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

# Topics Overview

# Best of the Best Customer Marketing Links

# Tracking the Customer LifeCycle: Recency

# Questions: ROI in Multi-Step Promotions
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Topics Overview
=============

Hi again folks, Jim Novo here.

This month we've got the usual "best of" Customer Marketing article links, we explore the concepts of "new customer" and "customer" with the owner of IMissAsia.com, and roll up the old testing sleeves to dig down into the depths of control groups, random samples, and halo effects.  If  you're not in the data-driven mood, hey, read it next week - it will still be here.  Take some time off, relax and enjoy the season.  But perhaps you have a bit of time on your hands, as business activity grinds to a halt.  If you are in this camp, 

Let's do some Drillin'!

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

This section flags "must read" articles moving into the paid archives of trade magazines before the next newsletter is delivered.  If you don't read these articles by the date listed, you will have to pay the magazine to read them from the online archives.

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.

Use the Web’s No. 1 Activity- Searching
November 27, 2002  DM News
For those who have been paying attention, search marketing found its way a long time ago.  What better position for a marketer to be in than to snag people actively searching for a product?  Think about it - how do you use the web?  But you can waste a ton of money on search if you don't set it up and track the results properly; if you need help, click here.

Furniture Retailer  Bolsters Analysis With Inhouse CRM
December 5, 2002  DM News
Say it isn't so!  People who signed up for a sweepstakes actually turned out to be profitable mailing targets?  That's unusual, but goes to show what you can do if you actually understand your own data.

Tracking the Customer LifeCycle: Real World Examples
=====================
If you are new to our group and want to review the previous LifeCycle metric - Latency - that discussion is here, along with the Real World examples
Hair Salon and B2B Software.  The previous piece on Recency is here.

Recency: The Web Retailing Example

IMissAsia.com offers an e-mail newsletter on every page of the site.  The owner tries to create a broadly appealing piece, mixing some new content with links back to areas of the site experiencing high activity - specific discussion boards, products, news clips, etc.  

The owner has always felt the visitor / customer should drive the direction of the site; if certain topic areas were getting the most traffic, then those must be the most interesting or attractive topics, and likely the ones with appeal to the most people.  This "swim with the tide, not against it" approach had always worked well in the past as a driver for newsletter content.

Within this content, the owner carefully mixed contextual sales opportunities directly related to the content, along with one or two more aggressive product pitches.  This formula had worked well and the newsletter drove a good chunk of sales.

But the owner of IMissAsia.com was getting worried about response to the newsletter, which has been falling.  Perhaps swimming with the tide was not a good idea, and the content should explore "not popular" issues and products?  Perhaps the product pitches were too frequent and aggressive?  Perhaps this market was just slowing down because of the economy?  Or worse, perhaps the owner had already "creamed" this market and the best days were over?

One thing the owner knew for sure - the percentage of total sales from new customers was falling.  Now, this could be a good thing, the owner thought, because it means more sales are coming from repeat buyers.  But it could be a bad thing, if what it means is the market is saturated and the best days are over.  How to resolve this question?  And how is the newsletter affecting this issue, if at all?

The owner thought a lot about new customers, repeat customers, and the newsletter.  What is a "new" customer, anyway?  Are they new only the day they make a first purchase?  Are they still new if they haven't made a purchase 30 days later?  60 days later?  6 months later?  Do they have to make a second purchase to not be "new"?  When do they stop being new?

For that matter, when is a customer not a customer any more?  If they purchased twice or more and have not purchased again for 6 months, are they still a customer?  What about no purchase in 2 years?  5 years?  When do customers cease to be customers?  What does the customer base of IMissAsia.com really look like?

The owner realized the only way to answer these questions was to actually look at the customer data, and to make decisions on what these ideas meant for this business.  Customer types for IMissAsia.com probably would not be defined the same way as a  customer types for Boeing, Wal-Mart, Oracle, or Ford.  No, these customer definitions needed to be based on the facts of this particular business model.

The owner also realized something else - if there are no definitions, there can be no measurement.  And without measurement, there is no way to understand the dynamics of what is happening to the business, for example, why the response rate to the newsletter is falling.  All the owner knows is one thing, the "what" - response is falling.  The owner wants to understand "why".  And there is no way to get to "why" without understanding the "who" first.

Response to the newsletter is falling because not as many customers are responding.  Who is not responding to the newsletter?  Is it new customers?  Is it repeat customers?  Is it "best customers"?  The owner realizes there is no definition of best customers either.  If these things were defined, the owner might be able to measure and understand what is happening.  Then another realization - not just defined, but tracked over time.  It does no good to define customers and count how many there is of each type; what the owner needs to know is how these counts are changing over time.

And since the specific topic at hand is the newsletter, what the owner needs to do is not only define the customers, but also to define them relative to the newsletter.  What percent of new customers respond now, and over time?  What percent of "old customers" respond, now, and over time?  What percent of "best customers" respond, now, and over time?  Knowing these numbers would almost certainly help the owner understand why response to the newsletter is falling overall.  The owner resolves to address this situation immediately by digging into the data.  

Yes folks, the inevitable Drilling Down...

Next month, we'll follow the owner of IMissAsia.com down the path of defining, measuring, and tracking customer types.  Which group is responsible for the decline in newsletter response, and what can be done about it?  Only the data knows for sure...

If you would like to read the next installment of 
Recency: The Web Retailing Example, click here.

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If you are a consultant, agency, or software developer with clients needing action-oriented customer modeling or High ROI Customer Marketing program designs, click here.  If you are in SEO and the client isn't converting the additional visitors you generate, click here.
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Questions from Fellow Drillers
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Jim's note: If you still don't know what RFM is and how it can be used to drive increased profitability in almost any business, read this.

**Note to readers**: the promotion being discussed below is for an offline retailer using direct mail.  If you're not hip to control and test groups or halo effects, you might want to read this article first.

Q:  I've really enjoyed your book and software.  I'm a business consultant with the University of (deleted).  I'm in the process of developing a Continuing Education class (2 1/2 hour program) on Customer Database Management.  I plan to reference your book extensively and show off your software.  I will be sure to provide attendees your web  address where they can order your book.

A:  Well, I'm thrilled you like it and thanks for the promotion!

Q:  One of the areas I'm working on now, and would appreciate your input (perhaps a future newsletter topic), is what is the best way to organize data (target / control lists) for calculating ROI on promotions. 

A:  Typically this is done with what is called a "promotional history table", which can either be part of the customer record or a unique table keyed by customer ID.  Each promotion has an "ID" and if the customer is selected for the promotion, the promotion ID is placed in the table with customer ID.  So you end up with (to use a spreadsheet analogy) for each row with a customer ID a list in the columns of the row flagging promotions they have been in.  This approach, of course, can create a non-symmetric table and can lead to issues down the line.

Another way to do it, which is more difficult to execute but sometimes preferred depending on what you are doing, is to have (to use a spreadsheet analogy) each row represent a customer and each column represent a promotion.  If the customer was in the promotion, the intersection of customer and promotion is "Y".  If the customer was not  in the promotion, the intersection of customer and promotion is "N".  This keeps the table symmetric and can make querying easier.

Q: I am trying to put together one promotion (for the client I mentioned above) and realized they have to track their promotion target list and control list.  If they begin doing a promotion every month, or every week, that's going to grow into a large number of lists.

A:  Yes, if you use lists.  I can't imagine weekly promotions for this kind of biz....unless they are going to different people each time...  Using the table approach described above might be easier.

Q:  Also, how do you handle a case where a customer was targeted for a promotion, does not respond, and it's time to do another promotion?  Wouldn't that customer be included in the next promotion (assuming that you have not given up, or written off the customer as gone), and if so, how would you handle the ROI calculation(s) if that customer responded to the next promotion in the sequence?

A:  As soon as you mail to that customer again you have "poisoned" the ROI measurement of the original mailing; you would have to cut off your measurement period before the second mailing if you wanted ROI on the original mailing.  But I doubt you will ever be able to measure the true effectiveness of the promotion with such short (weekly) time frames, because you cut-off any of the halo effects the promotion may have generated by stomping all over it with the results of the next promotion.

Now, you might not care about that, and I don't really know what the objective of the campaign is.  But if the objective is to maximize ROI, you won't be able to measure it if you poison the control group so quickly. Wait 30 days if you are just looking to measure the ROI of the promotion itself; wait 90 days if you are looking at the dynamics and ROI of customer retention campaigns.

If your plan is to sequence mailings, that is, "the promotion" is actually 4 successive weekly mailers, and you are measuring that against control, then you can mail every week and mail to whoever you want, as long as it isn't people in control.  You can set up a "decision tree" if you want that says "if they don't respond to #1, send #2 the next week; if they do respond to #1, skip a week and send #3 in week 3, that kind of thing.  

Realize this though:

1.  You will not be able to measure the effect of any one mailer or decision tree sequence, only the effect of the entire promotion.  So you end up with a lot of work and you don't know what was effective. 

2.  You can measure the effectiveness of an individual piece or sequence, you just have to set up control for it at each new branch or step, for example:

Mailer #1:  Initial mailer, total  = 1000
Test: 900 are mailed
Control: 100 are held back

You get results of:  Non-responders: 500  Responders: 400

Mailer #2: Re-mail non-responders = 500
Test: 450 are mailed
Control: 50 are held back

Mailer #3: Re-mail responders = 400
Test: 360 are mailed
Control: 40 are held back

So for a simple 2 step mailing, you have 3  control groups and 3 test groups, and you can measure the effectiveness not only of the total campaign, but each piece of it.  If you don't set up control at each step, if the overall campaign is successful, you won't know if it came from initial response or the re-mails of responders or non-responders. 

The ROI you asked about above would be the ROI of Mailer #2 - did not respond to initial mailer and were mailed again.  As long as control is composed of other people who received the first mailing and did not respond, you should be able to measure ROI very accurately for this sub-segment.

Jim

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I can teach you and your staff the basics of high ROI customer marketing using your business model and customer data, and without using a lot of fancy software.  Not ready for the expense and resource drain of CRM?  Get CRM benefits using existing resources by scheduling a workshop. 
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That's it for this month's edition of the Drilling Down Newsletter. If you like the newsletter, please forward it to a friend - why don't you do this now while you are thinking of it? Subscription instructions are at the top and bottom of the newsletter for their convenience when subscribing.

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 right here.

'Til next time, keep Drilling Down!

- Jim Novo

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