Quantifying customer interactions on ML optimized page layouts

In online businesses, personalization of site content is crucial for providing a better user experience and increasing customer engagement. Machine learning algorithms are often used to analyze customer data such as browsing behavior, purchase history to tailor the website content to each individual customer’s preferences and needs. However, measuring the success of these personalized experiences can be challenging. While the ultimate goal is to convert customer visits into purchase sessions, tracking individual customer behavior can be difficult at scale. As a result, businesses often rely on aggregate metrics such as site-wide conversion rates, sales, and revenue to evaluate the effectiveness of their personalization efforts. However, it’s important to understand individual customers’ experiences with these ML-optimized pages. To address this, we propose a supervised ML model that quantifies customer engagement while browsing auto-optimized web pages, by building a customer site interaction score (CSI score). We first introduce a novel representation of customer click logs as a tree data structure induced by the webpage’s DOM structure. Then, we propose a novel attention model on the tree structure that performs vertical attention across the depth of the tree and horizontal attention across the sequence of trees to summarize customer interactions. The effectiveness of the proposed approach is evaluated using click logs obtained from the e-commerce domain.