Freakonomics' Author Steven Levitt Explains Why Every Professional Should 'Think Like A Freak
With today's release of "Think Like a Freak," coauthors Steven Levitt and Stephen Dubner share their method for arriving at such insights - a set of critical-thinking tools derived from economic theory that can be applied to solve problems, think more creatively, and improve productivity.
"Think Like a Freak" is the latest in what's become a "Freakonomics" franchise that includes the documentary film "Super Freaknomics" and a weekly podcast. To date, the books have sold more than 5 million copies.We talked with Levitt, an award-winning economist at the University of Chicago, about some of the themes in the book - how blind we are to bias, why we mistake perceived problems for actual problems, and why the three most difficult words in English are "I don't know."
This interview has been condensed and edited.
BUSINESS INSIDER: What does it mean to "think like a freak?"
STEVEN LEVITT: It means putting away your moral compass and not worrying about what the answer "should" be, but focusing on what the answer really is. It means thinking hard about causality. It means going beyond the obvious to consider all the possibilities - but still being willing to accept the obvious.
BI: You spend a lot of time in the book discussing bias. What's a business application?
SL: As I've worked with businesses in the years since "Freakonomics" came out, I've seen that almost everyone's reaction to a new problem is to use exactly the same approach they used whenever they last successfully solved a problem.That's one of the reasons big firms have such a tough time transitioning when there are changes in the market. Firms adopt a set of shared beliefs - core values that everyone at the firm believes in and nobody questions anymore.
One example is when (I was) working with a large cellular company. They fundamentally believed that they were about voice calls and text messages. And really, they're so fundamentally about data now that text and voice are almost irrelevant. We couldn't have a conversation where it didn't come back to, "Well, what's the implication for text and voice."
If not now, text and voice will soon be free because they don't impose any cost on your network. It's only because of history that text and voice aren't free right now. Data is all you need to worry about, but honestly I don't think that company will ever consider themselves a data company. My hunch is they're likely to be passed by.
BI: You press the point in the book that the three hardest words to say are "I don't know."
SL: It's absolutely been my experience in business that nobody ever wants to admit that they don't know the answer to questions, even when it's patently obvious that they can't know the answers because of the (limited) information they have. Businesspeople, especially in front of their bosses, have an almost unlimited ability to sit back and mint answers they don't know. To me, that's exactly the opposite of the "Freakonomics" approach.
BI: How can we incorporate thinking like freaks into our workflow?
SL: Most of the time, the simplest rule of thumb is "do today whatever I did yesterday." If it worked yesterday, it will probably work today.
Then, every once in a while, you come onto a problem that inspires you, a problem that bedevils you. You stop and take a breath and say, "OK, it's worth it for me to exert the time and energy to really try to answer this question well."It might be once a week, once a month. Then really tackle this problem because it's important and you don't know the answer.
BI: What's an example of that?
SL: I had a consulting gig with a technology firm. This firm found that many of its customers who were buying their subscription product ended up canceling the subscription.
The question they were asking was: How can we reduce turnover to keep our subscribers for a longer period of time? People had different suggestions in the original meeting. We suggested to look at the data.
BI: What did you find?
SL: We found in the data that a lot of people seem to be quitting even though they were using the product a lot. It wouldn't be surprising if someone who hasn't opened the product in two months cancels their subscription, but there were customers who used the product a lot and ended up canceling anyway. You have to tell yourself, that seems strange. That became the point where we wanted to burrow.
When we looked into it, we saw that for a lot of customers who used the product frequently, when they tried to renew, they had a credit card failure. That was the end. You never saw them again.
No one understood this. It was in the data, but no one had thought to tunnel down in that exact way. When (the tech firm) created an intervention where they'd call a subscriber who had such a credit card problem, then 60%, 70% of those customers ended up staying with the product.
It wasn't that they didn't have the data to come to the conclusion. It's just that it's rare for people to have the patience to look at the data before trying to solve the problem instead of immediately reacting to a perceived problem. The actual problem and the perceived problem might not have anything to do with each other.In that example, the critical moment is when you have the willingness to say, "I don't know why our customers aren't renewing."