Brand Propensity Prediction with Click-Through Rate as a Target

Abstract

Personalization of the e-commerce experience is vital since there are enormous amounts of products to offer to the customers. Each day new products are introduced into the ecosystem, and customer purchase behavior is dynamic as well. This mapping between products and customers needs to be optimized. E-commerce platforms try to funnel those products by a variety of methods like user clustering and product propensity analysis. Brand propensity metric is one of those key features for the personalization of products that are offered to the customer. Once the brand propensity is calculated, it can be used to cluster customers or list products within the same brand. Since customers periodically interact with different products, these interactions (e.g., product visit, favorite, basket, search, and order) are aggregated to predict the next actions of the corresponding customer. Typically, the next action might be an order action or click. In this study, we develop Logistic Regression models to investigate the effect of the target variable on calculating brand propensity. More specifically, the target variable to be evaluated for the brand propensity model has been set to both order probability and click probability. The “Top N accuracy” metric has been used to evaluate the performance of the models. As the study’s outcome, click as a target variable has been revealed to be more beneficial since it also shows that customers are more likely to explore what is inside that brand.