The authors' explanation of these findings is that people who have conservative ideologies are more fond of tradition and the status quo, and wary of change. The paper obviously caught my eye because of the political component, especially as it touches on voting behavior, which is one of my specialties. I thought that this article might provide me a chance to give an example of how a social scientist can be valuable in analyzing business data.
The value of a social scientist here might not be obvious: after all, three social scientists already did the hard work, and now, don't we have an actionable insight for producers of consumer products? Not quite: the paper's findings are interesting, and I'd have no qualms publishing them in an academic journal as a starting point for discussion, but I wouldn't risk money and brand loyalty on something this vague and uncertain. As a social scientist, not only can I identify the questions that this research doesn't answer, but I can also suggest some practical ways to answer those questions. The short version is that we need more detailed data, preferably at the individual level, and that we might even want to run a few experiments to test our hypotheses.
There are two basic issues here. The first is whether we can make the jump from county-level data to individual consumers. The second is whether conservative personality traits actually influence shoppers' buying choices, or whether there's something else going on that just happens to be related to both factors. Let's address the problem of county-level data first. The problem we have is that we know more products of certain types are leaving the shelves in conservative counties, but we don't know exactly who's buying them. For example, it's logically possible (if unlikely) that the liberals in conversative counties buy more goods from established brands than the liberals in other places.
A more realistic concern is that, as any student of market segmentation can tell you, human psychology doesn't divide us neatly into two big groups, "conservatives" and "liberals". The fact that a candidate has to win a majority of the electorate leads voters naturally to bunch up into two competing groups , but there's a lot of diversity within each of those groups, as all of us who went to college have probably seen in the two-dimensional political graph that college Libertarian clubs like to trot out. But though we only have two choices as voters, as consumers, we have many more, and people who vote together might not shop together.
In the nineteenth century, when the big issues were things like freeing the slaves or allowing men without property to vote, we could safely say that conservative people favored the status quo, and no doubt many "conservatives" still do, but are those people who seek the safety of the well-known really the same as Tea Party members who want a revolution to roll back decades of big government, or the libertarians who favor gay marriage as ardently as they do low taxes? If it's actually just one group of Republican voters that favors the tried and true, we'd get a lot more bang for our marketing buck by focusing directly on them, or, at least, on places where they make up the biggest part of the population. There are other questions we could ask here, but you get the idea.
Even if we can identify the relevant segment of conservative voters who buy established brands, how can we be sure their conservative personality traits are what lead them to make those buying decisions? This question of causality is the one that, more than any other, keeps social scientists up at night. Sure, the psychological explanation offered by the paper's authors is a plausible story, but you can create lots of different plausable stories to explain any given set of facts (anyone who doesn't believe that should consider how quickly the latest management and marketing advice changes).
Here's one plausible story: the authors looked at sales from the same chain of stores in different counties, but the same chain typically offers a different mix of products at different stores. Places that are less densely populated typically have smaller stores, which offer a narrower range of goods, and I'd be willing to bet that where stores offer a narrower range, those offerings are dominated by established brands. And do you know what else is true of less populated places (that is, rural areas vs. big cities)? They tend to be more conservative. In other words, it's entirely possible that people in conservative counties buy more established brands because they don't have much of a choice.
How does a social scientist address these issues to pull out some information that we can act on? First of all, I'd try to find some individual-level data. We may already have data on individual brand choices from store loyalty cards, but to make that useful, we need individual-level pyschological data—that is, we need to know that specific individuals with personality trait X buy brand Y. The closest thing to that we're likely to have is demographic data on the holders of loyalty cards (both the data we gather from the loyalty card program, and data we can obtain from other sources and join with the loyalty-card data), but that may actually be counterproductive: sure, people with high incomes are more likely to vote Republican, but what actually interests us is people who vote Republican because they favor tradition, and their demographic data doesn't tell us a whole lot about that or any other personality trait. We're not, after all, actually interested in whether or not people vote Republican, but in the personality traits that make them both vote Republican and buy one brand rather than another.
In the end, assuming I work for a retailer with a loyalty-card program, I would try to survey a sample of card-holders (perhaps we could offer them coupons or some other incentives to participate). Actually, I probably wouldn't even ask political questions in the survey, because personality traits are what we're actually after (even if the observation about politics was what inspired us in the first place), and political questions might well offend our shoppers.
Getting individual-level data would get us closer to showing that personality traits cause consumers to make particular brand choices, both by looking directly at the traits and choices, and by allowing us to rule out other possible causes—for example, we could look at demographic data and psychological data at the same time in order to see which are related more closely to brand choices. And frankly, in the social sciences, that's about the best we can usually do. The "gold standard", though, is randomized experiments, because, if you divide people into two, randomly-chosen and essentially identical, groups, and then do X to one and Y to the other, you can be pretty sure that any differences you see after that point are due to the difference between X and Y. We rarely do experiments that look at behavior in the real world (as opposed to a psychology lab), because they're expensive and they pose ethical questions, but they're pretty viable for a big retailier with outlets all over the place.
With personality traits, a true experiment is never possible, because you can't force people to have certain traits (how many parents, teachers, and managers have wished it were otherwise?), but we could, for example, make sure that two (or more) stores in places with different sorts of shoppers carried exactly the same selections in one or a few product groups, thus ruling out different lineups as a cause of different brand choices. This might cost us money (lost sales or extra inventory), but unlike an academic researcher, we can recoup that cost in higher sales that result from the new information.
It's interesting to note that this experimental approach can yield results even without individual-level data—that's the logic behind introducing products in test markets, after all—but if we can combine the experiment with a survey of the shoppers at the stores taking part in the experiment (even though using both the survey and the experiment is our most expensive option), we can leverage the data from the experiment and the survey to get more benefit out of both.
I'm
I've noticed that many of the books advertised by Amazon on the sidebar of this blog are by one "Scott Orr". Just in case it isn't already clear, that Scott Orr is not the same Scott Orr writing this blog. Heck, I don't even know who that guy is, though I'm sure he's perfectly nice.
UPDATE:. Actually, yes, I am an author. I wrote this, after all. I've written other things, too, for that matter, many of them published. More to the point, my namesake appears, on closer examination, to be selling music.
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