Conversion Prediction from Clickstream: Modeling Market Prediction and Customer Predictability

TLDR

Because 98 % of shoppers do not buy on their first visit, predicting later conversion is essential for retargeting strategies that rely on estimating product conversion rates and customer behavior. The study aims to address two problems: predicting market conversion rates and assessing individual customer predictability. We jointly model market and customer patterns using the buying‑decision process, then apply the market model to customers with high predictability and a dynamic ad‑behavior model to those with low predictability, evaluating both on a simulated dataset derived from real web and retargeting logs. Our approach yields consistently more accurate and robust conversion predictions and customer predictability than existing baselines in dynamic market settings.

Abstract

As 98 percent of shoppers do not make a purchase on the first visit, we study the problem of predicting whether they would come back for a purchase later (i.e., conversion prediction). This problem is important for strategizing “retargeting”, for example, by sending coupons for customers who are likely to convert. For this goal, we study the following two problems, prediction of market and predictability of customer. First, prediction of market aims at identifying a conversion rate for a given product and its customer behavior modeling, which is an important analytics metric for retargeting process. Compared to existing approaches using either of customer or product-level conversion pattern, we propose a joint modeling of both patterns based on the well-studied buying decision process. Second, we can observe customer-specific behaviors after showing retargeting ads, to predict whether this specific customer follows the market model (high predictability) or not (low predictability). For the former, we apply the market model, and for the latter, we propose a new customer-specific prediction based on dynamic ad behavior features. To evaluate the effectiveness of our methods, we perform extensive experiments on the simulated dataset generated based on a set of real-world web logs and retargeting campaign logs. The evaluation results show that conversion predictions and predictability by our approach are consistently more accurate and robust than those by existing baselines in dynamic market environment.