Advertisers looking to reach affluent consumers typically have several options, but they’ve always struggled with the limitations of those options. They could buy print advertising in publications claiming to have affluent readership, such as Vogue, The New York Times, or Condé Nast Traveler. They could make TV buys during large events, such as the Super Bowl, hoping that the scale of those events brought a good percentage of affluent viewers. Direct mail presented a slightly more precise tactic, letting marketers send offers to homes in affluent ZIP codes, or to the subscribers of the previously-named publications.
These tactics may hit the target audience, but it was never clear as to how precise they were, or what kind of waste came with making scale buys. Even buying media in outlets aimed strictly at the affluent can result in exposure to aspirational consumers who may not have the purchasing power for a brand’s products or services. Some brands want to reach this audience, but others do not. But none of that mattered, because alternative options typically didn’t exist.
If a marketer’s goal is for 100% of their media dollars to hit an affluent audience, they want as much information about that audience as possible. Of course, there are limitations; marketers don’t have access to consumers’ personal financial information or history. So, while the affluent may have significant estimated income and assets that help marketers identify them, that Personally Identifiable Information (PII) is off limits.
Fortunately for marketers, there have been major advancements in the type of data they can use. One of the best options for segmenting audiences by buying power is estimated financial data, which can give marketers a more precise view of their target audience than is available through other media buying options. Most importantly, this respects consumer privacy and confidentiality by avoiding any use of PII.
The key here is the balance of granularity, predictability and anonymity built into the data. It is possible to use very granular, aggregated yet anonymous, financial information to better understand and reach an affluent audience.
While marketers can’t know exactly how much consumers have in their respective bank accounts, they can gain a sense of estimated financial capacity, disposable income, and assets. They can help identify the affluent, mass affluent, and the emerging affluent, and even further segment to focus on subsets within certain income brackets.
Armed with this information, marketers can not only better reach the affluent, but also adjust their message depending on the type of affluent consumer they want to reach. For example, auto marketers can more effectively pinpoint which audiences are likely qualified to buy a luxury model, then drill down to find out more about which models appeal to which qualified groups. One geographic area or customer segment may have a higher likelihood to buy a hybrid car, while another may be more likely to lease, with little interest in hybrids.
This kind of powerful insight helps make the data actionable. Armed with this type of information, the marketer can better adjust campaign creative for different audiences. By further factoring in additional third party information, they’ll know the places to buy media aimed toward their target audience, rather than simply guessing based on what the media company tells them about their audience.
Today, more effective prospect segmentation and identification that adheres to confidentiality and privacy best practices is possible in our big data era. Marketers looking to approach the affluent have more options than ever when it comes to reaching their desired audience and limiting waste.