Open Access
Cohort-Based Segmentation Framework for Machine Learning: Structuring Temporal Data for Enhanced Feature Engineering
4
Independent Researcher, Atlanta, USA
Abstract
Cohort-based segmentation is a well-established method for structuring customer data around time-based reference points, enabling causal inference and temporal feature engineering in marketing analytics. While extensively applied in subscription and retail loyalty contexts, its use in transactional service environments such as automotive aftersales remains underexplored. This paper addresses this gap by proposing a structured cohort framework tailored to irregular, discretionary service interactions, defining clear observation and outcome windows to enable robust engineering of recency, frequency, and monetary (RFM) features while avoiding data leakage. A real-world case study demonstrates the framework’s practical value, achieving a lift of 2.7 in the top decile and consistent capture rates across cohorts. These results highlight the approach’s ability to improve targeting precision, uncover temporal trends (including COVID-19 disruptions), and support marketing strategies for customer retention and engagement in industries with low-frequency, high-value transactions
Keywords
Predictive/Propensity Modeling,
Machine Learning,
Cohort Analysis,
Trend Analysis,
Seasonality Detection,
Temporal Feature Engineering,
Customer Segmentation,
Marketing Analytics,
Automotive Marketing
References
B. Omidvar-Tehrani, S. Amer-Yahia and L. V. S. Lakshmanan, "Cohort Representation and Exploration," 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), Turin, Italy, 2018, pp. 169-178, doi: 10.1109/DSAA.2018.00027
Lemmens, Aurélie & Croux, Christophe. (2006). Bagging and Boosting Classification Trees to Predict Churn. Journal of Marketing Research. 43. 10.1509/jmkr.43.2.276.
Blattberg, Robert & C., Robert & Byung-Do, & Kim, & Neslin, Scott & A., Neslin. (2008). Database Marketing: Analyzing and Managing Customers.
Gupta, Sunil & Zeithaml, Valarie. (2006). Customer Metrics and Their Impact on Financial Performance. Marketing Science. 25. 718-739. 10.1287/mksc.1060.0221.
Neslin, Scott & Grewal, Dhruv & Leghorn, Robert & Shankar, Venkatesh & Teerling, Marije & Thomas, Jacquelyn & Verhoef, Peter. (2006). Challenges and Opportunities in Multichannel Customer Management. Journal of Service Research - J SERV RES. 9. 95-112. 10.1177/1094670506293559.
Chen, J., Chen, M., Liao, W. and Chen, T. (2009), "Influence of capital structure and operational risk on profitability of life insurance industry in Taiwan", Journal of Modelling in Management, Vol. 4 No. 1, pp. 7-18. https://doi.org/10.1108/17465660910943720
C. X. Ling and C. Li, “Data Mining for Direct Marketing: Problems and Solutions,” Proceedings of International Conference on Knowledge Discovery from Data (KDD 98), New York City, 27-31 August 1998, pp. 73-79.
Larsen, Nicholas & Stallrich, Jonathan & Sengupta, Srijan & Deng, Alex & Kohavi, Ron & Stevens, Nathaniel. (2023). Statistical Challenges in Online Controlled Experiments: A Review of A/B Testing Methodology. The American Statistician. 78. 1-32. 10.1080/00031305.2023.2257237.
Neslin, S.A., Gupta, S., Kamakura, W., Lu, J.X. and Mason, C.H. (2006) Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models. Journal of Marketing Research, 43, 204-211.
Brownlee, J. (2020). Probability for Machine Learning: Discover How To Harness Uncertainty With Python. San Francisco: Machine Learning Mastery.
Tummalapalli, Vaibhav. (2025). Stratified sampling in Cohort-based data for Machine learning Model development. International Scientific Journal of Engineering and Management. 04. 1-8. 10.55041/ISJEM03377
V. Tummalapalli and K. Konakalla, "Statistical Techniques for Feature Selection in Machine Learning Models," International Journal for Innovative Research in Multidisciplinary Pursuit and Studies (IJIRMPS), vol. 13, no. 3, pp. 1-8, 2025, doi: 10.37082/IJIRMPS.v13.i3.232566
Chen, Yen-Liang & Kuo, Mi-Hao & Wu, Shin-yi & Tang, Kwei. (2009). Discovering recency, frequency, and monetary (RFM) sequential patterns from customers’ purchasing data. Electronic Commerce Research and Applications. 8. 241-251. 10.1016/j.elerap.2009.03.002.
Ngai, E. W. T., Xiu, L., & Chau, D. C. K. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications, 36(2), 2592–2602. https://doi.org/10.1016/j.eswa.2008.02.021
Hughes, A. M. (1996). The complete database marketer: second-generation strategies and techniques for database marketing. McGraw-Hill
V. Tummalapalli, “Feature Engineering for Building Machine Learning Models in Automotive Industry,” International Scientific Journal of Engineering and Management, vol. 4, no. 8, pp. 1–9, 2025. doi: 10.55041/ISJEM04985.
V. Tummalapalli, “Comprehensive study of data imputation techniques for machine learning models,” International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences, vol. 13, no. 4, 2025, doi: 10.37082/IJIRMPS.v13.i4.232674.
V. Tummalapalli, “Machine learning pipeline for automotive propensity models,” International Journal of Core Engineering & Management, vol. 8, no. 3, 2025, ISSN 2348-9510
V. Tummalapalli, “Understanding distance metrics in KNN imputation: Theoretical insights and applications,” Journal of Mathematical & Computer Applications, vol. 4, no. 4, pp. 1–4, 2025. doi: 10.47363/JMCA/2025(4)208.
Vaibhav Tummalapalli. (2025). Outlier Detection & Treatment for Machine Learning Models. International Journal of Innovative Research and Creative Technology, 11(3), 1–8. https://doi.org/10.5281/zenodo.16500050
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