You adore ginger-tofu bowls and bacon cheeseburgers. You read literary novels and gossip magazines, crank up both Pavarotti and Pitbull. When it comes to knowing ourselves, we easily accept that our tastes range from Kafka to Kim Kardashian. But when it comes to others, we’re quick to pigeonhole, assuming others only have a narrow range of tastes and interests.
Why is this important? According to Darden Professor Tami Kim, companies may be missing a wide variety of business opportunities if they assume that people who like one thing will automatically have an aversion to something perceived as its “opposite.”
The (Erroneous) Double ‘Dis-’
In a series of experiments, Kim and her colleagues demonstrated that “people sensibly expect others to like similar products, but erroneously expect others to dislike dissimilar ones.”
This blind spot is worth considering, says Kim, because it affects not only small matters, such as which songs to put on a playlist or which entrees for a room service menu, but larger issues, such as matchmaking, hiring and major purchases, like housing. Even, potentially, end-of-life decisions, Kim notes. “Physicians and next of kin are called on to make life-or-death decisions based on what they infer the patient’s preferences to be.”
How We’re Wired
Humans routinely predict others’ actions. What we see someone choose or do first becomes an anchor point. The more dissimilar we perceive a choice to be from that initial starting point, the less we think the other person will like it, Kim says. In fact, something highly dissimilar from an initial choice is predicted not to be a neutral option, but to be “actively rejected and disliked,” Kim and her co-authors write in a an article in the Journal of Marketing Research.
For example, in one experiment, Kim and colleagues asked participants to predict whether the subject (Joe) would like or dislike a city or a mountain vacation. One group was told nothing about Joe’s past vacations; the other learned he’d previously vacationed at a lake. “At baseline, only 9.8 percent of participants predicted that Joe dislikes cities, but 33 percent predicted that Joe dislikes cities upon learning of his dissimilar [vacation] choice of lake,” Kim and colleagues write. They predicted he’d like the mountain getaway. “Once a target’s choice is known, people (mistakenly) believe all of the other person’s preferences to be narrowly clustered around that single choice.”
Money Doesn’t Help
Kim’s findings stayed consistent even when participants were told they’d be paid a bonus for correctly predicting another (unknown) participant’s preferences.
“What we saw is that even when people know they could earn more with an accurate guess, they don’t change course,” she says. This implies that the dissimilarity bias is deeply ingrained and that “people feel very confident about the inference,” Kim notes.
Algorithms increasingly recommend products and services, but these formulas are built by humans and thus mirror our own biases. If websites recommend only similar choices, they may risk appearing “trite or unoriginal,” write the researchers. By never risking an unusual recommendation, they may be failing to capture sales.
In fact, in an experiment on movie selections, Kim found that the majority of people (nearly 7 in 10) readily switch to a dissimilar choice that’s higher quality (based on star ratings) rather than trade down to a similar but lesser-quality choice. This has implications for online grocery websites, for which a common default to an out-of-stock item is to substitute the most similar item, even if it is lower quality. Kim’s finding suggests that many people would rather forgo the trade-down substitute and just spend their money differently.
If we assume other people have a narrow range of preferences, we fall into traps. Should you assume that a colleague who works well with personalities unlike yours would not work well with you? Should a dating site only recommend similar matches — even though the cliché “opposites attract” is a time-honored one?
Or, writes Kim, “knowing that a terminally ill patient had previously chosen an aggressive … treatment, might a physician infer that her patient therefore rejects the more passive palliative option and fail to initiate a more comprehensive discussion of all possible treatments?”
One way to estimate others’ preferences more accurately, Kim says, is first to consciously remind yourself how broad your own interests are. When she asked others to use their own experience to calibrate their thinking, Kim saw a reduction in the gaps in making predictions for others.
A second strategy is to broaden your view, Kim advises. Instead of looking at one person who liked a lake vacation and trying to judge if he or she would dislike a night in the city, ask yourself: What’s the likelihood 100 people who enjoyed the rural retreat would dislike the city night?
Tami Kim co-authored “The Role of (Dis)similarity in (Mis)predicting Others’ Preferences,” which appeared in the Journal of Marketing Research, with Kate Barasz of IESE Business School and Leslie K. John of Harvard Business School.