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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.
(9.013 – 15.04) = ? + 9.98% of 5399.98
40.05% of 210.05 – 10.15% of 109.99 × 5.02 = ?
17.06 2 + √36.08 – (4.04/2.99) × 3.02 × 4.92 = ? × 4.99
509.85 ÷ 15.05 + 210.16 – 18.06 × 5.95 = ?
139.88% of 119.89 + 1451.89 ÷ 6.01 - √196.01 = ? ÷ 3.01 + 215.98
`sqrt(1297)` + 189.99 =?
25.02% of 460.02+?% of 300.02=295.21
35.1% of 1599 = ?–(449.96 ÷ 6.12) × 2
(11.98% of 449.99) - 3.998 = √?