The American dream depends on what mirror you're looking into.
That ageless bit of wisdom -- a commentary on our diversity as a people -- is being tested by a new breed of high-tech market researchers as they work to identify groups of like-minded consumers.
Marketing executives long have been frustrated by the fact that neighbors who are the same age, have the same number of children, similar houses and the same household income, quite possibly have polarized views of how to buy their way to the aforementioned dream.
But thanks to high-tech research tools, more businesses are unraveling the demographic knots and delivering their messages directly to neighborhoods within neighborhoods, and with increasing frequency, to individual consumers.
"The businessman is so squeezed today on profits that he cannot afford to just go out and mass-market," said Gary Hill, president and chief executive of Claritas/NPDC, one of the leaders in the new data-based marketing technology.
"A mass market has to be built around a lot of small markets. Target small areas first, then put together a mass-marketing strategy. We call it precision marketing," he said.
As far back as the 1950s, marketers have talked about the foolhardiness of treating consumers as a predictably needy monolith. But only in the last 10 to 15 years, as society has become increasingly fragmented, have researchers been able to classify households based on a mind-boggling array of characteristics.
Those skills help them dissect neighborhoods so they can better predict what their inhabitants will put in their garages, in their stomachs, on their coffee tables, in their closets and on their charge cards.
Aiding their efforts are computer-driven technologies that can narrow down target audiences so efficiently that consumers can be approached on a one-to-one basis. As recently as the early 1970s, marketers were hard-pressed to divide their customers into groups of less than 1,200 households -- the average size of a federal census tract.
Later, in the early 1980s, they were able to break that down to neighborhoods as small as 300 households. Today, some marketers are experimenting with technology that allows them to focus on areas as small as eight dwellings.
By combining demographic information obtainable from the U.S. Census Bureau with commercially generated data on buying patterns and lifestyles, researchers are at the threshold of "relationship," or one-to-one, marketing.
Imagine being able to pitch a luxury car to only those few who possess the taste and financial means to buy one . . . and whose existing car has over 50,000 miles on it.
Until the late 1970s, consumers were categorized by standard demographics only, such as income, number of children and home ownership.
The problem with that model was that marketers could not differentiate consumers by social rankings, according to Thomas Dailey, president and chief executive of Spectra Marketing Systems Inc. in Chicago.
For instance, a unionized plumber and a professor may each earn $60,000 a year, have three children and both own their homes, yet lead entirely different lives.
"Marketers asked the question: 'I like these people in Winnetka (Ill.). How do I find more neighborhoods like that?' But it was impossible to do," Mr. Dailey said. Impossible, that is, unless a company happened to know that Westchester County in New York and Fairfax County in Virginia were similar to Chicago's North Shore. Today, a marketer can sit at a computer and in a couple of minutes, at a cost of a couple of dollars, call up every neighborhood in the country that is similar to the North Shore.
Some call it the "clustering of America." The innovation was created in the early 1970s by Jonathan Robbin, a social scientist and computer whiz, who spearheaded computer-driven marketing techniques called geodemographics.
His goal was to break down the country into neighborhood-size markets that could be classified based on a wide variety of demographic characteristics. Using magnetic tapes from the 1970 census, Mr. Robbin started grouping census information by ZIP code, thereby making it easier for marketers to target consumers.
He created Claritas Corp. in 1971 and launched his "cluster" system of U.S. neighborhoods for use by marketers. Three years later, he unveiled 40 types of clusters based on social rank, mobility, ethnicity, family life cycle and housing style.
Claritas' PRIZM Lifestyle Cluster System cuts like a razor: At the top are "Blue Blood Estates," comprising "wealthy, suburban older families, 35 to 54 years of age, white collar and college grads."
Residents there are more likely to buy a new convertible, go to stage plays, play golf 20 times a year and belong to a country club.
At the opposite end are those classified "Public Assistance," described as "poor, solo-parent families in rented or public housing, blue-collar service workers, 18 to 24 and 65 and older."