Churn Detection and User Classification via Machine Learning in the Food and Beverage Sector
Article Sidebar
Main Article Content
Abstract
In the modern business world, detecting and predicting customer behavior is one of the key factors for any operation to achieve their goals. Customer churn is one of these behaviors of interest, which makes churn detection and prediction a hot topic in the Machine Learning domain. The customer data that was studied is obtained from a global food and beverage company’s operations in Turkey: Their gift-based mobile application rewards customers who buy their products, and the user data of many sorts is stored within its database. In this study, the unlabeled customer data of a large scale was analyzed and classified via the combination of various supervised and unsupervised ML methods such as K-Means Clustering, Random Forest, Support Vector Machines, Logistic Regression, XGBoost. Then, a score-based churn detection & prediction algorithm is developed after picking the best performing models based on their performance metrics.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
Xiahou, X., & Harada, Y. (2022). B2C E-commerce customer churn prediction based on K-means and SVM. Journal of Theoretical and Applied Electronic Commerce Research, 17(2), 458-475. DOI: https://doi.org/10.3390/jtaer17020024
Kumar, A. (2023). Customer Segmentation of Shopping Mall Users Using K-Means Clustering. In Advancing SMEs Toward E-Commerce Policies for Sustainability (pp. 248-270). IGI Global. DOI: https://doi.org/10.4018/978-1-6684-5727-6.ch013
Hudaib, A., Dannoun, R., Harfoushi, O., Obiedat, R., & Faris, H. (2015). Hybrid data mining models for predicting customer churn. International Journal of Communications, Network and System Sciences, 8(05), 91. DOI: https://doi.org/10.4236/ijcns.2015.85012
Bose, I., & Chen, X. (2009). Hybrid models using unsupervised clustering for prediction of customer churn. Journal of Organizational Computing and Electronic Commerce, 19(2), 133-151. DOI: https://doi.org/10.1080/10919390902821291
Khine, S. T., & Myo, W. W. (2023, February). Mining Customer Churns for Banking Industry using K-means and Multi-layer Perceptron. In 2023 IEEE Conference on Computer Applications (ICCA) (pp. 220-225). IEEE. DOI: https://doi.org/10.1109/ICCA51723.2023.10182152
Liu, Y., Fan, J., Zhang, J., Yin, X., & Song, Z. (2023). Research on telecom customer churn prediction based on ensemble learning. Journal of Intelligent Information Systems, 60(3), 759-775. DOI: https://doi.org/10.1007/s10844-022-00739-z
Çallı, L., & Kasim, S. (2022). Using machine learning algorithms to analyze customer churn in the software as a service (SAAS) industry. Academic Platform Journal of Engineering and Smart Systems, 10(3), 115–123. DOI: https://doi.org/10.21541/apjess.1139862
Bagul, N., Berad, P., Khachane, C., & Surana, P. (2021). Retail Customer Churn Analysis using RFM Model and K-Means Clustering. International Journal of Engineering Research and Technology, 10(3).
Olaniyi, Abdulsalam & Arowolo, Micheal & Jimada-Ojuolape, Bilkisu & Yakub, Saheed. (2020). Customer Churn Prediction in Banking Industry Using K-Means and Support Vector Machine Algorithms. 48-54.