Jim answers questions from fellow Drillers
Topic Overview
Hi again folks, Jim Novo here.
The standard RFM customer model is essentially a “snapshot” of likelihood to purchase (and so perhaps also profitability of campaign) at a given point in time. But what if you took these snapshots and turned them into a movie, looking at likelihood to purchase over time, and what specific inputs affected these changes? Then you’d have a LifeCycle movie / model, which amplifies the power of RFM substantially. Ready to find out more? The Drillin’, the Drillin’ …
Q: I am in catalog circulation. We currently use RFM to segment the file and then roll the RFM cells into more manageable segments (this is a new technique to me, I am new to this company, in my former company we mailed by RFM segment).
A: Hmm…this sounds like a “dumbing down” approach to RFM, but hey, if it works, why not. Sometimes this is done because the customer base is not really large enough to support 125 segments, and the differences between the segments can become unstable and less predictive unless they are aggregated.
Q: Because we are in a niche market and we saturate it pretty well, I would like to see which customers are on the edge or falling off (the Latency stuff) and which ones we can “reward” for being the best. I do not think the RFM analysis shows me that amount of detail.
A: Well, it can, and that is essentially what the Drilling Down book is about. RFM as it is traditionally used – as a “snapshot” of behavior – is pretty dumb compared to how it can be used. If you start looking at scores over time, you have a much more robust kind of tool – a “movie” of the Customer LifeCycle. In this scenario, you aren’t as concerned with the score at any one time, but what happens to it over time. Falling score indicates a move towards defection, rising score is an acceleration in loyalty.
An example might be helpful. Let’s say the acquisition folks run a huge new customer acquisition campaign in between the prior RFM scoring and the scoring done before your recent campaign drop. A big inflow of new customers from this new customer acquisition campaign can artificially “force” certain groups of customers down in score – even though their own behavior has not changed. In this case, the new score is not reflective of actual behavior – it’s basically increasing noise in the system.
That’s the problem with the “Snapshot” or date-specific view of Customer State – it’ a single point without reference. By using prior score, you are acknowledging behavior over time and the primary importance of the former State, as opposed to the current State – a Movie as opposed to a Snapshot.
In other words, from a Marketing perspective, I’m more interested in the path they are taking through the LifeCycle than any particular point in time during the LifeCycle represented by a single RFM score.
Since you seem to be familiar with this area and are likely to understand this statement, the final part of the book, “Customer Scoring Grids,” is where you really see the Customer LifeCycle emerge. Grids are a combination of Latency and RF(M) that produces a visual “map” of customer retention and defection (blog post readers, see the Measuring Engagement series).
Q: Thanks so much! I have turned my friend (in the tennis ball machine business) on to you and we have had some great conversations based on your e-mailed chapters!
A: Well, thanks for that, and have fun Drilling!
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