Drilling Down Newsletter # 15 -
Drilling Down - Turning Customer
Data into Profits with a Spreadsheet
Customer Valuation, Retention,
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Drilling Down Newsletter # 15 -
In this issue:
# Visitor Quality / Engagement
Calculator for WebTrends
# Best of the Best
Customer Marketing Articles
# Tracking the Customer LifeCycle:
Results of Poll
# Questions from Fellow Drillers
Hi again folks, Jim Novo here. This month we have a free tool
for you to help analyze and manage web site visitor quality, followed
by the usual "must read" articles on customer Retention from
around the web. The votes are in on the editorial content of the
newsletter for the beginning of next year, and I clean up the e-mail
bag with a year end rush of questions from fellow Drillers walking the
High ROI Customer Marketing walk.
Sound good? Let's do some Drillin'!
Visitor Quality / Engagement
Calculator for WebTrends
Every web site has at least two big issues to deal with: generating
targeted traffic and making sure this traffic gets "hooked"
when it arrives at the site. Server log analysis using WebTrends
can create the raw data you need to track important metrics
surrounding these issues of targeted traffic and engaging the visitor,
but the raw data isn't very useful in the WebTrends format for these
This calculator takes the raw data input by a user from the
standard WebTrends report and turns this data into actionable metrics
you can track over time. What do I mean by actionable? All
the metrics created by the calculator tell you specific things about
the way your visitors are behaving, and you can literally "take
action" based on the metrics, tracking the changes in visitor
behavior your action caused. These are not "Gee, that's
interesting" metrics. They are the ones that have dramatic
impact on the profitability of your web site, and by tracking them and
trying to affect them, you can get your web site a lot closer to the
goals you are trying to achieve.
So, as my holiday gift to you, may I present the beta version of
the Visitor Quality / Engagement Calculator for WebTrends! This
special edition of the calculator is being provided with detailed
background material on how the metrics are derived and how they are
used. Future free editions may not include these details.
Look for more on this next year in association with the good folks at
Future Now Inc, who have a few nifty calculators of their own you
The Visitor Quality / Visitor Engagement
calculator for WebTrends (Excel spreadsheet format, unzipped or
zipped) download is here.
If you have not already, why don't you download the first 4
Chapters of the Drilling Down book, or the ReadMe file for the
customer scoring software that comes with the book while you are on
the download page!
Best of the Best Customer Retention Articles
I find for this newsletter only one "must read" article they
are about to lock away in the paid archives at DM News, but it's a
doozy. So included are 2 other must read articles (which don't
expire) you may have missed from elsewhere around the web.
The URL's are too long for the newsletter, so the links take you to
a page with more info on what is in the article and a direct link.
Note to web
site visitors: These links may
have expired by the time you read
can get these "must read" links e-mailed to
every 2 weeks before they expire by
subscribing to the newsletter.
Traditional Segmentation - DM News
Read by: Expires December 12,
Just an absolutely fantastic article on life after RFM, which confirms
my experience - 80% of the predictive value of the most sophisticated
customer behavior model you can develop is available using RFM models
created with an Excel spreadsheet.
of Customer Satisfaction
is Necessary, but What does it Have to do
with Customer Relationships? - CRMForum.com
Read by: Expires
November 9, 2001
Gets the longest title award, but still a very good debunking of the
customer satisfaction survey myth.
The first steps of Web analytics - searchCRM.com
Read by: Expires
November 14, 2001
I shouldn't have to bring this up anymore by now folks, but I am
absolutely stunned by the number of people who do not use easy, cheap,
High ROI web site analytics. I don't understand how you would
run a web business - any web business - without them.
Make sure you check out the calculator mentioned
above if you are having difficulty deciding what is important to
track or how to use WebTrends!
Tracking the Customer LifeCycle: Latency
Speaking of stunned, about 26% of you voted on the choice to
continue with the Latency topic or not, and 99% of those voted for
continuation. Amazing. Who am I to argue? Continue
we will, but will put it off and start with a fresh perspective next
year. We will continue to Track the Customer LifeCycle, with a
focus on how to use the LifeCycle to make more money in your marketing
If you are new to the group or want to review the first five parts
the series, click
Questions from Fellow Drillers
Q: Just wondered if you could answer a question I can't ever
seem to find an adequate answer for.
A: I'll give it a shot. I have a pretty good track record so
Q: Are customer loyalty and customer retention the same thing, the
terms are used so interchangeably with one another, I presume they're
not - so how do they differ and conversely how are they similar?
A: They can be the same, in a broad sense. If you don't have
customer retention, you don't have customer loyalty, and vice versa.
I think most "old guys" like me think of customer retention
as the very tactical and targeted to individual customer actions you
take to keep customers on board. "Loyalty" is the end
result of these programs.
Personally, I don't think any customers are
"loyal". They may be loyal at a point in time, but it
seems to me this is more like "infatuation". It isn't
loyalty, which to me implies a long-term affair. Your best
friend is "loyal". Harley Hog buyers are
"loyal", and Harley Davidson is one of the very few
companies that can claim loyal customers in the true sense of the
For most companies, they will be lucky if they can get
"retention" - a short term, tactical idea; never mind
loyalty - a long term, emotional idea. While we're at it,
let's throw in "customer satisfaction". This is the
weakest sister; customers can be satisfied and neither
"retained" nor "loyal". The fact that some
pretty famous "experts" use these words interchangeably
tells you they really have no practical knowledge of consumer
Q: Also, what's the best way to measure customer retention - as
customer satisfaction surveys will never provide a good measure?
A: Retention is really easy to measure if you have direct contact
with customers. There's a ton of stuff about it on my web site -
in fact, that's just about all my web site is about. The fact
you didn't get this message is somewhat troubling to me. Did you
read the tutorials on Latency and Recency? Both are excellent
ways to measure customer retention. See:
If these are too "difficult" for you at this time,
If you don't have direct contact with customers, well, that's
another story completely. I'd have to know more about what
industry you are in and the role you play in that industry.
Describe your situation and perhaps I can help you with more detail.
As far as satisfaction surveys, they can be used as a proxy
for retention if you create a hard behavioral linkage between
the two. For example, do your satisfaction survey, and then
track the retention rates of the actual people in the survey.
If you find a hard match between satisfaction and retention, then
satisfaction = retention, simple as that. You want to recheck
this kind of proxy at the very least each time you have a major change
in product, service, marketing, and so forth. At the high end of
confidence, if you repeated this matching of satisfaction and
retention every year, you could be highly confident it holds true over
Hope this answers your question, and if you need additional
direction, please let me know.
Q: I read your section about how "R" and
"F" are better indicators than "M" which I agree.
But for the problem I face, do you have any ideas on how I can
redefine "F" for my purpose? If not, I can always use
RM, but will face the drawbacks you mentioned in the book which I
think are legitimate concerns for predicting potential value.
(Jim's note: this Driller is referring to the modified RFM model
used in the Drilling Down book. For an overview of what he is
talking about see this
description of what is in the book and this outline
A: Just to ground this discussion, I assume you are talking about
(a major enterprise software company with many products).
You should look for R and F in other places, if "short
term" prediction is what you are after (I'll discuss long
term in a minute). Long cycle businesses like enterprise
software can be more difficult to model because the variables you are
looking to do an RF scoring on are not as obvious. The sales
activity may not be particularly predictive of customer behavior
because the nature of the business precludes frequency of purchase.
For example, think customer service. Where in your
organization would you see RF show up relative to customer
satisfaction? Perhaps at the call center, help desk, or
"outstanding issue" logs of the implementation team?
There could certainly be other areas, depending on how customer care
is set up. The question is: how does the Recency and Frequency
of customer care predict the likelihood of customer defection?
Despite the fact you sell a "product", one could imagine
you are really in the service business. This type of product sets up
(hopefully) a very long Customer LifeCycle
and ongoing service relationship with upgrades, add-ons,
customization, and so forth. Perhaps most of the profit is
really in the ongoing relationship, not the initial sale. If
true, this is where the focus on RF profiling should be.
You want to go where the transactional behavior is, because this
transactional behavior is predictive. So you have to find out
where it is and run your profiling there. For example, once the
installation is over (is it ever over?), what is the Recency and
Frequency of calls for assistance? Does the RF of "trouble
calls" predict the likelihood of additional sales in the future,
or is it a negative predictor - the higher the score, the less
likely a customer is to upgrade? Many times in a service
business, high RF scores indicate negative satisfaction, as you
probably can imagine.
Somewhere in the organization there is transactional data
predictive of likelihood to buy additional services / likelihood to
defect. Your mission (should you choose to accept it) is to
figure out where it is, or if it does not exist, create a way to
Now long term. Over very long Customer LifeCycles, one simply
has to extend the time horizon. Remember, RF is a relative, not
absolute, scoring system, which is why it is useful across such a
broad range of businesses. It compares and ranks activity
between customers, not against an external benchmark. So even
though "frequency" may be every 5 or 10 years, it is still
predictive relative to other customers.
For example (and I certainly don't know your business, so I am
making this up as I go) say there is a "base" package, an
ERP Accounting / Planning / Forecasting module. It's the product
you are well known for and has high customer satisfaction; the product
most companies buy first when they engage in a relationship
Let's say satisfied, best customers tend to add on to this base
module as the years go by. They add Human Resources, Warehouse
Control, CRM, e-business marketplaces,. etc. This may happen
every 3 -5 years. But some customers do it more quickly the
others, and this is where you see high RF scores, as compared with
others who do it more slowly. So you still get an RF ranking,
and you still get predictive power in the model, even though the
transactions are spread out over decades. Your challenge may
simply be this - you don't have data that goes back over decades.
What you want to know is this: once you have identified high
scoring customers, what is it about them that is similar? Is it
who made the initial sale, the type of business they are in,
geography? If you compare high scoring and low scoring
customers, what are the differences? What kind of business adds
on to the base module every 2 years as opposed to the kind of business
that adds on every 5?
Plus, can you use this knowledge to predict defection, or in your
case, a low likelihood of further upgrades? If the top 20% best
(most profitable) customer businesses make their first add-on by year
3 after the initial install of the base module, what does it mean when
a business passes by year 3 and does not add on? Is this a red
flag? Should you send in a "specialist" to find out
why the add-on has not happened? Are they experiencing problems
with the base module which were never documented, or worse, never
fixed? Setting up this kind of "early warning system"
can be very helpful in a customer retention effort - the behavior of
the customer is telling you, flashing a signal, that something is
wrong relative to other customers.
I hope the above answers your question. Long cycle B2B is not
as simple to profile as B2C, but the behavior is still there.
You just have to look a little harder for it. Here's some
additional resources on my site.
The first goes deeper into behavior
profiling for service-oriented
The second reviews Latency, first cousin to Recency and another
"early warning system" metric which for some organizations
is easier than Recency to "sell" internally and
implement. The "didn't add-on by year 3" example above
is a form of Latency
Good luck with it! Let me know if you have
Q: I've been reading your web page and I'm interested in your book.
I do have a question about your model. Measuring repeaters and
Recency make a lot of sense. These are the customers that we
should monitor. If this # (percent) starts to decrease, how do
you know if this behavior is due to being "unhappy" w/ your
company or if this behavior can be explained by current economic
activity? When things are tight, I might have to buy less
frequently....not because I defected as a customer, but rather because
that is all I can afford this buying cycle?
A: It sounds like you were reading the Recent
Repeaters model, one of the most basic models on the site.
And you are correct, when you cannot isolate a variable and have
multiple effects happening at the same time, you can't rely on any
model to tell you what is going on. The essence of modeling
is screening out noise so what you are measuring can be attributed to
a single source. If you change all your product offerings and redesign
your site at the same time, and customer loyalty (% Recent Repeaters)
drops, you will never know for sure if this was because of the new
products or the new design.
That said, the drop is still real and tangible, whether caused by a
weak economy, displeasure with the company, or another variable.
At least if you are tracking Recent Repeaters, you can predict
a future drop in business - whatever the cause. That capability
by itself would be quite valuable.
Did you see the more complex (but still easy to implement) models
on the site? They are covered in the two tutorials:
"Trip Wire Marketing"
"Predicting Customer Value"
These two are generally more powerful than Recent Repeaters, and in
both cases, are about making more money - regardless of what the
economic situation. The first deals with recognizing and
attacking customer defection. The second is about comparing the
potential value of new customers generated by various sources - ads,
products, search engines, etc. The Recency Model is a lot closer
to what is actually in the book. If you want to see a Chapter by
Chapter overview of the book contents spelling out exactly what is in
there, see this page.
Hope this answered your question, and
good luck to you!
That's it for this month's edition of the Drilling Down newsletter.
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If you're in a tight spot on a customer marketing program or CRM
initiative (it just doesn't pay out / can't prove it makes money) and
need some help making it profitable, check out my project-oriented
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
'Til next time, keep Drilling Down!
- Jim Novo
What would you like to
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