Jim answers questions from fellow Drillers
(More questions with answers here, Work Overview here, Index of concepts here)
Topic Overview
Hi again folks, Jim Novo here.
Both RF(M) scoring and Lifecycle Grids use the same key predictive metrics – Recency and Frequency. So what’s the difference? RFM is a predictive “snapshot” at a specific point in time; LifeCycle Grids are more like a “movie” designed to be predictive over different periods of time. Another way to think of this: RFM is tactical, LifeCycle Grids are strategic.
You dig? Let’s Drill …
Q: We’re a telecom company trying to get a handle on customer churn and defection, so we can come up with some programs that will hopefully extend customer participation. We live in the no contract space, offering a service that’s an add on to wireless phone service, so we don’t have a good indicator as to when the customer relationship might end.
A: Ah, yes. Your business model is “built for churn”, as I said on my blog the other day. The behavior then is more like retail, where independent decisions are made in an ongoing way, deciding again and again to purchase.
Q: I think your LifeCycle Grids method will show best what is happening to our customers. If using this method, there doesn’t seem to be any reason to do the RF scoring as customers are just going into cells based on where they fall in the Recency and Frequency spectrum. Is that correct? Is there any real difference between RF scoring and the LifeCycle Grids approach?
A: You are partially correct, they are two versions of the same idea – both are scoring using Recency and Frequency. The traditional RF(M) scoring where customers are ranked against each other is a “relative” scoring method used primarily for campaigns – it is tactical, an allocation of resources model.
The LifeCycle Grids take the very same idea – the predictive value of Recency and Frequency – and turns it into a more strategic tool for learning about the customer migration ideas over time. This as opposed to a single point in time like RF scores, where you are looking more at response.
Put another way, the RF or RFM scoring look at likelihood to respond today, the LifeCycle Grids look at likelihood to remain a customer in the future. Scores are a short term idea; Grids are a long term idea.
Now, you could track RF scores over time for the same customer and accomplish the same thing as the Grids, except for the fact that the RF scores are forced to change sometimes when the customer behavior did not really change but there were changes in the data. This can lead to false indicators over time.
So for example, let’s say you have a customer with a 43 RF score and you drop a campaign. Large numbers of people respond and end up with a Recency of 5 and are now rank in front of this 43 customer. Because of the ranking and quintile counting method in RF or RFM, this customer might be “forced” down in the ranking to an RF score of 33, even though their behavior has not changed.
That’s precisely why I came up with the LifeCycle Grids / Delta Grids method. When you fix the boundaries of the cells instead of using a relative ranking, you don’t get this kind of artificial change in rank over time.
Bottom line, it’s just two ways of looking at the same thing, one follows from and is linked to the other. RF scores look at behavior at a point in time, Grids look at behavior over time.
Here’s what that looks like in practice.
Let’s say you have your LifeCycle Grid for Strategic purposes and are tracking the customer database over time. You notice from the Grid there is a problem brewing with best customers of a certain service coming from a specific campaign – they are becoming less Recent over time. The implication: this campaign is attracting low quality customers.
So you decide to do a campaign to this one cell on the Grid. The campaign will be very expensive per customer because it is a snail mail piece, so you want to mail it only to those most likely to respond in this cell at a point in time – the drop window for the campaign.
So you take the folks in this cell and RF score them, and drop the campaign only to those with the highest RF scores – those most likely to respond at the particular time you drop the campaign. Behavioral campaigns are all about timing; right message to the right customer at the right time.
Now, let’s say you have a monthly campaign opportunity of some kind, perhaps you can ride along in the telco statements. You could try to customize this “air cover” campaign at the customer level, but that’s kind of like crop dusting with the SST – it’s too much power, too expensive for the size of the audience.
And besides, the timing is not ideal – for best results, you probably should not “wait” for the monthly campaign, you want to address the defection behavior as it is happening. So you run a separate “Special Ops” campaign underneath, using the RF scoring to keep you costs down and response as high as possible.
See how they fit together? RF scores for a point in time, Grids for tracking over time. For more information on this topic, a discussion on Tactical versus Strategic LifeTime Value is here.
Jim
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