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Hi Folks, Jim Novo here.

This month, we've got a question on PRIZM clustering which begs an overall review of using geo-demographic information in general for marketing purposes.  There's a big difference between marketing to the "average audience" and marketing to an individual, a topic  I think many marketers need to learn more about, especially if their background is in media.

Related information is provided by two articles and a blog post.  The articles cover the new kinds of marketing opportunities that arise when marketing to individuals, and the blog post covers more on how to create effective campaigns using behavioral / individual data.

Sound good?  Let's get with the Drillin'...

Best Marketing Productivity Articles
====================

Call it E-RFM

The gist of the article is you can reduce spam complaints and better manage reputation by anticipating which segments of subscribers are going to click the spam button. Yes, anticipate. You know, predict?  For some reason, online marketers seem like they are not really into the prediction thing - or at least are unwilling to fess up to it.  Test, measure, test, measure, web analytics is mostly about history, as opposed to predicting the future. How about predict, measure, predict, measure? Same thing, only much more powerful - if you can guess what customers will do before they do it, you have real marketing power. Perhaps this is why folks don’t talk about it much…

How to do Direct Marketing Testing

“We don’t need testing. We know what works.”  
“If you do no testing at all, no one will complain.”

Arthur Middleton Hughes is one of the great educators in database marketing, and this article hits on several issues that are very well known in the offline customer marketing business, but few folks in online practice. Control groups, half-life effects, best customer segmentation, effects of promotion beyond the campaign.

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
==========================

Will Work for Data
August 1, 2007

Marketing is not always about buying mass media, yet most Marketing people have never had to create and execute a campaign using behavioral data against a behavioral Objective. So they do what they have always done - they create campaigns based on characteristics - and then execute against behavioral objectives using behavioral data.  This is a recipe for sub-optimal performance. It’s like buying a car with a high performance engine then putting the cheapest gas in it you can find...

Questions from Fellow Drillers
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PRIZM Clusters Not Predictive

Q:  I am on an interesting project (and my first DB Mktg one): the client has a large loyalty program, and loves his PRIZM clusters.  However, when I told him a little more about Recency and suggest that we spread all members across based on it, he was surprised to see that his PRIZM segments were not a predictive indicator at all!

A:  Yes, and here is something many people don't realize about PRIZM and other geo-demo programs, including census-driven.  They were developed for site location - where should I put my Burger King, where should I put my mall? They are incredibly useful for this.  However, think about all the sample size discussions in web analytics related to A/B testing, and now imagine what your PRIZM cluster looks like.

In most cases, you are talking about 1 or maybe 2 records in a geo location - what is the likelihood these households reflect the overall "label" of the PRIZM cluster?  Combine this with the fact that for customer analysis, demographics are generally descriptive or suggestive but not nearly as  predictive as behavior and you have a bit of a mess.

Here's a test for you.  It only requires rough  knowledge of your neighbors, so should not be very difficult (for most people!)

1.  What is your "demographic"?

2.  If you were to walk around the block and knock on doors, how many households would you find that are "in your demographic"?

Right.  Maybe a handful, unless you live in a brand new housing development or other special situation.  Now think about walking your zip code, or walking out 10 blocks or so from your house in any direction, and knocking on doors.  Do you find most of these people are in the same demographic?  Did you ever find the "cluster average" neighbor?

We certainly know from web analytics that dealing with "averages" can be very dangerous indeed.  So too with taking a demographic "average" of a zip or other area and tying it to a specific household.  The model falls apart at the household level of granularity.

So now what to you think of all those websites and services that claim to know demographics based on a zip code they captured?

Now, if you think about an e-commerce database, with most records being one of a very few in a zip or cluster, you can see how the cluster demos would really break down at the household level.

Again, nothing wrong with using these geo-demo programs for what they were intended to be used for.  When you are looking for a mall location or doing urban planning they can be very helpful.  But the match rates at the individual household level are poor.

Couple this with the fact that e-commerce folks are usually looking for behavior from customers, and the fact demographics are not generally predictive of behavior by themselves, and you have yourself analytical stew.

Better than nothing?  Absolutely, and for customer acquisition, sometimes all you can get.  Best you can be?  Not if you have the behavioral records of customers.  In fact, what we often see is a skew in the demographics being called "predictive" when the underlying behaviorals are driving action.

In other words, let's say a series of campaigns generates buyers with a particular demo skew.  A high percentage of these Recent responders then respond to the next promotion.  If you look just at the demos, you would see a trend and declare the demos are "predictive" of response, even though they are incidental to the underlying Recency behavior.

I suspect something like this was going on with your client.  Not looking at behavior, over time the client becomes convinced that the PRIZM clusters are predictive, when for some reason they are simply coincident in a way with the greater power of the behavioral metrics.  Given the client has behavioral data, that should be the first line of segmentation.

Q:  After reading you for some years, I now understand how one must be very careful with psycho-demographics.

A:  Well, at least one person is listening!  And now you have seen how this works right before your very own eyes.

I think this situation is really a function of Marketers in general being "brought up" in the world of branding / customer acquisition.  Most Marketers come up through the ranks "buying media" or some other marketing activity that focuses on demographics to describe the customer.  And most of the college courses and reading material available focus on this function, so even the IT-oriented folks in online marketing end up learning that demographics are really important.  And they can be, when you don't know anything about your target.

Then the world flips upside down on you, and now people are looking at customer marketing, and that's a whole different ballgame.  The desired outcome is "action" that can be measured and the "individual" is the source of that outcome, as opposed to "impressions" and "audience".  

In the past, if your tried and true weapon of choice for targeting was  demographics, that is what you reach for as you enter into the customer marketing battle.  Problem is, it's just not the best weapon for that particular marketing engagement.

Jim

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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 Defection here.

'Til next time, keep Drilling Down!

- Jim Novo

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