Drilling Down Newsletter - July 2001
In this issue:
# Best Customer Retention Articles
# Tracking the Customer LifeCycle: Latency
# Practice What You Preach: Online Advertising
Effectiveness? Tell Me About It... (Part 3)
# Questions from Fellow Drillers
Hi again folks, Jim Novo here. This month we've got great
customer retention article links and a look at customer LifeCycle
measurement and tracking. We also get deeper into measuring the
true payback of online advertising, and take a popular question - what
exactly goes on in data mining?
Let's do some Drillin'!
Best of the Best Customer Retention Articles
This article is on a DM News web site and will move into their paid
subscription archive 30 days after the date of publication listed
below, so check it out soon! The URLs are too long for the
newsletter, so the following links take you to a page you can link
directly to the article from.
Note to web
site visitors: These links may have expired by the time you read
this. You can get these "must read" links e-mailed to you
every 2 weeks before they expire by subscribing to the newsletter.
Should Be Spent on CRM
June 26, 2001 iMarketing News
This guy Hughes has got some nerve. He's basically saying it is
downright silly to go the CRM route for marketing when you can get
most of the benefits at a fraction of the cost using plain vanilla
database marketing - and he has a model to prove it. I agree
with him; that makes two of us.
And, here are three other "must read" articles which have no
Keys to Ensuring CRM Success
June 28, 2001 CRMCommunity.com
Now, here's a person who knows what they are talking about! Imagine,
making sure you understand customer behavior before you get involved
with CRM. Wish I'd thought of it. Oh, and by the way, if
you are looking for a framework to accomplish this understanding, may
I humbly suggest my book. You might not even need
"CRM", depending on what you are trying to accomplish
(increasing profits in customer marketing does not require CRM
implementation at all).
Distinctions in Buying Behavior
June 25, 2001 ClickZ
Don't look now folks, but the world is catching up to us. Track
customer behavior for best results, they say. Of course, they
don't tell you how, which is the information you need to know and will
find on almost every page of this web site, don't you know.
July 5, 2001 Knowledge@Wharton
"Focusing too closely (on customers) at the individual level is a
mistake" says the always excellent Peter S. Fader, supporting my
own Micro vs. Macro position. Other gems such as why
"firing" unprofitable customers is just ridiculous and the
related need to study customer behavior over time are covered in some
Tracking the Customer LifeCycle
Based on a national survey, 50% of marketing managers do not know
their customer defection rate, and the other 50% underestimate the
true defection rate. After reading this shocking statistic, I
figured it was time to do a series on customer LifeCycles, which can
be used not only to track customer defection, but also to define
opportunities to retain customers before they defect.
If you understand the customer LifeCycle, you can *predict* the
primary defection points and react to them before customers leave you.
This is the highest ROI marketing you can possibly do, because it's
cheaper than win-back (response is much higher) and preserves the
investment and profits you have in the customer.
So we're going to take a little tour though LifeCycle-based
marketing land the next few issues of the newsletter. If you're
new to our Drilling Down crew, or if you have not seen the articles on
the Drilling Down web site describing customer LifeCycles, you might
want to brush up. See the article Customer
LifeCycles and the tutorial Comparing
the Future Value of Customer Groups
for more information on Customer LifeCycles.
At the core of a LifeCycle-based marketing approach is (shocker)
customer behavior. Customers tend to behave in certain ways
unique to your business and products, and if you can discover these
patterns, you can use them to predict customer behavior. If you
can predict customer behavior, you can make a ton of money marketing
to your customers, because you can anticipate their behavior and take
appropriate steps to try and modify it.
Many approaches to customer marketing rely on customer behavior
"triggers". For example, a win-back program is
triggered when the customer defects. Have you switched
long distance or cellular providers lately? Did you get
inundated with win-back calls begging you to reconsider?
"Jim, we just wanted you to know we have lowered our rates".
Yea, well, thanks for telling me after over-charging me. But
could they have known I was about to switch?
Sure. If they had looked at the calling patterns of defected
customers like me, they would have seen a common thread in the
behavior. These patterns create the "trigger points"
for initiating high ROI marketing campaigns before the
defection. The proper profit maximizing approach is to wait
until I *look like* I'm going to defect, and then call me and offer a
lower rate *before* I defect.
I would humbly submit marketing to the customer *after* they
defect is a sub-optimal approach; the decision has already been
made. If you can market to them when they appear *likely* to
defect, you optimize your marketing resources by not applying
them too soon or too late in the customer LifeCycle.
An easy to implement and proven powerful LifeCycle trigger is
called latency. Latency refers to the average time between
customer activity events, for example, making a purchase,
calling the help desk, or visiting a web site. All you have to
do is calculate the average time elapsed (latency) between the
two events, and use this metric as a guide for creating and timing anti-defection
When you see a particular customer's behavior
diverge from the average customer behavior, you get a triggering
event. Since the calculation of latency is very simple, and the
diverging behavior is easy to spot, this type of anti-defection
campaign is an ideal candidate for "lights-out" or
automated rules-based customer retention campaigns.
As an example, let's take purchase behavior in a retail scenario.
If you were to examine your customers, and find the average time
between the second purchase and the third purchase was 2
months, you have found "third purchase latency". Any
who goes more than 2 months after the second purchase without
making a third purchase is diverging from the norm, and a likely
It's simple logic. If the average customer makes a third
purchase within 2 months of the second purchase, and a particular
customer breaks this pattern, they are not acting like the
average customer. Something has changed. This particular
customer's LifeCycle has become out of synch with the average
customer LifeCycle, and this condition is a trigger point for
high ROI customer marketing.
On average, if you divert marketing resources away from
customers who have made a 3rd purchase within 2 months after
the second, and apply these resources to customers who are
"crossing over" the 2 month LifeCycle trigger point without
making a third purchase, you will end up spending less money
and generating higher profits for any given marketing budget. You are applying your
limited resources right at the time in the
customer LifeCycle when they create the most powerful impact - at the point of likely customer defection.
Now, will all these customers respond? No, of course not.
the ones that do become active, loyal customers again, and those
that don't are probably not going to be good customers in the
future. The behavior of the rest of your customers tells you so.
These non-responding customers may not be worth spending
money on to "win-back", and in fact, will have much lower
response rates to a win-back campaign. They have already
demonstrated their lack of interest with their behavior, and you
could be better off financially by just letting them go and
focusing on more responsive, more profitable long-term customers.
The above example is a relatively crude approach to latency. As
you might suspect, different customer segments will have different latencies, and the more you fine-tune a latency
campaign, the more profitable it will become.
For example, let's say you execute the latency campaign
described above, and succeed in retaining 30% of the defecting
customers, making a tidy profit. But you really have two major
product lines, software and hardware, each 50% of sales. Could
the latency be different between software and hardware
customers? You betcha. Upon further analysis, you find
purchase latency for software is really one month, and for
hardware it's three months. The *average* is 2 months. So
bust the two groups apart, and run separate latency-based
campaigns, one for each product line.
In your original third purchase latency campaign, you promoted to
customers who did not make a third purchase within 2 months of
the second purchase. This means you were "late" for
(because the average latency is really 1 month) and early for
hardware (because the average latency is really 3 months). When
you realign the timing based on the line of merchandise, you find
instead of retaining 30% of customers, you retain 50% of the
customers, because you have synched-up the marketing effort with
the true customer LifeCycle more tightly.
And that, folks, is what LifeCycle-based marketing is all about -
using your own customer's behavior to telegraph to you the most important (and profitable) time to
market to them. The customer,
through their behavior, raises a hand and asks you to take
action. If you synch up your marketing efforts with the natural
customer LifeCycle, you can't help but being more successful.
If you'd like to see more on LifeCycle-based marketing
in future newsletters, be sure and let me
Practice What You Preach: Online Advertising
Effectiveness? Tell Me About It #3
OK, is Jim getting ripped off on his online advertising or not?
The only advertising I buy is highly targeted to search terms,
primarily through GoTo and the Google AdWords program. This
means I get two kinds of traffic from the same search engine - paid
and unpaid - for the same search!
Last month, we looked at a chart comparing the
value of these visitors for my top 3 search terms (relationship
marketing, customer retention, customer loyalty), broken out by visitor value by source - paid ad or "free" search.
By the way, in many cases both paid and free links are displayed at
the same time (if I rank high enough for the search term involved).
Visitors from paid ads are clearly of better quality - higher rates of
downloading, bookmarking, and newsletter subscription. Paid ad
visitors also stay twice as long on the web site.
This is a monster change from the previous analysis, which showed when
looking at *all* search terms (not just the top 3), paid versus
unpaid, the *free* visitors appeared to be of higher value based on
The implication of the above shift: there is variability in the
quality of visitor generated according to the *search phrase*, and
this may account for some or all the difference between the quality of
a pay versus free visitor. Intuitively, this makes sense to me,
because I only pay for relevant search terms, and "free
visitors" may be arriving as a result of a non-relevant search.
This is tremendously important to know, especially in light of the
general industry commentary that paid search listings result in poorer
search quality for users. Hmm...
So, let's take a closer look at search term quality by busting up
the aggregate "paid" search results above by search term,
and see what we get. The following table compares each search
term individually with the total site statistics, where RM =
Relationship Marketing, CR = Customer Retention, CL = Customer
Loyalty, and TS = Total Site statistics.
Avg. Visit Length 8.49 8.44
% 1 Page Visits 24%
22% 20% 43%
% Downloading 8.2% 6.1%
% Bookmarking 9.6% 7.6% 12.2%
% Subscribing 4.5%
4.5% 2.4% 3.2%
Clearly, the paid ads on average generate a higher quality visitor,
and there is substantial variability even among the top 3 search terms
in visitor quality. The term Customer Loyalty generates visitors
with a shorter visit length and lower newsletter subscribe rate than
the overall site! But at the same time, they bookmark at much
higher rates. A bit puzzling, and whenever a behavioral marketer
sees data sets with potentially conflicting indicators such as seen in
the term Customer Loyalty, we know there is probably something else
going on we need to find out about.
So find out we will, by Drilling Down yet another level in the next
If you'd like to see more on web log analysis
in future newsletters, be sure and let me
Questions from Fellow Drillers
Q: Hi Jim,
I'm interested in the different algorithms used by the various
analytical CRM vendors you mentioned on your web site. Are you,
or someone you might recommend, well versed in their
differences, applications, successes?
A: Well, that's a big topic. The vendors pretty much use
same algorithms - there are only so many approaches, and once
they're coded, anybody can use them. They may have minor
differences in the way they are implemented, but particularly
among the "data mining" type algorithms, they are all very
closely related. New ones come along once in awhile (the latest
is called "genetic modeling") but they are all parts of the
family; each has strengths and weaknesses, depending on the
data and ultimate goal.
There are really two big camps - so called "top down"
where a human creates a hypothesis and tests it by building a
model, and "bottoms up", which is data mining or machine
learning, where the machine looks for patterns and tries to make
sense of them.
Top down models include all the pure statistical approaches, like
nearest neighbor, clustering, regression, and so on. Machine
learning includes neural networks, fuzzy logic, case-based
reasoning, genetic modeling, and so forth. Algorithms like CHAID
and CART are something in between; they evolved out of statistics
but are also the basis for machine learning models.
If you are really interested in descriptions of what all these
things are and how they work, try these two books:
Data Mining Your Website - Jesus Mensa
Building Data Mining Applications for CRM
- co-authored by Berson, Smith, Thearling
The first is a tighter, more practical book. The second is a
monster and ties data mining more directly to CRM. Both also
briefly describe the top down or statistical approaches, and talk
about some of the reasons you would use one instead of another
(particularly the Mensa book, which doesn't "worship" data
as much as the other).
Generally, to be *incrementally* more successful than a
traditional statistical approach, data mining requires very clean
data, a long period to "train" the application, customer
with 100's of variables, and a few Ph.D. stats people hanging
around to interpret the machine language. The training thing
consists of the machine spitting out improbable answers over
and over until you train it to spit out the right ones. How do
you know which are the right ones? Frequently, from "top
analysis done by humans.
So there is a somewhat circular argument for data mining, and it
is really best used when you have gone though all the statistical
top down work first, and are looking for the "next level" of
answer. Otherwise, you don't know if what you got out of the
miner makes any sense, unless you know your business very well from a
Of course, the reason data mining became so popular as a
concept in the past couple of years is the software was going to
tell you everything and like magic run your business for you - no
humans needed. Turns out not to be the case, it seems.
Good top down modelers have a number of different modeling
approaches at hand, and will "test" statistically to see
modeling approach provides the best "fit", that is, is the most
stable over time and doesn't under or over predict an outcome. The most common
stats packages, some of which have evolved to
the point where you can pretty much run them out yourself without
a stats background, are from SPSS and SAS, which have naturally
made forays into data mining as well. In fact, many of the CRM
packages that "deliver" data mining capabilities really
SPSS or SAS.
Which brings us to RFM, the original behavioral model and the
topic of my site and book. When you run all this stuff above
looking for response or future value models, Recency and
Frequency will always factor highly into the result, no matter
whether you use top down stats or bottoms up machine analysis to
derive your forumlas.
The Recency and Frequency variables are so
embedded into human nature they always end up in any model
predicting behavior. What you get with all these other models,
top down or bottom up, is the 3rd, 4th, 5th etc. most powerful
predictors, with diminishing predictive power at each level. Said another way,
Recency and Frequency will give you an 80%
accurate model. Add a 3rd variable and you get 85%. Add a
and you get 88%. Add a 5th and you get 90%, and so on.
That's why I tell people who have never done any customer
modeling before this data mining stuff is like trying to get a
Ph.D. without ever going to high school. It's overkill in this
situation, and is most useful only if you have gone through basic
stat models first. In fact, the result of a regression model or
other statistical approach is one of the best data sets you can
feed a mining engine. Likewise, any good human stat person,
before they build you a regression model, will ask for RFM testing
results to help build a model.
It's like a pyramid, with RFM at the bottom, statistics in the
middle level and data mining on the top. The most powerful, most
broadly applicable models providing the highest immediate
impact, the so-called "low hanging fruit", are RFM-based.
Statistics adds further refinement, targeting even finer shades
of behavior. Data Mining figures out if there is anything left
to predict; it can produce segments you would never have found
otherwise, although there may only be 1/8 of 1% of your
customers in these segments, **particularly** if you have gone
through RFM and stats first.
The original RFM is a static model, predicting behavior at a
point in time. In my book, you learn how to convert the model to
look at behavior over time, a much more powerful,
LifeCycle-oriented tool. Any company considering adding
customer analytics, whether CRM-related or not, should go through
the process of scoring customers using simple behavior-based
models and trying their hand at high ROI marketing programs
first. If you do this before shelling out the money for a data
mining package, you'll be more likely able to justify the ROI and
figure out if data mining will really help you.
Want to do a quick test for potential ROI on customer
analytics? Read this.
Will any CRM consultant or software vendor tell you all this?
Nah. That's why I wrote the book, don't you know...
That's it for this month's edition of the Drilling Down
newsletter. If you like the newsletter, please forward it to a
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Any comments on the newsletter (it's too long, too short, topic
suggestions, etc.) please send them right along to me, along with
any other questions on customer Valuation, Retention, Loyalty,
and Defection, to me.
'Til next time, keep Drilling Down!
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