Recency Frequency Monetary Value: Primer
RFM Analysis
RFM analysis evaluates the recency, frequency, and monetary value of customer behaviors to determine the likelihood that a given customer will respond to a campaign. It is an empirical method that has long been applied to campaign planning and optimization as an alternative to segmenting a customer base by demographic means. The results of RFM analysis provide marketing departments the financial justification for specific marketing campaigns. RFM analysis provides the measurements for success from both planned and actual perspectives. The RFM analytic engine performs two processes:
* Segmentation — Similar to clustering, RFM analysis segments customers into different target groups. The main distinction is that RFM analysis is focused on specific aspects of customer behavior, namely:
1 Recency — When was the last purchase made? The most recent customers are sought based on the assumption that most-recent purchasers are more likely to purchase again than less-recent purchasers.
2 Frequency — How often were purchases made? The most frequent customers are sought based on the assumption that customers with more purchases are more likely to buy products than customers with fewer purchases.
3 Monetary value — What was the amount of the purchase? The biggest-spending customers are sought based on the assumption that purchasers who spent the most are more likely to purchase again than small spenders.
* Response rate calculation — Once segments have been established, the response rates for each customer segment is calculated based on historical data of actual response rates of past campaigns. The results of the response rate are then saved and then used during the segment building process. This building process models target groups by specifying attributes and building customer profiles for use in marketing activities such as running a campaign.
For the analysis to be effective, representative data must be used from prior campaigns. A campaign is considered sufficiently similar if the nature of the campaign and the customer target groups hold similar attributes. If historical data cannot be found for the representative target group, investments in learning must be made by launching new campaigns targeting the desired representative group so that RFM analysis can be applied. Using random, non-representative data for RFM analysis can render it useless.
To segment the customers, one needs to know which customers to segment, how many RFM segments to determine, and where to get the values for RFM analysis. The segmentation starts with recency, then frequency, and finally monetary value. First, customers are ranked into recency segments and given a score based on the number of segments. Within the recency segments, the frequency segments are then ranked and scored. Finally, within the frequency segments, monetary value scores are determined.
Figure 7.4 RFM segmentation process.
Another consideration is the number of segments that are created vis-à-vis the amount of records available and its impact on the response rate accuracy. For example, if the defaults of five segments per RFM were configured, then 125 segments would be calculated (5×5×5 = 125). Out of a customer base of 1,250,000 customers (10,000 customers per segment), one response would affect the response rate calculated by a hundredth of a percent (1/10,000). Out of a customer base of 1,250 customers (10 customers per segment), the difference of one response could swing the response rate by 10 percent. For smaller customer bases, the number of segments should be decreased. There are two options for handling customers that do not have data for the evaluation periods under consideration. The first option is the default, where all those customers fall to the bottom of the RFM analysis in the segment with the score of 111. The second option is to place these customers into a special segment with a score of 000, so that they fall out of the RFM analysis entirely (preventing any skewing of the segmentation results if there are enough customers missing data).
