The former head of the Bain Capital NY office has a new book out on the U.S. economy. I suspect it will be one of the most controversial and probably hated books of the year. It is a logical (at times, but not always) argument for why poor people should thank rich people for being richer and richer. And why the rich aren’t really rich enough.

He was recently interviewed in the NY Times about his book. One of the most “teachable” aspects of the article is how this ex-Bain guy used a statistical approach to choosing his wife.

Here’s an excerpt from this fascinating article:

*** Start ****

“There’s also the fact that Conard applies a relentless, mathematical logic to nearly everything, even finding a good spouse. He advocates, in utter seriousness, using demographic data to calculate the number of potential mates in your geographic area.

Then, he says, you should set aside a bit of time for “calibration” — dating as many people as you can so that you have a sense of what the marriage marketplace is like. Then you enter the selection phase, this time with the goal of picking a permanent mate.

The first woman you date who is a better match than the best woman you met during the calibration phase is, therefore, the person you should marry. By statistical probability, she is as good a match as you’re going to get. (Conard used this system himself.)

This constant calculation — even of the incalculable — can be both fascinating and absurd.”

*** End ***

Now, obviously not everyone from Bain actually uses this approach to choose a spouse. Some of us prefer the romance of falling in love. But (and this is very important), everybody I know at MBB most certainly sees the logic behind this approach (even if they would not use it themselves) and many of them will use a similar logical approach to other aspects of their lives.

For example, you should have seen one of my former McKinsey colleagues. She was a woman who was planning her wedding and basically “estimated” how many people would attend her wedding.

She started with a list of everyone invited, estimated whether or not said person would bring a date based on their current relationship status, further estimated based on distance of travel needed what percent would actually make the trip to create an excel forecast model that estimated total headcount.

This in turn provided the assumptions for her wedding cost forecasting model, which factored in the latest head count estimate and the cost per person for food.

Crazy? Maybe. Did everyone at McKinsey who saw this understand, and at some level were they secretly impressed (even if they wouldn’t admit to it out loud)? Oh, most definitely.

This is just how consultants think about things… or at least have the option to think in this way when they want to. I hope this gives you a sense of the mentality MBB consultants have, and how they approach (or have the option to approach) nearly any unstructured problem, and structure it in some logical way.

You can see the whole article here: