Problems Calculating Retention Rate
Newsletter #80 7/2007
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've got a short piece on measuring and acting on
customer retention stats. I chose a short piece because I have
been writing extensively on the blog and elsewhere and wanted to
provide those links, if you are interested in the topics.
Also, a number of fantastic but controversial articles were
published in the display ad and data mining areas I wanted you to have
the chance to read.
And if that's not enough going on, the Web Analytics Association is
driving some live events I will be participating in, so thought I
would let you know about those.
So a short newsletter, lots of links, and a list of coming events
you might want to check out.
Let's get with the Drillin'...
Best Customer Marketing Articles
What Data Mining Can and Can't Do
Peter Fader of the Wharton School with a view of data mining from a
marketing perspective. This has been a pretty controversial
piece with a lot of miners kinda ticked but I think if you take the
time to listen to what he is actually saying you will see the truth in
it - data mining is good for some things and not for others.
Where You Should Stick Your Ad and Why
Joseph Carrabis of NextStage penned this piece is about ad placement on a web page - and how most of the current Display Media placement ideas are just plain terrible or wrong.
Great piece of social modeling, and highly relevant to some of my blog
work, this piece blew some minds over in the ad world.
Beware the Walking Dead
Peter Fader at it again. What we’re talking about here is the effectiveness of cross-sell and retention efforts based on the Customer LifeCycle, and how if you execute too late in the Cycle, you could end up “waking the dead” and driving defection rather than encouraging retention.
These is a full-blown formal research publication attached to this for
those of you who dig seeing all the actual models.
To access the full article reviews and links to the articles
Sample Marketing Productivity Blog Posts
July 2, 2007
I have put off commenting on what is going on with online display
advertising for a very long time, but there has been a string of
events lately that forced my hand, including some major players coming
out and saying "it doesn't work". My take on why it's
broken and how to fix it - if you've got comments to add on my
proposals, I'll respond.
WAA BaseCamp and "Guru Tour"
I'll be giving an all day workshop on Web Analytics for Site
Optimization as part of the WAA
BaseCamp series in Los Angeles on 7/23 and Chicago on 8/22.
More details, other courses and cities for this series are
The Gurus of Online Marketing Optimization Tour is a very
interactive Q & A event put together in conjunction with the WAA
BaseCamp courses. I'll be one of the Gurus on the panel in Los
Angeles 7/24, Boston 8/21, and Chicago 8/23. More
Questions from Fellow Drillers
Problems Calculating Retention Rate
Q: Seasonality has great effects on customers'
purchasing activities in the retailing industry, as you may easily
Q: Furthermore, what you call Latency
has also great effects on their purchasing activities, (I mean, for
example, the customer who purchased a coat in one winter season are
not expected to purchase another until the next winter season and so
A: Yes, but you are profiling customers, not products,
right? The customer who bought the coat may also buy a dress,
shoes, pants in other seasons? Your approach so far sounds a but
too product centric...
Q: Here is the problem, how these issues of
seasonality and Latency must be taken into consideration for
calculating retention rate?
A: Well, you can take it into account or not,
depending on your objectives. What is the objective of the
analysis? If the objective means you should take these issues
into account, then you probably should segment the customer base to do
So, for example, a customer who only buys winter coats - and
nothing else - probably is not your most valuable customer. So
is this segment important to track by itself? How many customers
are in this segment? What kind of winter coats do they buy - the
most expensive available or the cheapest? The answers to these
questions determine the value of the segment, and then based on your
objectives, decide on whether the segment is worth tracking uniquely.
Let's say this segment is fairly large and they primarily buy very
expensive winter coats with high profit margins. This makes the
segment worth tracking by itself. So rather than including them
in the RFM or retention scoring, track them
separately using the Latency metric. If these seasonal coat
shoppers usually buy their expensive winter coat every August like
clockwork, and then you identify customers in the segment who do not
buy the coat in August, you have potentially lost this sale.
Depending on the objectives of the analysis, you can act on this
information immediately to try to get a sale of a coat (or
accessories, if the coat sale has been lost) or act the following year
in a "pre-emptive" way to increase the chances a coat will
be bought from you.
Now, let's say this segment is fairly small and not worth a lot of
money. I would simply ignore them, perhaps even exclude them
from any specific analysis. At some level, the data doesn't
justify the effort, particularly if the segment is so small that it is
not efficient to take any action on.
For example, many companies eliminate 1x buyers or 1x visitors from
critical analysis, because the skew they create makes the analysis
less actionable. A good example of that is RFM scoring - if 50%
of the population are 1x buyers and the population is small, the
result of the scoring is meaningless.
That's not to say you don't track the size of the population and so
forth, but these populations can be so large and so unresponsive they
simply are not worth any kind of specialize marketing approach.
The point is, when you are modeling a customer base, if you have
reason to believe that segments exist with behaviors outside the norm,
find out if they indeed exist, how big they are, and what their value
is. Then determine if this value justifies handling the segment
as a unique group. If it does, then proceed with the best model for
that segment, regardless of what model you are using with the other
This is an ongoing process. The more segments you discover,
the more models you will use, and the more accurate your modeling will
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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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
Copyright 2007, The Drilling Down Project by Jim Novo. All
rights reserved. You are free to use material from this
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