What's the Frequency?
Newsletter #83 10/2007
Drilling Down - Turning Customer
Data into Profits with a Spreadsheet
Customer Valuation, Retention,
Get the Drilling Down Book!
Hi Folks, Jim Novo here.
This month, we're looking a bit more deeply in the RFM model and
ways to customize it for your particular business model or target
audience. We also have a great article on the topic of
encouraging Failure, and a blog post on a different way to merchandise
a retail web site - the way we do it in the Lab Store.
A retail oriented newsletter for the holiday season - better hurry
and get to that Drillin'...
Best Customer Marketing Articles
From Failing to Thriving
One of the major challenges the Analytical Culture faces is Fear of Failure; itís so uncool to fail in many companies today. Yet some of the most spectacular wins often come after spectacular failures, and we have to teach managers that without Failure, there is no Learning Process.
Do it like they do at 3M and IBM, using the real stories of how failure went unpunished and was ultimately rewarded.
To access the full article review and links to the articles
Sample Marketing Productivity Blog Post
Store: Web Merchandising
October 8, 2007
Everything we do in the Lab Store is really based not on Sales, but on Productivity - how can we generate the greatest amount of profit for the least amount of time, money, and effort? I realize this approach does not square with conventional wisdom, but the Objective of the store was to replace 1 income (my wifeís) with the least amount of effort possible. If that is the Objective, then the Strategy is Productivity, not a focus on Sales.
For example, we turn our entire inventory 21.8 times a year.... Read
Questions from Fellow Drillers
What's the Frequency?
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.
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
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...
If you are a consultant, agency, or software developer with clients
needing action-oriented customer intelligence or High ROI Customer
Marketing program designs, click
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
Copyright 2007, The Drilling Down Project by Jim Novo. All
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