New RFM: Segmenting Customers
# 44: 4/2004
Drilling Down - Turning Customer
Data into Profits with a Spreadsheet
Customer Valuation, Retention,
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In This Issue:
# Topics Overview
# Best Customer Retention Articles
# Question: Segmenting Customers
# New RFM Metrics: eMetrics Summit
Hi again folks, Jim Novo here.
The article links this month cover some plain speaking advice on choosing data mining software
and a 150+ year old
company that has perfected High ROI Customer Marketing. And then
we take a very deep look into the whys and hows of segmenting
Straight-up and to the point, put on those data shoes and Let's do
Best Customer Retention Articles
to Choose a Data Mining Suite
March 23, 2004 DM Review
This is an easy to understand look into the very confusing world of data mining
software. If nothing else, it does provide a framework for trying to
understand the choices to be made. Of course, you don't need data mining
to create customer models and it's probably not worth the effort until you get
the basics down.
Data for Sail
April 5, 2004 Direct Magazine
What a fantastic example of a company on top of both customer analysis
and turning the analysis into profitable actions. No fuss, no really fancy
systems, a customer database and people who know what to do with it. Simple yet stunning segmentation and results, not bad for a company founded in
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Questions from Fellow Drillers
If you don't know what RFM is or how it can be used to drive customer profitability in just about any business,
New RFM: Segmenting Customers
Q: Hi Jim, I'm a great fan of your work!
A: Well, thanks for your kind words.
Q: I have a basic question for you. We are an
online retailer and thus use email as the primary marketing
communication channel (we do use Direct Mail to our best customers
A: That's smart. I've seen some stats on using
direct mail to drive lapsed online customers either back online or
into a store that are very encouraging, real money-makers for retail.
Definitely worth testing, though in both cases, the product mix
averaged higher ticket than your category typically does.
Q: However, we don't have a set customer segmentation
technique and thus no specific customer segments. One outside
consultant, a statistician, had suggested looking at a new customer's
activity in the first 30 days and then classifying them into High
Spender, Frequent Transactor, etc. segments. Not sure how well
A: That's quite unusual, I think. It would work
in the first 30 days, but I think you would have to re-classify every
30 days using a scheme like that. Considering web-only behavior,
the typical retail lifecycle beyond 2nd purchase (many buy only one
time) is a ramping to a peak and then a more gradual, but still steep,
falloff in purchases. The model above would not take this into
account, and while the initial label might be accurate, it soon would
not be. That's not to say these kinds of models don't work, but
it usually takes years of testing and study to perfect them.
"Data miners" often believe the numbers will simply tell
them things like this, but they don't take into account the human
behavioral and other mitigating factors which may not be in the data.
For example, Recency and Latency
are really "meta-data" about customer behavior; they are
data created from other data. You can't just look at the first
30 days of transactions and give a customer a label; customers have LifeCycles
and you drive the highest ROI when you take advantage of knowing these
cycles and acting on them to increase profits.
Q: I feel that we target our customers primarily by
their category purchases, and not by any kind of behavioral model.
A: Category is often a secondary indicator, and probably
more useful along the lines of writing copy than the timing of a
promotion or offer. Your industry is full of stories about mis-targeting
by category, e.g. I bought a book as a gift about something I have no
interest in and you keep making offers to me
like I want to buy every book in this category. But it really
comes down to "when" first, and then "what".
The highest ROI promotions are always about "when" - the
timing of delivery. "What" is pretty much secondary,
since a dollar is a dollar no matter what category it comes from.
Put another way, if in the end, you want me to buy a book - any book -
and don't really care which category I buy from, then I'm not sure
"category" is anything other than a copy hint.
The exception to this would be if you find **known patterns** of
category trending and are using those to generate incremental sales.
For example, let's say you know on average, people who buy gardening
books eventually either stop buying altogether or continue on and buy
interior design books. Given this choice, I would screen for
people who are decelerating in their purchases of gardening books and
start making interior design book offers to them. Some will stop
buying altogether, but some will convert to interior design
buyers. If you use a control group with this kind of test you
will find out how many people you transitioned to interior design that
*would not have transitioned without your promotions*. These
people represent incremental sales and profits due to the promotions -
their defection was prevented, and that has a very high value.
Q: My marketing management thinks that segmenting
customers is not worth the effort, since the cost of email is so
low! We have over 15MM customers, with about 5MM active (have
bought in the past 12 months).
A: Well, that's a typical attitude, and there is some
truth to it if you only look at the cost of delivery. There are
two other costs, one tangible and one intangible. The most
common tangible cost in online retail is subsidy cost, that is, the
cost of a discount you didn't have to give to induce purchase, which
impacts margin. Do you remember the "ramping" after
2nd purchase I mentioned above? It is common for online
retailers to blow a ton of margin discounting to people in this ramp
who would have bought anyway. If you use control groups you can
literally see it happening before your eyes.
For example, let's say you take a group of customers who made their
first purchase in the same month due to some promotion or ad, and they
have all made more than one purchase. You split this group 50/50
into two groups, the control, which gets no e-mails, and the test,
which receives e-mails with discount promotions. Over the next
60 days, the control group spends an average of $200 per person and
the test (promotional) group also spends $200 per person - except you
gave them $20 in discounts, so their sales are really $180 for the
period. Multiply that $20 times a million customers and all of a
sudden you are talking about real money, know what I mean?
That's subsidy cost, and it is as real as e-mail delivery is cheap.
The second cost is more intangible, but it manifests itself through
declining response rates and unsubscribes. It's the cost of a
shorter LifeCycle caused by delivering too many promotions too often.
In other words, the cost of irrelevance. By the time the
customer is preparing for defection, they are ignoring your e-mails
because there have been so many of them that were not relevant to the
customer. So just when you need to make that big splash to
retain the customer, they are no longer paying attention and defect.
Q: What's your suggestion on a good way of segmenting
them and how many segments do you normally recommend? Also, how
often do we re-run the segmentation technique you recommend?
Once every 6 months, e.g.
A: Hmm... Well, I rarely try to guess these
things and simply let the data speak to me. Whatever the right
segmentation is will be revealed by the behavior of the customers
themselves in the data. Since you're somewhat familiar with my
stuff you probably know that the heart of it is either Recency or
Latency, and everything else from there is just a further
But even if the population on average is mainly driven by Latency,
you will certainly find sub-segments where Recency is the primary
driver. In the end, it's about increasing profits, and as I said
above, the profits in High ROI Marketing are usually a function of
timing. One of the components in the equation is reducing
subsidy costs to active customers; the other is squeezing more profits
out of defecting customers on their way out the door.
How can you figure this out? Start looking for patterns.
Here's a very simple example. What is the average number of days
between purchases? Let's say it is 40 days. When you do
your promotions, make smaller offers to those with a purchase less
than 40 days ago and larger offers to those with a purchase more than
40 days ago. Two segments, the offer is correlated to days since
After this promotion, inside each of those two segments, you have
two sub-segments: responders and non-responders. Aggregate the
members of each of the four groups and compare: what is similar or
different about them? Categories, time of day, day of week,
price point? Ad they responded to?
This is a Latency-based approach, which often works better when the sales process is not completely
controlled by the customer.
If the customer is in control, as she is in most retail situations, a Recency-based approach is probably better.
Recency looks at time *since last purchase* rather than time *between purchases*.
Do the same thing as with Latency above. When you drop your
promotion, look at response by 30-day segments - last purchase <30
days ago, last purchase 31 - 60 days ago, last purchase 61 - 90 days
ago, etc. You will see the customer LifeCycle right before your
eyes. Then look at responders versus non- responders for each 30
day block. Are they similar? Different? Similar /
different within a 30-day block? Similar / different when
comparing between 30-day blocks?
You're looking for patterns. When you start to see them, they
form the basis of the next test, where you specifically target a known
segment of people with specific characteristics who exhibit a known
behavior. Then sub-segment, and so on. Two segments become
four, four become eight, etc. You stop creating new segments
when you can't find a reason to create another one, there are no
significant differences left to group people by. Again, the data
itself tells you when you have reached the end of the segmentation
Of course, with 5 million actives, that could take a LifeTime!
The bigger question of course is this: how do you increase the number
of 12 month buyers? The answer is slow down the defection rate
by looking for it early, recognizing when it is beginning, and
attacking it specifically. Most of the people "defecting at
12 months" really defected a long, long time before that, you
just are not measuring it.
Hope that helps!
If you are a consultant, agency, or software developer with clients
needing action-oriented customer intelligence or High ROI Customer
Marketing program designs, click
New RFM Metrics: eMetrics Summit
Those of you who were at NetConnect 2004, it was great to meet you
and dig into web analytics and customer retention metrics. We
may be a relatively small bunch now but I've seen this same movie play
out in cable TV, TV Shopping, and cellular phones. Most onliners
are still focused on acquisition and by the time they start thinking
about retention they will already have messed it all up. Happens
every time, the web will be no different.
If you want to catch the retention wave early, you'll have another
chance this year at
Jim Sterne's eMetrics Summit in Santa Barbara June 2 - 4.
be there speaking on (guess) customer retention metrics and
management. The official title of the piece is "LifeTime
Value Without Waiting a LifeTime" and explores how to predict
which customers will be more valuable than others in the future.
Why would you want to know this? Because future value should
drive all your customer investment decisions, from how much you can
afford to acquire a customer to when to stop wasting money marketing
to a customer. If you attend the conference, be sure to grab me
and say "Hi." More info on the Summit:
2004 Santa Barbara
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 2004, The Drilling Down Project by Jim Novo. All
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