Sponsored Content WEBINAR: ELECTION 2008: E-mail Tips from a Political Insider   WEBINAR: Becoming the Illusionist: The Magic of Using "Brand" to Drive Response without Results Disappearing in the Process   WEBINAR: Score and Win Through Cooperation   TOOLS OF THE TRADE: Holiday's are prime-time for email marketing - maximize your ROI by getting to the inbox   ON DEMAND WEBINAR--MASTER CLASS: High Performance Online and Off-line Strategies for Large Scale Customer Acquisition   FREE WHITE PAPER - Direct Mail and the Internet Find any supplier you need - agencies, CRM, fulfillment, lists, e-commerce, paper, printers, telemarketing, and more. Featured Categories Lists and Data Telemarketing Database Marketing E-commerce Web Marketing Agency & Creative Services Print, Production & Paper Lists and Data Processing :: view all categories Get free access to more than 50,000 list data cards - one of the most comprehensive databases in the industry. >> Search Now DIRECT Buzz Blog Webinars Buyer's Guide Calculators JobZone ListFinder Promo Calendar Research Store E-Newsletters Print Magazine This Month in Direct Magazine Bare Bones Postal reform works. The rate hike that takes effect May 12 is the lowest in memory: an average of 2.88%. And some mail classes are getting even less than that... See Full April Issue

While geo-demographic data can play a major role in a targeted marketing program, its misapplication can cause poor targeting decisions and a waste of financial resources.

To effectively incorporate geo-demographics into your company's targeting, it's important to understand what this data is, its advantages and disadvantages compared with other data, and the ways direct marketers have successfully applied it.

There are several types of data available to DMers. One is behavioral, which defines the relationships that you have with your customers and inquirers. Examples include:

• Gross demand history, such as orders and items.

• Post-demand history, including returns, exchanges and allowances.

• Promotional history, such as mail, e-mail, outbound telemarketing and personal visits by field salespeople.

• Customer service history, including complaints about delays in the resolution of back orders and damage to merchandise during shipping.

Another type of data is overlay demographics. Some examples:

• Age, measures of income and wealth, presence of children, marital status, and race and ethnicity.

• Interests, such as golfing, skiing, crafts and reading.

• Life stages, such as families with young children and empty nesters.

• Dwelling information, such as owner vs. renter, multifamily vs. single-family residences, and urban vs. suburban vs. rural.

There are two basic permutations of overlay demographics. The first is individual/household-level data. As its name suggests, this information describes individuals and households. The second is geo-demographics, or data specific to units of geography.

An important source of geo-demographic data is the several hundred elements that come directly from the 2000 U.S. census. This data is available for the 211,827 block groups and 66,438 census tracts that were defined for the census report. Also, some data-enhancement companies have taken their proprietary pools of individual/household-level data and aggregated them to block groups and census tracts.

Geo-demographic data also is available in postal units such as ZIP codes, carrier routes and ZIP+4 codes. There are about 43,000 ZIP codes and 603,000 carrier routes in the United States, as well as tens of millions of ZIP+4s. Also, some of the data-enhancement companies have translated both the census data and their own individual/household data into postal units.

It's common to aggregate geo-demographic data into “super-variables” such as clusters. There are quite a few commercial cluster products on the market. Also, you can create your own custom clusters that are tailored to the specifics of your business.

While geo-demographic-level data provides almost universal coverage, the same is not true for individual/household data. Age, income and length of residence are among the only individual/household elements for which coverage typically exceeds 75%. And the extensive coverage for income is because most of the data, instead of being actual, is an estimate generated by statistics-based predictive models.

Most other individual/household data elements display coverage well below 75%. Self-reported data — such as lifestyles and interests, for example — generally cover no more than 30% to 35% of the population, and rates for some elements are well under 10%.

Another advantage of geo-demographic data is that it is usually less volatile than individual/household-level data. Volatility occurs because of changes in the underlying sources. Sometimes a compiler will replace one or more original sources, either in whole or in part. Other times a source will be pulled off the market. Volatility has increased in recent years with the widespread concern for privacy and the corresponding passage of restrictive legislation.

Volatility is a particular problem when doing statistics-based predictive modeling. Often, changes in the coverage and/or distribution of the independent (“predictor”) variables result in the premature degradation of a model's effectiveness. The only way to counteract this is to carefully monitor the model and either recalibrate or rebuild it whenever volatility becomes apparent.

Still another advantage of geo-demographic data is that it allows direct marketers to access information that cannot, by law, be supplied at the individual/household level. Credit data and other sensitive financial information are important examples.

Traditionally, a disadvantage of geo-demographic data compared with individual/household information has been its relatively modest predictive power. However, many would argue that the “power gap” has decreased in recent years with the rise in privacy concerns and the corresponding passage of more restrictive legislation targeted at individual/household data. In some instances the result has been a decline in coverage and the replacement of “actual” data with modeled (“inferred”) elements. Ironically, a significant driver of many predictive models to create inferred individual/household elements is geo-demographic data.

Generally, behavioral data is the most predictive of future customer and inquirer behavior. In fact, when a company has the benefit of robust behavioral data on its customers, it typically is difficult for demographic overlays — in either their geo-demographic or individual/household forms — to add any cost-effective predictive power. Therefore, behavioral data is considered by many DMers to be more important than overlay demographics. Nevertheless, overlay data including geo-demographics can play several key roles in sophisticated targeted marketing.

Overlay demographics is the only data available for prospect segmentation where, by definition, behavioral data pertaining to your company does not exist. Also, overlay demographics can cost-effectively supplement the limited amount of behavioral data available for predictive models to segment single buyers (those who only made one purchase) and inquirers.

Occasionally geo-demographics can be almost as predictive as individual/household overlay data, and at a favorable cost. Generally with geo-demographics, the more granular the data the more predictive it is.

The most granular of all is ZIP+4 data. On average, there are four to six households per ZIP+4. Therefore, it's no surprise that DMers have had success making projections using ZIP+4 data.

Back in the 1990s, for example, one telecommunications company employed ZIP+4 predictive models to drive “win-back” programs targeted to customers immediately after they'd defected to the competition. The environment at that time was one in which prices were falling rapidly, and the most profitable customers often were those whose inertia had resulted in their remaining in older, no-longer-competitive service plans. Many of these customers were moved to switch to the competition by price-competitive offers. In such an environment, an immediate matching offer could stem the attrition rate and retain a significant portion of customers.

ZIP+4-level models were constructed to drive virtually instantaneous win-backs. Every ZIP+4 was scored regularly and assigned to a win-back offer track. With each customer defection, his or her ZIP+4 immediately triggered the appropriate track.

At the other extreme of geo-demographic granularity are tried-and-true ZIP-code-level models for segmenting rental lists. For all but the largest mailers, it's difficult to secure “net/net” list rental arrangements for traditional mail-order-responsive lists, where only the names that are mailed have to be paid for. Instead, “net” arrangements are the most common, where a certain percentage of names have to be paid for whether or not they're mailed.

The traditional industry standard is an “85% net” arrangement where credit can be received for no more than 15% of the gross names that are rented. Typically, the net rates out of a merge/purge are less than 85%, which consumes the entire 15% credit. Therefore, every post-merge name has to be paid for whether it's mailed or not.

The way around this is to build ZIP-code models. The output of such models is a list of ZIP codes to be employed for selection or omission prior to the shipment of names to the merge/purge shop. It is easy for list managers to process such a list. Typically, “run charges” for ZIP selections or omissions are no more than $5 or $10 per thousand.

A way to enhance ZIP-code prospect models is to supplement census-derived and other forms of commercially available data that are the primary drivers of such models. This is accomplished by taking individual and household behavioral data on your own customers and inquirers and rolling it up to the ZIP code.

Beyond predictive modeling, geo-demographics have descriptive qualities that can be leveraged in important ways to enhance any targeted marketing effort. For example, geo-demographics can be excellent profiling tools for tailoring promotional content, a process known as directive messaging. With some creative marketing, the possibilities here are endless. For example, by employing outbound telemarketing, existing customers can be targeted for cross-sell offers based on scripts tailored to various target audiences. The idea is to employ geo-demographic and individual/household demographic data to produce more effective conversations.

In terms of operational complexity, directive messaging is perhaps most easily implemented in e-mail. Next is telemarketing, assuming your company has access to the customizable screen-scripting technologies that frequently are part of today's call center infrastructure. Often the most operationally complex is direct mail, but if your product or service lends itself to digital printing, the process is considerably more streamlined than it once was.

---

Jim Wheaton (jim. wheaton@wheatongroup.com) is a principal and co-founder of direct/database marketing company Wheaton Group, Chicago.