The Customer Data You Didn’t Know You Needed

That 24-year-old sports enthusiast watching Major League Soccer on Univision and that 65-year-old news junky listening to NPR may not actually be in your target audience. Not everyone in a demographic will be, and that’s OK. There are other types of data that can effectively help you home in on the consumers who could turn out to be your next high-value customer.

You must be thinking, “Wait, what?! There’s more customer data to consider?”

Actually, yes. But don’t hyperventilate. It’s not necessary to overwhelm your data warehouse, CRM system, or marketing automation platform with reams of additional information. It is, however, essential to think differently about the types of customer data beyond demographics and behavior — such as location, affinity, and values — that you can use to connect with today’s demanding, yet fickle consumers. This information may just be the customer data you didn’t know you needed until you test it.

Think about your own behaviors. You may not “act your age” or follow gender stereotypes when it comes to your viewing or listening habits. Based on my demographics, for example, you might expect me to watch one of the Real Housewives series; instead, you’ll find me viewing crime shows and baseball games.

Here are several examples of illuminating data types that can inform your marketing strategy and media buying decisions:

Location-Based Data

Location isn’t just about where someone is; it’s also about understanding who someone is by knowing where they go and spend their time. Overlay that information with demographic and viewership data, and you get a clearer picture of your audience, which allows for a more relevant conversation at scale.

If, for instance, two people go to Madison Square Garden, but one is seeing Disney on Ice and the other is attending a New York Rangers ice hockey game, this says a great deal about their interests. Similarly, “if someone searches for wine, that’s a valid signal. But someone who goes to a wine tasting class … or vineyard tour — that’s a water cooler conversation the next day,” Gravy Analytics chief executive officer Jeff White said at a recent DMCNY event, Beyond Demographics. “If a brand can intersect my passion with a recent and relevant experience, that’s when the magic happens.”

Affinity Data

If a consumer spends her free time, say, using an auto manufacturer’s online tool to soup up her favorite sports car, sharing the final design on social, and then reading reviews on that car and its competitors, targeting her with minivan ads would most likely be a wasted spend.

That information can be hard to come by for individuals, but it’s possible to scale that type of customer knowledge by understanding affinities among specific cohorts. For instance, ad agency Episode Four asked 100,000 Americans, “If you had extra time or money, how would you spend it?” And then prompted respondents with 300 difference genres of activities and items — everything from podcasts to TV to wine to shopping — to help them make selections. The agency also collected 25 different demographic data points, such as age, income, and political affiliation. The findings were full of surprises, Mark Himmelsbach, Episode Four’s founding partner, pointed out during the DMCNY event. Consider this: Among ultra-high-net-worth individuals (those with at least $10 million in investable assets), golf was polarizing: half hate it and half love it. That’s an important consideration if a brand was thinking of sponsoring or advertising during a golf tournament.

Values Data

Today, more than ever before, consumers make purchases that align with their values. This often translates to a consumer purchasing a brand because of a shared value, such as caring about the environment or being adventurous. Shared values are also a data set that scales.

According to David Allison, author of “We’re All the Same Age Now: The End of Demographic Stereotypes,” consumers who share values agree far more often than consumers within such age groups as baby boomers and millennials. Research he conducted among 400,000 consumers globally showed that baby boomers agree on anything from values to needs and wants to expectations only 13 percent of the time; for millennials, that drops to 11 percent. “How can we target them? We can’t. They’re not a group,” Allison asserts, adding that the same holds true for demographic attributes such as gender, income, and marital status.

Consumers who share values such as “adventurous” or “friendship” or “safety” are much more likely to agree with each other and, thus, be targeted as a cohort. Allison cited three archetypes as examples: Adventure Club consumers agree 89 percent of the time; Super Savers agree 76 percent of the time; and Tech Fellowship consumers agree 81 percent of the time.

“What we value determines what we do,” Allison said during the DMCNY event.

Connecting on a Deeper Level

If you’re making media buys, you may be thinking of your audience in broad demographic swaths. If you’re going for a more direct approach, you may be using lookalike models to define your audience, getting down to finer grains of detail based on attributes such as past behaviors. As effective as these approaches might be for meeting campaign goals, they can get even better when you layer on data that hints at intention.

So, the next time you’re planning a campaign or mapping out a media buy, consider what else you can learn about your audience that will help you target those most likely to become top customers with messaging that will resonate with them. Knowing who your audience is beyond being 18-34 or 25-49 will give you a distinct advantage in speaking to consumers with the relevancy they now expect.

This article originally appeared on MediaVillage.