Introduction to Geodemography
Internal release notes by R.Bruce Carroll
Sr.VP The Polk Company - Data Engineering/Marketing Technologies Group
Geodemographic neighborhood classification systems have been around since the mid-1930's but widespread commercial applications really only began in the late '70s and early '80s, principally with the launch of the PRIZM system by the Claritas Corporation in the United States.Since that time, cluster systems have been adopted by most major consumer marketers, including financial institutions, retailers and automotive manufacturers in North America, Europe and around the glove.Approximately 22 countries have already been "clustered".Cluster systems have become the established "lingua franca" of marketing.
Most marketers are familiar with the basic tenet of geodemographic neighborhood classification systems; People with similar cultural backgrounds, means and perspectives naturally gravitate toward one another - or "cluster" - to form relatively homogeneous communities.(It's the old "birds of a feather flock together" phenomenon.) Once settled in, people naturally emulate their neighbors, adopt similar social values, tastes and expectations and, most important of all, share similar patterns of consumer behavior toward products, services, media and promotions.This behavior is the basis for the development of classification systems such as MOSAIC, ACORN and Compusearch's PSYTE system in Canada, all of which classify neighborhoods and their households into clusters of groups of neighborhoods, based on their underlying socio-economic and demographic composition.
It's not uncommon for some people, on their first exposure to cluster systems, to debate the underlying homogeneity of neighborhoods and the resulting linkage to consumer behavior."I am not like my neighbor" is a common response.I suppose the argument starts with a misunderstanding of what homogeneity means in the context of spatial demography.Because our marketing perspectives have long been focused on univariate demography, we tend to define homogeneity in a vertical context, expecting everybody living in a given neighborhood to be identical in order for clusters to work - i.e.every cluster should consist of all young families or upscale singles, all executives or hard hats, all rich or poor.
But these are univariate criteria, used over the past forty or so years to segment and target mass markets.Clusters, however, are a multivariate creation, designed to segment and target neighborhoods.Obviously, all residents in any given area, however small, are not identical.In an urban neighborhood, the older wealthy gentry may live a block away from the welfare recipient.In a rural neighborhood, the gentleman farmer may dwell amongst hardscrabble farm workers.Such are the realities of community structure, which in turn provide the building blocks of any cluster system.Homogeneity, as used in geodemographic cluster technology, simply means that all neighborhoods within a given cluster will share highly similar neighborhood lifestyles and predictable consumer behavior.
Theoretical debates aside, cluster systems have already proven themselves where it counts - in the marketplace.At a conservative estimate, more than 20,000 companies in the United States and Canada alone used clusters as part of their marketing information mix last year.This kind of acceptance doesn't happen unless the effectiveness of using clusters can be measured and tracked, season after season, year after year.Marketers simply don't pay for something that doesn't work.
There are many reasons behind the enduring and even increasing popularity of generic geodemographic cluster systems.Here is my personal list of the major contributions I believe geodemographic clusters have made to modern marketing.
While it's true that cluster systems often cannot match the discrimination produced by highly customized statistical solutions - which use Chaid and other forms of regression on a specific data set with good unit-record type data - they are certainly superior to most univariate demographic measures such as age, sex, income, etc.These simplistic measures are still favored by too many marketers and media but they are in fact obsolete in describing modern consumer behavior.
Moreover, cluster systems can capture the different "franchises" or behavioral components of a product's user base whereas demographic measures tend to homogenize consumer profiles into a simplistic caricature.For example, women, 18 to49 is a profile that could be applied to thousands and thousands of products.
To illustrate this point, take a look at the cluster profile of the Mazda Miata (attached).The clusters are ranked in terms of their Cluster Group sequence, urban to rural.The index on the left shows the sales penetration of the Miata for each cluster, compared to the national sales or buy rate.An index of 300 means that households in that cluster buy at three times the US average.The variance in buy-rate across the clusters certainly makes the point about the discriminating power of clusters.But the even more important thing to note here is the complexity of the profile.There is a decidedly up-market bias in the buyer profile at a group level but not necessarily within each group.In looking at, literally, thousands of cluster profiles, I've seen very few products where more than one "franchise" has not been identified.Simple demographic measures, in contrast, can't capture the bi-model or tri-model consumer profiles that often exist within a product's consumer profile.
This profile and other like it demonstrate the evident link between social structure and consumer behavior - which is the basic commercial promise of geodemography.More than anything else, however, these profiles illustrate the extraordinary diagnostic, predictive and motive power of PSYTE>
Medium of Integration
This is a clustering technology's marketing forte.You build a consumer target by profiling your own customer files or you can use a profile of your particular product or service, using any number of syndicated databases such as SMRB.You can then compare or correlate that profile to dozens of databases that will be coded with PSYTE.After you have a good idea which cluster targets you want, you can then rank TV programs and/or dayparts, target out-of-home advertising, select names from a mailing list, rank telephone exchanges and carrier routes, and target retail distribution, all using the same target definition.
This is what I mean by describing clusters as a medium of integration.And it is one of the principal advantages that generic cluster systems have over customized segmentation systems.There is no need to change the description of your target simply because the marketing medium and the select option have changed.Let me emphasize this crucial point: you can take the same cluster target you used for placing outdoor advertising and used to target television, radio and newspaper buys, or to order names from a mailing list or to select retail sites for selective promo drops.A former partner of mine used to call this "cluster bombing" and it's a pretty impressive process when executed properly.
There is really nothing new about market segmentation.Everyone knows that different kinds of people consumer different products, and marketers have been segmenting for years.But the real advantage of cluster segmentation is not in segmenting per se but in being able to hit the target, once defined, and in being able to concentrate all elements of th4 marketing mix against this target.
The results of cluster targeting can be easily measured.Remember, the basic unit for geodemographic targeting is every ZIP, Tract, Block Group, Zip-4 code in the country, which has been assigned t one of the 65 clusters.To see if cluster targeting worked, a client simply has to track his sales, shipments, subscriptions or whatever by one of the above units of geography and summarize them up to each of the sixty five clusters and see if sales have, in fact, increased in the targeted clusters.
Even more accurately, marketers can determine whether sales increased more in those clusters than for the market overall - or, in a declining market, whether sales declined less in targeted clusters than for the market overall.As marketing dollars come under ever-closer budget scrutiny, marketers will embrace anything that can reliably measure the success or failure of a program.
Longitudinal/Time Series Analysis
This is perhaps one of the least appreciated and underutilized benefits of using cluster segmentation to analyze consumer behavior.PSYTE is what I call a fixed segmentation system; it does not change because the database it is being applied to changes.This means that marketers can analyze their sales going back three, four, five years, whatever, along with the changing structure of their consumer franchise over that time period and see how their market has changed by cluster.In short, PSYTE delivers the ability to track market share for groups of products or individual products on a cluster-by-cluster basis, both at the national and the individual market level, month over month, year over year.
Most marketers know their sales at a national and market level and even sales by the branch or retailer within a market.But they usually do not know the demographic constituency of those sales on a market-by-market basis or within a retailer's trading area.And equally important, they do not know the evolution of those changes at a small area level over time.
Imagine being able to answer or at least consider these questions:
When we increased the price of our product two years ago, did we alter the demographics of our profile? If so, was the change uniform across all markets?
When we stopped using rebates one year ago, did we alter our consumer profile?
What was the effect on our customer profile when our main competitor doubled their investment in spot television last quarter?
Or imagine you are a national marketer and you have found over years that your success was determined by how well you had penetrated the old, suburban gentry market (e.g.PSYTE Groups S2).These clusters represented your core constituency.If you lost customers here, you were lost.Wouldn't you want your MIS department to give you a report of monthly sales in these clusters, not only nationally but also n a market-by-market basis, to use as a barometer of how business is doing nationally and locally?
Here's an actual example of what I'm talking about.A well-known marketing vice-president of an automotive company insisted on receiving reports of his company sales in two key clusters in his top 20 markets every month.If sales started to go down in either of these two clusters, which he considered to be "leading indicator" clusters, he ordered an increase in local ad expenditures.In effect, he was using cluster analysis to build or protect "micro share" in order to maintain his national share.I'm aware of a Canadian packaged goods executive doing something similar by tracking his market share in selected clusters on Nielsen's NEDS panel.
Addressable, Mappable Targets
We often use the phrase "I see what you're saying" to mean we understand something.The beauty of a cluster-based targeting strategy is that it can be found on the ground - it can be mapped! Using a desk-top GIS mapping system, you can illustrate targets at any level, right down to individual postal walks, proprietary distribution/sales zone, grocery store trade areas, etc.You can map clusters that show increasing and/or declining sales; map response rates from a coupon drop or mail campaign.It's difficult to do this with a disembodied cross tab like, Woman, 18-34,HHI $75,000+.
Your data can be visualized, which means they can be used - and used more easily by more people in the organization.PSYTE takes information out of the hands of the few and puts it into the hands of the many.Clusters are "executive friendly," too.Even company presidents not well known for their facility with statistics and market research can "see what you're saying" when you present them with a cluster profile and/or map of your customers.Clusters are just plain easy to understand.
Where Geodemography meets Database Marketing
Over the last six or seven years we have heard and read a lot about how 'relationship' or 'one to one marketing' was going to replace marketing to groups, broadly defined by demographic and/or geodemographic segments.And there is no question but that marketers are increasing their investments in this new marketing paradigm.But, as with most trends, the case is usually overstated.The fact is, most mass marketers don't have products that can be profitably marketed directly to the consumer.Their main distribution channel is not the mailbox or an Email address but the retailer ...the channel is everything.For them the game is won or lost on the grocery or retailer's shelf, the dealer's lot, etc.Mass media targeted to large, sustainable consumer segments is still the engine of choice ...market share is still the barometer of marketing success.
However, while marketers may be resisting the charms of direct consumer selling, they are still acquiring as much data as they can on the individual buying and shopping habits of their customer.But they're doing this so that they can do a better job of doing what they have done for years.Instead of relying on limited sample data to determine the profile of different segments of their customer base, they are using millions of records of actual purchase data.Instead of treating every retailer location as though they were equal in value, they exploit customer data to calculate the correct trading area of every retailer location so they can match distribution and local promotion efforts to the geodemographic lifestyles of each area.This whole process, variously called "site-based category management" is probably done better with clusters than anything else for all the reasons noted above in that, fundamentally, it ties behavior (profiles) to geography (location).
As I said earlier, this is my personal list but it is by no means a complete summary of the marketing applications of geodemographic segmentation.Clusters can also be used as variable in customized direct response and site modeling, in positioning and targeting new products, for creative message targeting, and for projecting future market penetration and share.