RFM (Recency, Frequency, & Monetary Value) is the classic direct marketing optimization method dating back to the 1990's. While the original calculation methods were klugy, to say the least, the underlying marketing concepts are still sound today. In fact, recency and frequency are often top predictors in propensity to purchase or churn models. In addition to utility functions for calculating and visualization of RFM, we extend classic RFM with RFM based customer segmentation (including segment transition over time) and the handling of additional variables that measure customer engagement like "Breadth."
Package details |
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Maintainer | |
License | GPL-2 |
Version | 0.1.1 |
Package repository | View on GitHub |
Installation |
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