Data Doesn’t Equal Insight
A few days ago, Advertising Age ran an article, “Psychographics: What Your Taste in Beer Says About You,” that read like a horoscope. For instance: “People who drink a broad portfolio of beers … are more open-minded and emotional people who enjoy a variety of life experiences.” The source for this nonsense was a standard psychographic survey that also demonstrated that “Bud Light drinkers are also 48% more likely than the average person to play the lottery every day and 34% more likely to never buy organic products.”
This is all fine and good, as long as you know how likely the average person is to play the lottery daily or never buy organic. But this information was missing. Worse, the unfortunate title of the article asks readers to connect these facts to not just of a tiny proportion of Bud Light’s drinkers, but to all of them.
I think I’m angriest because this shabby treatment of advertising research (by an industry publication, no less) belies how useful it can be in exposing trends and generating insights. I’ll use data from a Slate article on baseball and birthdays to demonstrate the pitfalls and possibilities.
If you’re an American boy born in August, you are 55 percent more likely to become a Major League Baseball player than the average child. If this were Ad Age, I’d now develop the profile of the August baby. He is a baseball player.
But let’s flesh that out with more of the data. Of 4515 American-born players, 503 of them were born in August. So that means that the average August baby’s chances of joining such an elite group are near zero. It also means that August birthdays are not the norm among the MLB players, as only 11 percent of them share the month. So you can’t make predictions about the babies, and you can’t even make characterizations about the players.
So what can you do? You can use the data to search for further motivations and causalities. Rather than describing the customers, try to understand them.
The MLB data led researchers to believe that age cutoff dates for youth baseball leagues created the skew in birthdays among top players. The oldest boys on the team are usually bigger, therefore gaining a competitive advantage and increasing their (admittedly minuscule) chances of making the big leagues. If I were the MLB commissioner and wanted to increase the quality of the pool of players, I’d want this artificial advantage gone. Based on the insight derived from the research, I’d work to create multiple cutoff dates across the youth leagues.
What’s the takeaway for advertisers? Market research is a great tool to use, as long as we’re careful. But first and foremost, remember that data doesn’t equal insight.