Cohort-Based Segmentation Framework for Machine Learning: Structuring Temporal Data for Enhanced Feature Engineering

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