RFM analysis segments customers based on how recently they purchased (Recency), how often they purchase (Frequency), and how much they spend (Monetary). This technique helps identify high-value customers, churn risks, and potential targets for re-engagement campaigns. Option B : Decision trees are better suited for predictive tasks rather than segmentation based on purchasing behavior. Option C : Neural networks are powerful but unnecessary for straightforward RFM-based segmentation. Option D : Social media trends are often fleeting and may lack relevance to a specific user’s history. Option E : Surveys provide limited insights compared to behavioral analysis.
24, 34, 46, 76, 100, 140
132, 136, 109, 125, 2, 36
66, 220, 384, 543, 702, 861
32, 50, 82, 124, 180, 252
60, 63, 71, 86, 125, 145
-1236, -724, 5, 1005, 2336, 4062
4, 8, 24, 96, 485, 2880, 20160
164, 20, 216, -40, 284, -112
5, 13, 41, 185, 1111, 8149
384, 1152, 144, 432, 56, 162