« View all blog posts

What is demographic segmentation—and does it work?

Demographic segmentation. It’s hard to talk about customer data and marketing targets without talking about demographics.

In the early days of marketing, they were all we had. You either marketed to everybody or you tried to skew your message and perhaps your medium to go after people based on age, gender, income level, religion, marital status, etc.

These days, we have a lot more data at our fingertips. So, what does demographic segmentation look like today—and is it still worth doing?

What is demographic segmentation?

Demographic segmentation is marketing segmentation based on demographics. Age. Gender. Marital status. Income level. Religion.

Whenever you segment your customers in order to target women over 40 or men in their 20s or married people with a household income over $80,000 per year—that’s demographic segmentation.

Demographic segmentation examples

The four most common ways to segment by demographics are age, gender, income, and religion/race/nationality.

Age: Age can be used to narrow in on a group that’s likely hitting the same life milestones. The average age of marriage in the US is 28 to 30, so wedding planners—for example—may choose to target ads based on that average.

Age can also be used to target whole generations of people who think similarly and value similar things. Sustainability is a core value for most Millennials and Gen Z, so companies competing on sustainability may choose to target these groups with their sustainability campaigns.

Gender: While this category is becoming much less binary and predictable over time, there are still some educated guesses marketers make based on gender. Menswear is still marketed to men. Jewelry is still marketed to women. Women are more likely to donate to charity. Men are more likely to shop in store and pay full price.

Of course, research from the American Psychological Association shows that gender has no bearing on a person’s personality. And 81% of your Gen Z customers strongly believe gender doesn’t define a person. So, perhaps appropriately, this kind of demographic targeting may well be on its way out.

Income: This one’s pretty obvious. If you’re selling Ferraris, you want to target people who can afford them. If you’re marketing a luxury product—and pricing accordingly—you’re probably not targeting people making minimum wage.

Religion, race, or nationality: Evangelical Christians don’t want your sweary t-shirts. Kosher Jews don’t want your cheeseburgers. Strict Catholics don’t want your birth control ads. For better or worse, backgrounds and religious choices dictate people’s actions and can be a quick way to rule people into or out of the ideal audience for a marketing campaign.

Advantages and disadvantages

The advantage of demographic segmentation is pretty obvious: the more you can hone in on your target audience, the less likely you are to waste your ad dollars, irritate potential future customers with irrelevant ads, or even get yourself into legal or PR trouble (marketing alcohol or guns to kids, for example).

If you’re advertising alcohol, you want to leave out anyone under 21. If you’re selling something decidedly non-kosher, you want to leave out people who follow kosher law. If your campaign is about retiring in the Caribbean and the average retirement age is 65, you’re probably targeting people over 60.

In all those scenarios, demographics are a quick, simple way to hone in on the people most likely to want or need your product or service.

But here’s the disadvantage: demographics aren’t enough.

We analyzed hundreds of client campaigns to find out what predicts customer behavior. And demographics? Turns out they are 20 times less predictive than behavioral data.

In fact, demographics were the leading predictor of customer actions only 4% of the time in those hundreds of campaigns. Behavioral data, on the other hand, was predictive 86% of the time.

The problem with demographic segmentation

If you think about it, it makes sense. Targeting on demographics alone limits us.

Moms buy diapers, yes—but so do dads. People over 60 plan for retirement, but so does a growing movement (called FIRE) of people in their 20s, 30s, and 40s. Not all Jews eat kosher, and not all people who buy kosher products are Jews. And women are increasingly offended at the “pink tax” that comes with buying products marketed to women.

In fact, not a week goes by that I don’t hear a friend or colleague joking about badly targeted advertising.

Child-free friends hit their mid-twenties and are bombarded by diaper ads. Happily single friends are chased around by ads about loneliness and online dating. “I’ve been married six years, and now I’ve become a target for divorce ads,” one happily-married friend recently joked when their feed filled up with lawyers.

So, should demographic segmentation go the way of the dodo?

If demographics are only predictive 4% of the time and behavior is knocking results out of the park, what’s the smart next step for marketers?

In our experience, the answer is a combination of strategies. We’re not saying demographics don’t matter. You still need to filter out people under 21 for your alcohol ads. It still might be safe to assume that people under 25 aren’t that interested in your retirement villas.

The point is that demographic segmentation isn’t enough. To truly reach your target audience, you need behavioral data too. And even with behavior and demographics in the mix…with today’s technology, businesses can do one better…

From traditional segmentation to automated personalization

The truth is that personalizing ads for maximum impact doesn’t stop with behavioral data either.

Because segmentation itself is flawed.

Segmentation is about grouping people together based on shared characteristics. But no matter how much behavioral and demographic data you have, grouping people is never going to get you to the 1:1 personalization that customers expect.

In every group of 100 or 1,000 or 10,000, you’ll have people you’re wasting ad dollars on, annoying with irrelevant ads, or completely misunderstanding. The bigger the group, the more outliers you likely have in there.

Which is why, ultimately, if you want to reach individuals with the right messages at the right times in the right places, the only answer is a move toward 1:1 personalization through data science, machine learning, and automation.

What is 1:1 personalization?

So, what exactly do we mean by 1:1 personalization? We mean taking the idea of segmentation and whittling the group down to one. One customer. One individual. One person whose combination of behavior, demographics, and interests dictate what ads you show them, what messages you send, and when and how you send them.

The only way to get there? Technology.

After all, your team of 10 or 30 or even 300 isn’t going to be able to build individual campaigns for millions of customers. But smart machines? They can tell you in an instant—based on all that customer data—what’s most likely to move an individual customer toward a purchase or away from churn.

Going beyond demographic segmentation

Curious about how to get your company from demographic segmentation to 1:1 personalization? Download our guide, Personalization at Scale: 1:1 Marketing to the Millions. Or, get in touch today for a free demo or to ask us any questions you have about personalization and segmentation.