Hacking the RFM Model
Drilling Down Newsletter #100 5/2009
Drilling Down - Turning Customer
Data into Profits with a Spreadsheet
Customer Valuation, Retention, Loyalty, Defection
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Hi Folks, Jim Novo here.
This is the 100th monthly edition of the Drilling Down
newsletter. I'm not sure what that means other than I have been
doing them since October of 2000, and have built up a tremendous amount of
content on measuring and acting on customer behavior in a profitable
way. You can search the archives here, or use your favorite Search engine and restrict content to
Speaking of measuring and acting, the past month I have been
"engaged" in a lot of conversations about engagement. What
I found is everybody talks about measuring engagement but nobody
ever talks about acting on these measurements. What's the
point of measuring engagement if there is no plan to act in a
That's the topic of this newsletter.
For the 100th time, let's get to that Drillin'...
Sample Marketing Productivity Blog Posts
Got Discount Proneness?
May 15, 2009
Discount Proneness is what happens when you “teach” customers to expect discounts. Over time, they won’t buy unless you send them a discount. They wait for it, expect it. Unraveling this behavior is a very painful process you do not want to experience.
The latest shiny object where Coupon Proneness comes into play is the “shopping cart recapture” program.
Mark my words, if it is not happening already, these programs are teaching customers to “Add to Cart” and then abandon it, waiting for an e-mail with a discount to “recapture” this sale - a sale that for many receiving the e-mail, would have taken place anyway.
Continue reading on the blog:
Got Discount Proneness?
and feel free to leave comments.
Questions from Fellow Drillers
Hacking the RFM Model
Q: First of all thank you for your help. I have some questions I would be pleased if you answer
them for me.
A: No problem!
Q: 1. RFM analysis - is it possible to use some other ranking technique rather than quintiles? Using
quintiles for bigger databases will cause many tied values, isn't it a problem?
A: Sure, you can use it anyway it works best for you. There is no "magic"
behind quintiles, you can use deciles or whatever works best. It's the idea
of ranking by Recency, Frequency, and Value that is the key concept.
I've seen dozens and perhaps hundreds of variations on the core RFM
model, depending on how you classify a "variation". One
change that's common is changing the scaling, as you mention above, to accommodate
the size of the database. Smaller databases use quartiles or even
tertiles. Larger databases, choose the ordered distribution that
meets the need.
A more common modification is to convert "M" to different
types of "value" depending on the business model. Instead
of Sales, people fine-tune the financial side by using Net Sales, or Gross
Margin, net out discounts, etc. Or they use non-sales
representations of value tuned to the business model - ad revenue per
visit, total days of activity, that kind of thing.
Further, what can happens is the analyst or marketer will begin
to see patterns underlying the RFM cells - in sales, response, location,
merchandise, source, or some other customer variable. This leads to
cross-tabbing RFM score with other variables, and discoveries are made
which lead to customized versions of the RFM model.
For the most part, I envision this work really as segmentation, meaning
the scoring is not really modified - it's the population the scoring is
run on that is modified. So for example, you run separate RFM scores
for customers who are primarily hard goods buyers versus primarily
soft goods buyers. This approach to scoring is sometimes referred to
as RFM-C, C = category.
Or for large, ongoing campaigns, you can cross-tab RFM score by
source of the customer. This leads to "weighting" the
value of campaigns not by Sales or Response, but the long-term
profitability of the customer - you see campaign sources
"clustering" in high or low RFM scores.
Some campaigns generate weak customer profiles, but the
volume justifies doing them, as long as they are kept "reigned
in". Other campaigns generate high value profiles who are
"slow starters", and might be killed if you only looked at
Response and not RFM Score. So the scores begin to play more of a
role as a "standard" way to view customer value across
categories, campaigns, channels, etc.
This approach to scoring can eliminate a lot of the "gut
feel" legacies that can happen in marketing and merchandising.
Sure, go with your gut, but let's use a standard way to compare the
results of your gut feel and produce a "gut check" comparison.
Q: 2. I am planning to add user complaints and suggestions to RFM analysis. Each complaint will decrease the user score and
then cause to organize promotions just for users who had a complaint recently. Is it a good approach to add it to RFM analysis?
(some are using this method.)
A: I'm not exactly sure I know what you mean by "add", but
I think I get the gist of what you're trying to accomplish. In fact,
this project sounds like an example of a company actually trying to
"do something" about customer engagement and experience instead
of the usual navel-gazing. I have done these kinds of "apology
campaigns" before and they can work especially well, especially for
most valuable or highly engaged customers.
The scores only are predictive on a single behavior being scored, so I would not involve 2 different behaviors (purchase and complaint) in the same score, since the result would be defeating to the purpose of the score. I would not “adjust” a score directly based on a different behavior; I would score this behavior separately - and then use the scores in tandem to make adjustments in execution. If you really want to use multiple behaviors simultaneously in a model, you need to move up to regression.
As an analyst, you can of course "add" to the RFM scores any
way you wish. You can add any characteristic as a "tag" to a score but I would not involve
these characteristics in the scoring itself, unless they *are* the score.
But from the perspective of a Marketing person who has to use the scoring,
I would not want you to "corrupt" the scores themselves, but
rather to segment by other variables and then examine and use the scores
So for example, if these complaints are in the customer account, you could
score the customers on some other behavior such as purchases and include the
score in the account, then cross-tab score to complaints. For
example, "Give me every customer with a high RFM score AND at least 2
complaints". Or a reverse approach, "Of customers with at
least 2 complaints, what are the RFM scores?"
So for example, the complaint idea is an opportunity to create a custom
RFM-style score for complaints. Recency and Frequency are still
important, but there is no Monetary Value. Time frame may also be
different for complaints than purchases, for example, past 30 days or past
3 months as opposed to a full year or longer. You could generate
this "RF" score and then use it in combination with the RFM
score to drive different messaging to people by both:
1. How engaged they are in some behavior
2. Intensity and level (overall Frequency) of complaints, where
the more Recent a complaint has been made, the more likely it needs to be
addressed in some way.
Customers with high scores in both areas would be both most valuable to
the company in the future AND at highest risk for defection. This
is, of course, an extremely valuable target from a Marketing perspective
and one that should be addressed with great care. Sending these
people "normal" e-mail communications, for example, is much more
likely to accelerate to defection than retain the customer.
Depending on your business model, you might want to skip e-mail or
snail mail and get the President of the company to phone them!
Have a question on Customer Valuation, Retention, Loyalty, or Defection?
Go ahead and send it to me here.
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That's it for this month's edition of the Drilling Down newsletter.
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'Til next time, keep Drilling Down!
- Jim Novo
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