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Problems Calculating Retention Rate
Drilling Down Newsletter # 80: 7 / 2007

Drilling Down - Turning Customer
Data into Profits with a Spreadsheet
*************************
Customer Valuation, Retention, Loyalty, Defection

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Prior Newsletters:
http://www.drilling-down.com/newsletters.htm
-------------------------------

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 Marketing Productivity 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 these and other reviews of recent articles on Marketing Productivity with links to the original articles click here.


Sample Marketing Productivity Blog Post
==========================

Online Brand-ing Series
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.

You can subscribe to the Blog by e-mail if you want to,
 just go to the Blog home page.


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 here.

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 info here.


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 understand.

A:  Yes...

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 forth.)

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.

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 segments.

This is an ongoing process.  The more segments you discover, the more models you will use, and the more accurate your modeling will become. 

Jim

-------------------------------
If you are a consultant, agency, or software developer with clients needing action-oriented customer intelligence or High ROI Customer
Marketing program designs, click here
-------------------------------

That's it for this month's edition of the Drilling Down newsletter.  If you like the newsletter, please forward it to a friend!  Subscription instructions are top and bottom of this page.

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 here.

'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 newsletter in whole or in part as long as you include complete credits, including live web site link and e-mail link.  Please tell me where the material will appear. 

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