Market segmentation is one of the truly powerful tools to come out of the big data revolution, but in too many instances, its promises fall far short of its capabilities.

Many businesses use data sets with huge gaps or suspect accuracy, undermining the foundation of their market segmentation strategies and making it much more difficult to achieve actionable data about smaller and smaller subsets of customers.

Our recent partnership with LiveRamp shows what can happen when a powerful library of consumer information is combined with comprehensive demographic data reported at the zip+4 level.

Thousands of businesses across the country have used LiveRamp to gain market intelligence on consumer interests. LiveRamp uses non-digital and digital data, such as magazine subscriptions or website cookie, to identify key market segments and target consumers based on their previously identified preferences. Retailers and marketers could target, for example, readers of a specific magazine, or people who have shown interest in luxury cars.

Powerlytics’ data can take those insights one step further, by telling marketers, for example, whether potential customers have the disposable income to pay for their products. It might be one thing to have a list of people who have shown interest in an Adrenaline Red, V10, 645 horsepower, 2017 Dodge Viper ACR. It’s another thing altogether to know which of those people have the power to purchase the $118,000 sports car.

Powerlytics sharpens leads with the industry’s most accurate data set of consumer financial demographic information. Marketers can target prospects based on proprietary information derived from administrative records of the U.S. Government, down to the zip+4 level.

The zip+4 is an extremely granular geographic area. There are, for example, roughly 40,000 zip codes, and more than 40 million zip+4s. The zip+4 on average has 3-4 households in it while a single zip code might include hundreds, or thousands of people, and is likely to include homeowners of wildly different tastes and demographic profiles.

What does that mean for businesses?

Targeted, personalized offers, such as emails, have much higher open rates than “spray and pray” marketing campaigns. Some research suggests that personalizing an email can nearly double the rates at which they are opened.

Now businesses can target residents who report a high proportion of income from interest, dividends, or capital gains with a high degree of confidence that they are reaching consumers whose incomes are derived from wealth rather than work. That information could be used in deciding where to locate luxury stores, and which areas might be fertile ground for investment advisers.

Imagine, for example, that a customer requests information through your firm’s website. Using ISP data, you could determine that individual’s likely income, and its components – wages, income, investments. You could then route that prospect to a representative familiar with their needs – for example, someone who deals with high net worth individuals.

But Powerlytics data isn’t confined to measures of wealth. Available information includes the number of people in a zip+4 who receive alimony, and the average size of the payments, IRS distributions, business and income loss, disposable income, non-wage income, ordinary dividends, pension and annuity payments, retirement income, social security payments, taxable interest, taxable refunds, wages, household wealth, student interest, mortgage expense and nearly 200 other household financial items.

Market segmentation works best by tapping the most complete data sets and combining consumer preference data with the reality of their economic life. With those resources, marketers, financial institutions, insurance companies, and policy makers can answer questions that other datasets don’t and more finely tune their segmentation strategies to connect with their customers.

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