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Even Mazeratis go to McDonald’s

by Sam Ladner on May 24, 2012 · 2 comments

in Blog, bourdieu, class, culture, ethnography, luxury goods, qualitative research, quantitative research

Using traditional segmentation and “variable-driven” view of the world sometimes fails. This is the story of that sort of failure.

Sometimes Maseratis are not "events" like this one. Sometimes they're just parking.

The other day my streetcar drove past a McDonald’s. The parking lot was jammed. Searching in vain for a parking spot was someone driving a…Maserati. A what? I did a double-take. My researcher’s mind couldn’t help but notice the contrasting image. Here was a person who owns a car worth more than some houses, about to buy a $1 fountain drink or perhaps he’d go full hog and buy a $4 Big Mac. What was a Maserati doing in a McDonald’s parking lot?

That driver was doing exactly what everyone else does at McDonald’s: getting something quick to eat, which also happens to be cheap to buy.

The image was so jarring because it contrasts so sharply with how we picture consumers and their actual consumption behaviour.

Surely there has never been a marketing brief that included both the phrases “Maserati driver” and “McDonald’s.” Yet, here there was a Maserati driver, at McDonald’s. Why do marketers fail to see this real-life scenario?

I imagine the word “Maserati” has probably been included in a brief for, say, a Rolex online keyword campaign. “Along with doing online research for Rolexes, our target also owns a Maserati, Bentley or similar car appropriate for HNWIs (high net-worth individuals).” The brief would go on to talk about his preferences, his income level, his occupation, and his hobbies. It would also likely be one steaming load of BS.

The problem that most marketing briefs have is that they are based not on actual observations of real-life scenarios, but on quantitative representations that strip out real life. If there ever was a Maserati-driving McDonald’s enthusiast found in a set of “big data,” they would likely be stripped out as a mere “outlier.” But here was an exact instance of this very outlier. And no amount of Big Data will ever explain why he’s there.

And that’s the best case scenario. The worst case scenario we resort to downright stereotypes, without any real data at all. In this vein, women become “moms” instead of “investment bankers” or “freelance journalists” (just two real-life examples of “moms” I know). African Americans become “ethnic audiences.” Low-income earners become…well they become nothing because they’re often arbitrarily dropped off the brief due to the mistaken belief they have no money to spend (just Google a little thing called “microfinance” to see if low-income earners have money to spend on things like “interest.”)

Marketers that fail to reconcile the Maserati-driving McDonald’s enthusiast really don’t understand consumption at all. Buying a Maserati and a Big Mac are not mutually exclusive. Wealth accumulated in the form of a car is a particular type of wealth. This is a kind of “conspicuous consumption” (a phrase invented by a sociologist, by the way) can also mean a frugality in other areas, like food. You can love good cars but think expensive food is “fancy” and unworthy. You may know nothing about “good wine.” You may be proud of this.

Sometimes people confound you. Good marketing explains this.

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Categories: Blog · bourdieu · class · culture · ethnography · luxury goods · qualitative research · quantitative research

{ 2 comments… read them below or add one }

1 arvind June 6, 2012 at 7:04 am

I think in this context, Big Data means something very different than how you’re using it. Specifically: massive, interlinked data acquired through instrumentation, e.g. your google search history, or what amazon knows about you based on what you wishlist or purchase.

I’d argue that Big Data, thus defined and properly analysed, could explain ‘outliers’ better than standard market research surveys do.

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2 Sam Ladner June 6, 2012 at 7:08 am

Well sure, Big Data could explain outliers better than surveys. But can it explain outliers as well as interpretevist research? No, absolutely not. Interlinked databases or not, there is no substitute for sense-making that comes from the deep analysis of qualitative epistemology. Big Data’s problem is the same problem as “small data”; it has no epistemological ability to incorporate the exception.

What does Facebook know about me, for example? That I went to school in Texas (I did not). I am a man (I am not). I live in the Orkney Islands (I do not). I make a lot of money but I am a total Marxist (well…). What can Big Data do with that? Absolutely nothing because its method is to summarize quantitatively not to interpret qualitatively.

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