Modeling is a powerful tool that is worth considering when determining how best to spend your marketing dollar.  At its simplest, modeling looks for patterns in data to predict future behavior.  That data could be past behavior.  If someone bought diapers last week, it is very likely they will buy them again this week.  It could also include demographics such as age and gender or, in a B2B context firmographics, the number of employees and annual sales volume.  Attitudinal information, such as willingness to purchase a product, could also be used in a model.  The power of modeling comes from the fact that it weighs all of the factors and results in a unique algorithm that predicts future behavior.  Instead of the usual “spray and pray” approach, modeling enables you to focus your dollars where they will have the most effect.

Two articles in the Wall Street Journal last week offered real life examples of how models can solve business problems.  I have seen clients use attrition models and proportional hazard models to determine which customers are likely to leave.  Google is building an attrition model to identify which of its employees are most likely to leave the company for another opportunity.  Presumably Google will target those employees most likely to leave and be able to retain valuable talent that might otherwise walk out the door.

Chrysler’s digital agency has designed a media modeling system according to the Wall Street Journal.  It sounds like a marketing mix model and is being used to allocate Chrysler’s marketing dollars.  At a basic level, this model tells Chrysler how much money needs to be spent on marketing to drive a certain number of vehicle sales based on the web traffic generated.  By monitoring online activity and tying it to their marketing campaigns, Chrysler has determined how many web visits translate into sales.  The media modeling system, including enhancements based on the ongoing performance of television advertisements, has helped Chrysler determine how to structure their marketing campaign and tweak marketing in real time to drive results.

These two examples may not fit your exact situation but they highlight the power and value of modeling.

Last night I saw an episode of an upcoming cable television show in my local movie theater.  The lead character is played by an actress I have enjoyed in movies in the past so I thought I would go to the screening despite being the wrong “demographic”.   I do not have cable and average less than 5 hours of television a week.   When I want to watch cable, I go to the gym! 

The television show was disappointing and slightly disturbing.  The experience itself was wholely unsatisfying.  Once the episode aired, the lights came on and that was it.  No one wanted to know what the assembled audience thought.  There was no mechanism to provide feedback.  I was left wondering why the cable channel bothered.  If this wasn’t an opportunity for market research, perhaps it was part of a viral campaign?  If so, it worked.  I have told two friends how terribly disappointing and unfunny I found the show to be. 

I have since read that an integrated campaign to introduce the show included more than just screening episodes of the show at movie theaters in major markets.  The first episode is available on websites like YouTube and on some mobile phones.  Since I went to a screening, I am surprised that I needed to find this out using Google.  While I applaud the use of multiple channels and delivery devices, more integration could have been done.  Why not let the audience at the movie theater know about additional ways to view the first episode?  Why not enable viewers to take an on-line test to determine which personality featured in the show best fits them?  At a minimum the channel could have informed the audience at the screening I attended when the first episode would air.