How To Increase Response Rates by Making RFM Better

April 4, 2009
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Here’s another argument for why performance matters – this time a simple one based on a simple concept:  RFM.  RFM is the foundation of database marketing (Direct magazine’s 2000 subscriber survey reported that at least 75% of consumer and 52% of b-to-b direct marketers maintain standard RFM indicators).  RFM is a simple linear modeling technique […]

HourglassHere’s another argument for why performance matters – this time a simple one based on a simple concept:  RFMRFM is the foundation of database marketing (Direct magazine’s 2000 subscriber survey reported that at least 75% of consumer and 52% of b-to-b direct marketers maintain standard RFM indicators).  RFM is a simple linear modeling technique for ranking an audience based on their likelihood to respond using behavioral data.  It looks at:

  • Recency – The length of time since the audience’s last desired behavior (page view, ad view, click-thru, sign-up, survey completion, order, etc.).  Customers that have purchased recently are more likely to do so again versus customers that have not purchased in a while.
  • Frequency – The rate at which the audience is exhibiting those desired behaviors.  Customers that purchase frequently are more likely to purchase again than customers that have only purchased once or twice.
  • Monetary Value – The sum of the value of the audience’s desired behaviors.  Customers that have spent the most money in total are more likely to purchase again.

There are more advanced analytical techniques that can predict the likelihood of response with greater accuracy (e.g. CHAID, factor analysis, cluster analysis, logistic regression, etc.), but even these techniques typically incorporate RFM-based variables since they tend to be particularly predictive of response.  So while RFM is a simple concept, it’s also an important one.

Recency (the “R” in RFM) is a particularly powerful predictor of future behavior – it often has the most predictive strength of the three data elements in RFM.  In other words, your audience’s most recent behaviors are the most powerful indicators of their intent.  So the more quickly you incorporate the most recent available behavioral data into your RFM scoring methodology, the more precise your targeting will be. And as previously mentioned here, increasing targeting precision creates lots of goodness (like increasing campaign response rates, user engagement, etc.).  Increasing query performance and data load performance are great ways to achieve these goals.

Photo credit:  John-Morgan

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