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In This Issue:

# Topics Overview

# Best of the Best Customer Marketing Links

# Question - New RFM: Subscription Business?

# Question - New RFM: Conference Business?

# New RFM Metrics: Take 10 on Retention

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Topics Overview
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Hi again folks, Jim Novo here.

Two white hot customer marketing articles kick off this edition followed by two questions from fellow Drillers who are in some of the most difficult to optimize business segments.  These folks are also a bit more advanced than the average reader of the book, but there is a reason I periodically post questions like this.

You may not quite be where they are yet, but I absolutely believe that understanding how **all** kinds of businesses and users look at  these metrics will help you increase your ROI.  Every biz situation is different, and by exposing you to a lot of varied approaches, I'm hoping to trigger your personal "Ah-Ha!!" moment. 

Good To Go?  Let's do some Drillin'!

Best Customer Retention Articles
====================

This section usually flags "must read" articles about to move into the paid archives of major trade magazines before the next newsletter is delivered.  I highlight them here so you can catch them free before you would have to pay the fee.  This cycle there were no great articles from these particular magazines, but there were a couple of great articles from other magazines.  So check 'em out!

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.

***** The 2nd Deadly Sin of Customer Value Management -
 Ignoring the Customer Life Cycle
September 12, 2003  DM Review
Wow, this is great stuff, I guess these guys compete directly with me, though they are publicly traded.  Hey, there's plenty of room in this world for more people to talk about the Customer LifeCycle and how critical it is to the ROI of CRM.  You might want to check out the 1st Deadly Sin here.

**** The 3rd Deadly Sin of Customer Value Management -
Single Factor Optimization
September 19, 2003  DM Review
People always try to bucket customer marketing into acquisition, retention, loyalty, and so forth.  But in the end, in the best customer value management practices, all these pieces of the whole are approached in an integrated fashion.  Just as more traffic does not always mean more sales, your acquisition methods substantially affect your retention.

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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
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Questions from Fellow Drillers
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If you don't know what RFM is or how it can be used to drive customer profitability in just about any business, click here.

New RFM:  Subscription Businesses

Q:  Jim, first let me say that I am enjoying your book VERY MUCH!!  Nicely done, and a nice job of integrating it with the CRM paradigm, 1-to-1 etc... I'm reading very slowly and finished the Latency Metric Toolkit.

A:  Great!  Thanks for the kind words.

Q:  I had a couple of questions on the Latency toolkit and the Latency tripwire, especially as it applies to environments with built in cycles for repeat purchases.  I am in a business where our resources are quarterly based, i.e. customers purchase our resource use them for a quarter and re-purchase the next quarter's resource.  That is, we have a built in pattern, where customers would purchase our resources each quarter.  I was wondering how well I can use Latency with this type of built in cycle or if I would have any problems applying your Latency concepts to it, maybe they apply that much more readily?   In our case we try to call most folks who haven't purchased within 2 weeks of a new quarter beginning.

A:  Right, a subscription-type business.  This is also an issue with utilities and other like businesses who bill about the same amount each month or have contracts for service (like wireless).  The answer is if the revenue generation really doesn't represent anything to do with the behavior, then you simply look for other parameters to profile.  For example, a friend of mine was responsible for analyzing the likelihood of subscription renewal in a business that provided the content online.   Increasing Latency of visit was a warning flag for pending defection, and they triggered their most profitable campaigns based on last visit Recency.  In wireless, the correlations are found in payment Latency and age of phone.

Q:  Also, we have actually built a model with only 3 behavioral variables in it to predict those who would leave before they had actually left so we could do something about it.  The difference here from just calling folks would be that we are predicting who is at risk of leaving (defined by someone who hasn't purchased 2 consecutive quarters).  

From the score, the model lets us sort our customers from hi to low and then we usually take the first 6 groups out of 20 to actually run our test groups (we do use our control groups!).  We usually run the model about 4 weeks prior to a quarter starting to flag the at risk customers, and then run our campaign. One of our questions is timing our use of this where we have a built in cycle of purchasing.

A:  Well geesh, what are you asking me questions for?  You already built it!  And where models are concerned, simple is good!  You obviously learned some things from the book!

Q:  One of the variables was a Latency measure, but instead of looking at the overall customer average and comparing variations from that, we measured each customer's history of purchasing and created a cycle-percent variable that in essence measured what percent they were over their expected time to buy and thus the model incorporated this "customized measure" for each person.  I was wondering your thoughts on this type of measure, compared to the overall customer average of buying cycles in your "Hair Salon" chapter, and also any thoughts on running an anti-defection campaign like this in an environment where we have a built in cycle for repeat purchasing?

A:  Sounds like a good model to me, a bit more advanced than the average person could swallow but you're essentially using the customer's own behavior to set the Latency tripwire for the customer.  Perfect for this kind of business, as long as you have enough behavior history on a customer to be predictive.  If you don't (first subscription), you could always default to the average customer behavior.

As far as campaign timing / anti-defection goes, if I understand the situation correctly, you probably want to time it back from the renewal event, e.g. test dropping the campaign 1 week before renewal, 2 weeks before renewal, 3 weeks before renewal, etc.  Once you get baseline for this, then matrix against "persistence" or your likelihood to renew model and try different offers, if you can.

So, for example, you will probably find out that people who are highly likely to renew require less lead time and perhaps no discount to encourage renewal; conversely, the less likely someone is to renew, the more lead time they need and the higher the discount.  And likely to renew probably roughly correlates with the number of past renewals.  So you end up with some kind of Frequency / Latency matrix that drives campaign timing and offer.

But don't try to guess, test.

Q:  Thanks for any thoughts and input!  I've mentioned your book in a lot of circles as a must read, even before any Peppers and Rogers stuff.

A:  Well, that's pretty good company to be in, though I don't know that P & R ever showed anybody how to do something, I believe they usually tell people **what** they should be doing and charge fees to do it for them.  Nothing wrong with that, I guess that's why they make big bucks and got bought out!

Jim

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New RFM:  The Conference Business

Q:  Jim, could you give me some guidance and opinion on two matters?

A:  I'll try!

Q:  The first is very simple.  I recently attended a seminar in which XXX of XXX Associates  discussed his new technique, Dynamic Segmentation Analysis.  Have you come across this and if so, what are your thoughts on it?  I have found only little information on this subject on the web.

A:  Hmm...sounds like XXX has been reading my web site or book, the use of phrases like "relative lifetime value" and describing RFM as a "snapshot" I believe are ideas unique to my method...at least they were...

He's right about the weakness of of traditional RFM analysis, and the "Customer LifeCycle" portion of my book addresses this directly, by enabling you to track relative LTV on an ongoing basis.  In other words, each donor gets two scores, one based on past giving, one based on likelihood to donate in the future.  Falling likelihood to donate scores, especially among best donors, are a warning sign and this "segment" needs to be addressed in a unique way.  I suspect since XXX only needs "four fields" to do his segmentation and they are the **same ones** I use, he is
probably doing something quite similar.

By the way, I don't have a problem with that, I want people to use the techniques I created.  He also may have come to this position on his own.  If you are looking for information on the topic of using relative Lifetime Value to improve ROI, see my book or this article:

http://www.drilling-down.com/LTV.htm

and these tutorials:   Latency    Recency

Q:  My second point is rather more long winded and I've tried to summarize it as best I can below.   I am looking into whether RFM is suitable for our organization in terms of better marketing our paid for seminars and conferences, given the limitations we have:

The transactional data we hold is, whilst complete, is very limited.  I estimate that even our best customers attend only one paid for event per year.  As I understand it, RFM is best geared towards analyses where there is a large flow of regular data.  Is RFM applicable here?  Do we have enough data?

A:  Well, it would help to know a little more about your product, but in general, if you have long cycle times, you simply score over longer horizons.  I've seem variants of RFM used in the auto business, for example, where the cycle time between purchases is 3 years and the scoring period 10 years.

Q:  Regarding best practice, should we or should we not mix other types of purchase behaviour in with these stats?  For example, we have a publishing arm where contacts can buy books and copies of proceedings.  This of course is a totally different type of purchase activity from buying a place at a seminar or conference.  My feeling is that these transactions should be defined separately in order not to distort the findings.  Any ideas? 

A:  This is really a strategic decision and a LifeCycle issue; it would help to know a bit more about the business.  If it is your intent that conference attendees be targets for retail activity then I would say mix it up.  In reality, there are probably 3 segments: attendee only, attendee / retail mix, retail only.  You are right to think about scoring each by itself if you perceive them to be unique relative to each other and there is no **strategy** that says you want them to cross-over.

My guess is the attendee / retail mix is the most profitable segment, and the others should be cross-sold into this segment.  For example, the purchase of a certain retail product probably is more likely to generate conference attendees than other retail products.  This is the LifeCycle; if the end game (the "back end") is to create customers who buy both retail and conference, then on the "front end", you want to push retail products or conferences that generate new customers most likely to cross over. 

Q:  My third point is a question of who actually owns the transaction.  What I mean by this is that a decision to attend an event is quite often a financial one, over which in many cases the contact has no real control.  He may want to attend an event but his being able to do so is dependent on having the funding from the company.  RFM looks at purchasing behaviour which leads me to question again whether RFM is useful here.  If the purchase behaviour is in many cases actually down to the company's accountants, how can his decision to attend be attributable to him?  Can it?  

Or should we be looking at two models here, RFM behaviour on an individual (contact) level, and a second model that groups behaviour by company, i.e. adding all contacts from one company into one group and then looking at purchase behaviour in that way.  We might then be able to determine (at a rather cruder level) the purchase behaviour of individual companies.  This may for example illustrate that (large auto manufacturer) appears to be more inclined towards paying for events than (another large auto manufacturer).  Of course we could then go further and group each company by SIC code etc. 

A:  Sounds to me like you understand the application of scoring models very well.  Scoring by company is an excellent idea in this case, since there is external control over the end consumer behavior.  You could employ 2 scores, a macro (company) and a micro (person) and use them in a matrix.  For example, a person who is highly likely to attend but works at a company with low likelihood to fund may be a weaker target than a person with medium likelihood to attend in a company with strong likelihood to fund.  This you will only know though testing, but a matrix like this would allow you to discover "score pairs" with highest response to completion.

In addition, within each matrix cell you would have sub-segments - for example, is the person in the country where the conference will be held or would it involve a trip overseas.  Some of this variable may be embodied in the "likelihood to fund" variable at the company level, depending on how you define "company" - as an international entity or on a country by country basis.  If defined country by country, you will probably see "likelihood" rise on conferences in country, and fall on conferences out of country.  Then you have SIC code and so on.  It's important when thinking about these ideas to get the hierarchy correct.  For example, is "likelihood to fund" more or less predictive than SIC code?  This you find out through testing.

Finally, if you do campaign sequencing, Latency would come into play.  For example, let's say you mail a brochure and follow-up with a telephone call, and have limited resources for phone calls - you can't afford to call everyone you mail.  There is some number of days after the mailing where non-responders will start becoming less and less likely to attend any specific event.  You need to find this "trip-wire" point and make sure calling begins prior to it for best response.   On execution, you start the calls with non-responders that have highest macro and micro scores (perhaps a weighted or combined score?) and work down the list.  In this way you allocate the resource to highest and best use, increasing ROI.

Modeling is not a magic bullet, it's a process, a journey.  You take your best ideas based on available information and test them.  The data will tell you when you are right or wrong, and the models are simply a way to create reference points and bread-crumb trails along the journey to increased ROI.

Hope the above was helpful!

Jim

New RFM Metrics: Take 10 on Retention
====================

If you would like to know more about how to use the new RFM metrics to improve your profitability on the web, check out the free "Take 10 on Retention" package I wrote.  It includes a 10 minute presentation on the strategy and reporting behind increasing web customer ROI using simple predictive models.

Here's the idea in a nutshell: when you make investments, you expect the value of them to rise in the future.  You have web investment choices to make - ad design, media, building out content, etc.  Retention metrics tell you which of these investments are the most likely to generate increased profits in the future.

Click here for the Take 10 on Retention

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