PRIZM Clusters Not as Predictive as
Newsletter #81 8/2007
Drilling Down - Turning Customer
Data into Profits with a Spreadsheet
Customer Valuation, Retention, Loyalty, Defection
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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
Sample Marketing Productivity Blog Post
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
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
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
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
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
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
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
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
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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'Til next time, keep Drilling Down!
- Jim Novo
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