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Love, Money and Happiness

love-money-happiness

Love, Money and Happiness

More time’s passed than I intended since my last blog entry on behavioral economics, so if you want to revisit the first in this series, click here. Again, if what you read here piques your interest, I highly recommend Daniel Kahneman’s book, Thinking, Fast and Slow. These blog entries are merely summarizing several of his discoveries that have stuck with me and looking at how they can have a positive effect on the way we approach marketing, business and life in general.

With the disclaimer out of the way, let’s talk about what makes people happy. Generally speaking, a person’s satisfaction with their love life is very strongly correlated to how happy they are. Well, as long as you ask them about their love life first and then their general happiness. If you reverse the order of the questions, the correlation ceases to exist. The same holds true for all kinds of things. Ask a group of people how content they are with their level of wealth then have them rate how happy they are and you’ll find that money truly does buy happiness. Flip the order of the questions and you’ll see that it’s just an illusion.

In short, if you cause someone to think about some specific aspect of their life and then ask them about their overall happiness, you’ll generate solid data that said specific aspect of their life strongly correlates to how happy they are. If you make someone think about something that makes them happy, they’ll subsequently report being happier with their overall status in life than they actually are. The effect holds true in the opposite direction as well: if you cause them to think about something that makes them unhappy, they’ll report being unhappier than they really are. Neither result is valid.

If your professional responsibilities include gauging customer satisfaction, you’re probably seeing some instant applicability of this phenomenon. Knowing this effect exists, would you ever want to ask customers how satisfied they were with your delivery times, followed by a question about how happy they are with your company as a whole? Only if you’re looking to greatly exaggerate the effect delivery time has on your customers’ satisfaction. Generally speaking, when crafting research, question order can wreak havoc on your results, exaggerating the relationships between factors or even creating them out of whole cloth. That’s why most research tools today allow for randomization of the order in which respondents are exposed to questions. When you’re asking questions that don’t require sequential exposure, randomization should be used.

These insights have implications beyond determining question order in a survey. For example, let’s say the majority of your customers who need assistance contact your customer service via phone. Let’s also say one of your primary means of gauging customer satisfaction with your company is with a post-call survey to those who contact your customer service. See the problem in such a situation? If your customer service experience is exemplary, the survey data will likely yield an artificially positive view of your company. Likewise, if your customer service is terrible, the data will probably look worse than it truly is. Obviously, you would never want to intentionally offer less than the best customer service, but in such a situation, you would want to be aware of potential bias. If possible, you would want to structure your survey questions in such a way as to negate it as much as possible.

That’s it for this blog entry. Next time I’ll touch on framing and how different presentations of identical facts can lead to wild divergences in how we interpret and respond to data.