Building Models on Quicksand

  |  February 28, 2013   |  Comments

How often do you start with a clean slate and rebuild your model because the variables themselves have changed?

My good friend, frequent mentor, and occasional network diagnostician Mark Gibbs sent me the following quote the other day and it rang a bell. Several of them. I knew I would have to explore the resonance here.

The only man I know who behaves sensibly is my tailor; he takes my measurements anew each time he sees me…The rest go on with their old measurements and expect me to fit them."
- George Bernard Shaw


Three bells went off at the same time when I read that. And they harmonized.

1. Fails expectations. The people asking for numbers know what they want the answers to be. If the numbers are not in line with pre-conceived notions, these people naturally question:

  • The data
  • The data collection method
  • The definition of the data that's been collected
  • The time frame in which the data was collected
  • The analysis
  • The analyst
  • The weather
  • The I Ching

2. Been there, done that. The team leader has solved that problem already and sees no reason to go back to revisit it. Once the problem is solved, it's not only no longer a problem, it's no longer interesting. The world may have changed and time marched on, but the team leader will assure you that we don't need to spend any more time on this issue. Asked and answered. Fixed. Mischief managed. Why are you bothering me with this?

3. A worldview is a terrible thing to waste. This is actually the same as number two, but with one important difference. This time, the old stick-in-the-mud is you. And there's good reason for it.

As humans, we thrive on learning the things we don't need to pay attention to. That noise in the night? The neighbor's dishwasher. That scathing email from a customer? Oh, it's that customer. That error message in your browser? Not your fault.

We make many assumptions in order to function: gravity; time tables; the fury of a woman scorned. And then we make assumptions that are harmful: the person in the other car knows I'm going to turn; using Bcc in emails is OK; thinking that there are some variables we can ignore while engaged in marketing analytics.

We know that this segment of our prospects always responds this way to this sort of offer.

It's a fair assumption. After all, it might be the very definition of that segment. But how often do you dust off the cobwebs and start with a clean slate?

"Most of our assumptions have outlived their uselessness."
- Marshall McLuhan

We have entered into an era of marketing mix modeling, predictive analytics, and chasing the e-commerce genome. Over time, by necessity, we build models on top of models of the world around us in order to predict where our marketing investments will best pay off.


Each model is built upon assumptions and validated against real data. But how often do you start with a clean slate and rebuild your model - not because the values of the variables have changed but because the variables themselves have changed?

I propose twice a year. Think of it as spring/autumn cleaning. Take a good, long, hard look at the assumptions that went into the models on which you rely and try to look at them from a new angle. Then bring in some new people to help you.

Make sure those people have a different background from you and a different worldview. Make sure they have difference assumptions. The forecast/prediction/model/rear end they save might be yours.

"Your assumptions are your windows on the world. Scrub them off every once in a while, or the light won't come in."
- Isaac Asimov

Tailor, Mathematics Formula, and Quicksand images via Shutterstock.

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Jim Sterne

Jim Sterne is an international consultant focused on measuring the value of the online marketing for creating and strengthening customer relationships. Sterne has written eight books on using the Internet for marketing, produces the eMetrics Marketing Optimization Summit and is co-founder and current chairman of the Digital Analytics Association.

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