Creative Optimization (DCO) in a Consumer-Packaged Goods (CPG) e-commerce setting, with the goal of discerning the most effective factors—temporal, demographic, and behavioral—on enhancing customer conversion. To evaluate this, an experiment was conducted over 35 days. Visitors to a CPG e-commerce site encountered one of three different Product Listing Pages (PLPs), each employing a distinct variant of DCO over the 35 days. Temporal-Based DCO adjusted content in real-time based on the timing of the visit, anticipating fluctuations in consumer intent. Demographic-Based DCO tailored the user experience to demographic information, presuming that a more relatable presentation would lead to higher conversions. Behavioral-Based DCO modified PLPs in accordance with individual browsing patterns and purchase history, operating on the premise that a user's past online activities could inform future interests. Data collected from these PLPs were analyzed using supervised machine learning to evaluate and predict conversion events from each. The findings revealed that PLPs with Behavioral-Based DCO demonstrated the highest conversion rates, affirming the significance of individual user behavior in optimizing e-commerce experiences. While demographic data also contributed positively to conversion rates, its effect was less pronounced than that of behavioral data, indicating the latter's superior predictive power. Temporal factors appeared to have the least influence, suggesting that the timing of dynamic content presentation alone may not be as critical as the substance of that content. The implications of these findings suggest a prioritization of behavioral data in DCO strategies to maximize the potential for customer conversions in the CPG e-commerce sector.