When chatrooms were in their infancy, people would introduce themselves with three simple facts: age, sex, and location (A/S/L). For years, marketers have clung to those demographics, too.
Now, when strangers connect online, they can know far more before one another before initiating that conversation: music interests, hobbies, political affiliations–dating sites like OkCupid use all those factors to formulate how well of a match two people would be.
Sports teams can also have access to all those other factors, but all too many are limiting themselves to “A/S/L” when who a fan is is so much more. There could be a love story in the making for sports teams willing to dive into who their fan matches are, but they’ll have to go deeper than the demographics they might be used to.
How demographics get used now
Marketers have been using demographics to build out ideal buyer profiles and use market research about demographics to craft messaging since the Madison Avenue days. Which made sense; it was impossible to target individual consumers in the past, so demographics like age and ethnicity were the most effective way of targeting groups of people. For example, you could find it useful that Nielsen survey data finds that African-American millennials are 25% more likely than all millennials to say they’re the first of their friends to try new technology.
When it comes to digital, marketers still use demographic data to segment ads and messaging. If they believe 25-34 year olds respond to a different message than 35-49 year olds, knowing age groups (or location, or any other easily accessible third-party data) allows them to craft messages tailored for those groups.
The problem is that A/S/L is not always reliable or relevant. We’ve talked before about how you don’t know where purchasable demographic data came from, but that’s not the only problem. Certain demographics will be very important for venues, for example location (e.g., you don’t want to advertise to people in Anchorage about an event happening tomorrow in New York). Aside from that, though, as Todd Yellin, Netflix VP of Product Innovation puts it,“here’s a shocker for you, there are actually 19-year-old guys who watch ‘Dance Moms’, and there are 73-year-old women who are watching ‘Breaking Bad’ and ‘Avengers’.”
Traditional stereotypes around gender and age are not reliable indicators of “who someone is” because identity is no longer singular. One person can have multiple “identities” depending on where they are and who they’re with. So using 3 or 4 buckets may be more reliable than no buckets at all, but can be far less reliable than trying to find what truly makes someone (or a group) unique.
Picking the gold out of the garbage
So, the obvious question then is, if demographics aren’t the key, what is? Well, it’s not that they aren’t a part of the key, but they aren’t the key. Todd Yellin of Netflix also says, “There’s a mountain of data that we have at our disposal. That mountain is composed of two things. Garbage is 99 percent of that mountain. Gold is one percent.”
For Netflix, location is going to go in the trash heap because it’s not relevant to their business model. For venues, location matters, and so does gender (e.g., for promoting a ladies night event). So it’s finding that one percent of gold particular to your business and your fans to be able to better advertise–not just using demographics as a proxy to guess who might be valuable.
As an example, let’s look at a fictional baseball team looking to sell season tickets. We know that an obvious part of their one percent of data is “Who has bought season tickets?” Once they identify that group, the question is, “What makes this group unique?”
Matching by uniqueness
So, going back to OkCupid, how do they make a match? By using all the data they have, even if that data doesn’t immediately seem “relevant.” If the fictional baseball team captures social data, they can add numerous data points in the form of brand affinities and interests to compare against the rest of their audience. Why compare to the rest of the audience?
Without a reference point, you just can’t tell what falls into the 99 percent of garbage. For example, you can find that your season ticket holders are 70 percent female, but that won’t help a season ticket campaign if your entire database is 70 percent female, too. It’s not unique.
What might be unique however, is if your season ticket buyers have not only a household income $20K above your total database, but a particularly unique affinity for a local car dealership, Ziggy Marley, and the local hockey team. (Within our platform, we call that uniqueness the “Umbel Index,” which compares your audience to the millions of other people in the Umbel universe.)
The more data points you have, the more accurate an “identity” you can form for the segment that you want. With that identity, the baseball team can do a number of things:
Rank other people in their database based on how closely they “match” to your past season-ticket buyers for sales outreach.
Use your newly identified unique affinities and interests to target people on the different ad platforms.
Use reach extension (e.g., Facebook Lookalikes, AdWords Similar Audiences) to find even more people that the algorithms judge as “similar” enough
But the use can be anything you want: Identify people who are at risk of not renewing, seek out fans similar to those with a high lifetime value, or even those similar to those who engaged with a specific campaign.
You have so many sources of data about fans that there’s no excuse to use the same data points that marketers used in the 50s or 60s. Understand your fans and you’ll be able to reach the right people, no matter what demographic they fall into.