About RFM Analytics

RFM Analytics is an analytical tool that provides rankings for your contacts based upon their financial participation with the organization. It is a very commonly used tool for fundraising, but its applications are not limited to the fundraising marketplace. Any organization that targets contacts based on past participation can benefit from RFM Analytics.

With RFM Analytics, you can:

• Rank and organize your customer population into specific groups manually or automatically.
• Define ranking scores for recency, frequency, monetary, combined (calculated), and total (calculated) values.
• Create groups, such as quintiles, that reflect the relative ranking of customers according to all RFM measurements.
• Analyze transaction patterns to accurately predict future behavior.
• Define different customer population and transaction queries to include in each analysis.

RFM Analytics relies upon three core elements that turn transactional data into a three-dimensional score based on:

• Recency – How long since the last transaction?
• Frequency – How often does this transaction occur?
• Monetary Value – How much are these transactions worth?

  

RFM Analytics scoring

Rankings, once run, result in a score being assigned to each record. This score is important because ranks contacts/prospects within a matrix of highest to lowest values.

There are different schools of thought about how to use RFM scores. Some organizations use the total score (1+1+1 = 3). Others use the product of the score (1*1*1 = 1) and others use a concatenation of the scores (111).

Example RFM ranking

Tips for implementing RFM Analytics

• If you are running an RFM ranking for the first time, the automatic ranking might be able to help establish a first pass at quartiles or quintiles. However, make sure to review the results to see whether these rankings are correct for your organization. There is a danger in using and re-using automatic rankings for target marketing purposes. Automatic rankings will always produce equal buckets rather than telling you who is falling where based on typical transactional behaviors. After determining some basic highs and lows for each category, use the manual ranking to define those buckets and continue to evaluate the results.
• The key to successfully using RFM Analytics in any organization is having the patience to watch how people perform over a long period of time. Remember that anything can impact behavior and do not react immediately to changes. RFM buckets tend to remain stable.
• Build your queries separately. Do not use Save As to copy queries. There may be some unwanted criteria carried over from the original query.
• Make sure the transaction query Includes Quantity, Amount, and Transaction Date. These properties are used for the ranking. You can alias other properties if you wish, such as aliasing Date Received or Effective Date as the Transaction Date.