Jim answers more questions from fellow Drillers
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Q: I ordered your book and have been looking at it as I have a client who wants me to do some RFM reporting for them.
A: Well, thanks for that!
Q: They are an online shoe shop who sends out cataloges via the mail as well at present. They have order history going back to 2005 for clients and believe that by doing a RFM analysis they can work out which customers are dead and Should be dropped etc. I understand Recency and have done this.
A: OK, that’s a great start…
Q: But on frequency there appears to be lots of conflicting information – one book I read says you should do it over a time period as an average and others do it over the entire lifecycle of a client.
A: You can do it either way, the ultimate answer is of course to test both ways and see which works better for this client.
Q: Based on the client base and that the catalogues are seasonal my client reckons a client may decide to make a purchase decision every 6 months. My client is concerned that if I go by total purchases , some one who was really buying lots say two years ago but now buys nothing could appear high up the frequency compared to a newer buyer who has bought a few pairs, who would actually be a better client as they’re more Recent? Do I make sense or am I totally wrong?
A: Absolutely make sense. If you are scoring with RFM though, since the “R” is first, that means in the case above, the “newer buyer who has bought a few pairs” customer will get a higher score than the “buying lots say two years ago but now buys nothing” customer.
So in terms of score, RFM self-adjusts for this case. The “Recent average” modification you are talking about just makes this adjustment more severe. Other than testing whether the “Recent average” or “Lifetime” Frequency method is better for this client, let’s think about it for a minute and see what we get.
The Recent average Frequency approach basically enhances the Recency component of the RFM model by downgrading Frequency behavior out further in the past. Given the model already has a strong Recency component, this “flattens” the model and makes it more of a “sure thing” – the more Recent folks get yet even higher scores.
What you trade off for this emphasis on more recent customers is the chance to reactivate lapsed Best customers who could purchase if approached. In other words, the “LifeTime Frequency” version is a bit riskier, but it also has more long-term financial reward. Follow?
So then we think about the customer. It sounds like the “make a purchase decision every 6 months” idea is a guess as opposed to analysis. You could go to the database and get an answer to this question – what is the average time between purchases (Latency), say for heavy, medium, and light buyers? That would give you some idea of a Recency threshold for each group, where to mail customers lapsed longer than this threshold gets increasingly risky, and you could use this threshold to choose parameters for your period of time for Frequency analysis.
Also, we have the fact these buyers are (I’m guessing) primarily online generated. This means they probably have shorter LifeCycles than catalog-generated buyers, which would argue for downplaying Frequency that occurred before the average threshold found above and elevating Recency.
So here is what I would do. Given the client is already pre-disposed to the “Recent Frequency” filter on the RFM model, that this filter will generally lower financial risk, and that these buyers were online generated, go with the filter for your scoring.
Then, after the scoring, if you find you will in fact exclude High Frequency / non-Recent buyers, take the best of that excluded group – Highest Frequency / Most Recent – and drop them a test mailing to make sure fiddling with the RFM model / filtering this way isn’t leaving money on the table.
If possible, you might check this lapsed Frequent group before mailing for reasons why they stopped buying – is there a common category or manufacturer purchased, did they have service problems, etc. – to further refine list and creative. Keep the segment small but load it up if you can, throw “the book” at them – Free shipping, etc.
And see what happens. If you get minimal response, then you know they’re dead.
The bottom line is this: all models are general statements about behavior that benefit from being tweaked based on knowledge of the target groups. That’s why there are so many “versions” of RFM out there – people twist and adopt the basic model to fit known traits in the target populations, or to better fit their business model.
Since it’s early in the game for you folks and due to the online nature of the customer generation, it’s worth being cautious. At the same time, you want to make sure you don’t leave any knowledge (or money!) on the table. So you drop a little test to the “Distant Frequents” that is “loaded” up / precisely targeted and if you get nothing, then you have your answer as to which version of the model is likely to work better.
Short story: I could not convince management at Home Shopping Network that a certain customer segment they were wasting a lot of resources on – namely brand name buyers of small electronics like radar detectors – was really worth very little to the company. So I came up with an (unapproved) test that would cost very little money but prove the point.
I took a small random sample of these folks and sent them a $100 coupon – no restrictions, good on anything. I kept the quantity down so if redemption was huge, I would not cause major financial damage.
With this coupon, the population could buy any of about 50% of the items we showed on the network completely free, except for shipping and handling.
Not one response.
End of management discussion on value of this segment.
If you can, drop a small test out to those Distant Frequents and see what you get. They might surprise you…
Good luck!
Jim
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