Brand Propensity Prediction with Click-Through Rate as a Target

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

1. [1] Hussien F. T. A., Rahma A. M. S., Abdulwahab H. B., 2021. An e‐commerce recommendation system based on dynamic analysis of customer behavior, Sustainability, 13(19), 10786.
2. [2] Abdul Hussien F. T., Rahma A. M. S., Abdul Wahab H. B., 2021. Recommendation Systems for E-commerce Systems An Overview. Journal of Physics: Conference Series, 1897(1), 012024.
3. [3] Daoud M., Naqvi S. K., Ahmad A., 2014. Opinion Observer: Recommendation System on E-Commerce Website. International Journal of Computer Applications, 105(14), pp. 975–8887.
4. [4] Zhang Y., 2021. Prediction of Customer Propensity Based on Machine Learning. In: Proceedings of Asia-Pacific Conference on Communications Technology and Computer Science, pp. 5–9.
5. [5] Valecha H., Varma A., Khare I., Sachdeva A., Goyal M., 2018. Prediction of Consumer Behaviour using Random Forest Algorithm. In: Proceedings of 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, pp. 1-6.
6. [6] Szabó P., Genge B., 2020. Efficient Conversion Prediction in E-Commerce Applications with Unsupervised Learning. In: Proceedings of 28th International Conference on Software, Tele-communications and Computer Networks.
7. [7] Liu X., Li J., 2016. Using support vector machine for online purchase predication. In: Proceedings of International Conference on Logistics, Informatics and Service Sciences.
8. [8] Hu W., Shi Y., 2020. Prediction of online consumers’ buying behavior based on LSTM-RF model. In: Proceedings of 5th International Conference on Communication, Image and Signal Processing, pp. 224–228.

9. [9] Zhai X., Shi P., Xu L., Wang Y., Chen X., 2020. Prediction Model of User Purchase Behavior Based on Machine Learning. In: Proceedings of IEEE International Conference on Mechatronics and Automation, pp. 1483–1487.
10. [10] Micol P. L. et al., 2021. Machine learning through the lens of e-commerce initiatives: An up-to-date systematic literature review, Computer Science Review, 41, 100414.
11. [11] Stubseid S., Arandjelovic O., 2018. Machine Learning Based Prediction of Consumer Purchasing Decisions: The Evidence and its Significance. In: Workshops of the Thirty-Second AAAI Conference on Artificial Intelligence, pp. 100–106.
12. [12] Sasi R. K., John H., Jerard B., Sudheer S., A. Shaju, 2020. Customer Behaviour Prediction using Propensity Model. IPEM Journal of Computer Application & Research, 5, pp. 38–43.
13. [13] Peng C. Y. J., Lee K. L., Ingersoll G. M., 2010. An Introduction to Logistic Regression Analysis and Reporting. The Journal of Educational Research, 96(1), pp. 3–14.
14. [14] Cremonesi P., Koren Y., Turrin R., 2010. Performance of recommender algorithms on top-N recommendation tasks. In: Proceedings of the 4th ACM Conference on Recommender Systems, pp. 39–46.
15. [15] Lakshminarayanan B., Decision Trees and Forests: A Probabilistic Perspective, Ph.D. Thesis, 2016.
16. [16] Ali J., Khan R., Ahmad N., Maqsood I., 2012. Random Forests and Decision Trees. International Journal of Computer Science Issues, 9(5), pp. 272–278.
17. [17] Chen T., Guestrin C., 2016. XGBoost: A scalable tree boosting system. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794.