As an industry, we have tended to focus on demographic targeting because it’s straightforward. Homeowners in Florida don’t need snow blowers, and teenagers living in Kansas prefer wireless headphones over snow blowers, no matter how much snow is on the ground. But psychographic targeting—marketing to people based on their attitudes, lifestyles, and preferences—can be even more effective. Yet psychographic targeting generates a lot of apprehension. How do we obtain the data? Can we really trust it?

It used to be that if you wanted to do psychographic targeting, you had to have the data and do the analysis and profiling yourself. Today, the options for purchasing this information are exploding. Companies like Experian and Acxiom have lots of data to sell you. But does psychographic targeting work? Yes. A 2015 study by Michael Kosinski, for example, found that by studying what people like on Facebook, an algorithm can actually predict what consumers will do with more accuracy than a human being, even someone they know well.

For example,

  • With 10 likes, the algorithm can predict better than a colleague.
  • With 70 likes, it can predict better than a roommate.
  • With 150 likes, it can predict better than a family member.
  • With 300 likes, it can predict better than a spouse.

That’s creepy, but it makes the point. Psychographic targeting works. To make it less creepy and more accessible, some data companies focus on the broader concept of personas, or the broader buckets that people fall into. Acxiom calls them “clusters.” Here is a common set of “cluster demographics” I input into the cluster creation tool on Acxiom’s website:

Age: 36–65
Income: $120,000 +
Kids: School-age Kids
Suburbs & Towns
Net Worth: $100K–$1MM

Consumers falling into this category are placed into Cluster 7: Active Lifestyles, which is described this way:

Active Lifestyles is made up of wealthy couples with older children and teens, driving the SUVs needed to move them and their gear. Their kids are driving now, too, which means additional vehicles at home. These parents are planning for the future, saving money for college expenses and taking out disability insurance. Before the kids head off to college, though, they enjoy the here and now with casual, family-friendly activities like cooking out, watching movies and playing outdoor games. They also stay active with deliberate exercise and high-energy recreation.

If you have customers selling SUVs or disability insurance, you probably want to purchase a cluster like this.

If, however, the cluster is changed to single rather than married, the cluster changes to Cluster 13: Work and Play. This cluster is described like this:

Work & Play households are all unmarried parents with mixed-age children. They rank above average for income and net worth. They are devoted to sports that they can either pursue individually for fitness or enjoy with their children, such as playing basketball or volleyball. Work & Play shopping, media and travel all reflect the mix of ages in the household, which can range from toddlers to teenagers. These parents buy family necessities, as well as electronics such as game systems, TVs and cell phones, and name-brand clothing and shoes for their kids.

This is fascinating stuff, and I could spend a good part of my day just creating new clusters to see what they would look like. For clients, however, the goal would be to work backwards. Look at the clusters first (in Axciom’s case, all 70 of them), then purchase the cluster that makes the most sense for your clients’ marketing goals.

If marketers have always wanted to crawl inside consumers’ brains, now they can. Why should these benefits be restricted only to online marketing? Printers and their customers can tap into them, too. Have you purchased psychographic data, clusters, or profiles from a third party? What was your experience?