Data Analytics: RFM Analysis
Our fourth post in this series on Impact’s Data Analytics is about RFM Analysis
What is an RFM analysis?
- A Recency, Frequency, and Monetary analysis scores customers, providing a clear method for ranking customers in terms of their past behavior.
- RFM analysis provides a basis for allocating marketing budgets to customer groups in proportion to their proven value and likely response.
How does an RFM analysis support marketing management?
- The fundamental premise behind RFM analysis is that past customer behavior is a good predictor of future behavior. Without linking sales history to a customer list, all the customers look equal, but they are not. Businesses prosper when they recognize and nurture relationships with best customers.
- RFM analysis provides a framework for research designed to identify important market segments. Given a choice, a business should first target the market segments from which their best customers come.
What else can you use an RFM analysis for?
- Welcome offers to new customers, featuring products that existing frequent customers have purchased.
- Growth offers to induce customers in promising market segments but with low RFM scores to buy more.
- Reactivation offers to draw lapsed customers back into active status. The timing of such appeals is often supported by additional buyer behavior cycle research.
What are the key concepts involved in developing and presenting a RFM analysis?
- RFM analysis uses information about customers’ past buying behavior to predict which customers have the potential to be most valuable to your company in the future. The customers are divided into quintiles for each RFM parameter, and ranked on a scale of 1-5. Each customer is then assigned a three-digit score based upon their rankings, with “555” being the best and “111” being the worst. These scores are then used to create the various customer segments.
What is needed to do a RFM analysis?
- Historical sales data (including name, address, date, and sale information).
- RFM scores are calculated at a “point in time” and would need to be recalculated each time the sales data is refreshed.
How can I utilize a completed RFM analysis?
- By identifying the most valuable customers, RFM analysis allows you to concentrate resources on customers in proportion to their likely response. At the high end, targeting boosts sales. At the low end, targeting eliminates waste.
- RFM analysis allows you to develop and test strategies to move customers from one level of customer relationship to another and determine the cost/benefit benchmark that justifies the effort.