Brand Propensity Prediction with Click-Through Rate as a Target

Yıl 2022, Cilt: 5 Sayı: ICOLES2021 Special Issue, 32 - 37, 30.11.2022

Alptekin Uzel , Kaan Pekel , Fatih Abut , Fatih Akay

https://doi.org/10.34088/kojose.1019277

Öz

Personalizing the e-commerce experience is vital since there are enormous amounts of products to offer 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. The brand propensity metric is one of those key features for personalizing products 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 (LR) models to investigate the effect of the target variable on calculating brand propensity. For comparison purposes, models based on Decision Tree (DT), Random Forest (RF), and XGBoost (XGB) have also been developed. 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. In addition, the LR-based propensity models exhibit the best average performance for both Top 3 and Top 5 accuracies among the machine learning methods.

Anahtar Kelimeler

Logistic Regression, Ensemble Model, Brand Propensity, Prediction

Kaynakça

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