ML models analyze customers’ past activities and attributes to estimate their future purchase values and spending likelihood.
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Start by collecting transactional history, demographics, and engagement signals. Engineer features like purchase frequency, recency, and product preferences. Train models such as regression, random forest, or gradient boosting to predict Lifetime Value or spend categories. Validate with real-world data and integrate into CRM for targeting.
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Use RFM segmentation and behavior clustering to group customers, then apply supervised learning to estimate expected spend. Continuously retrain models based on seasonal trends and campaign outcomes. The results improve personalization, retention strategies, and promotional ROI.
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