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1/21/2005
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Ad Game Analytics

DDB Matrix is pushing the BI envelope -- among ad agencies, at least -- by rolling out sophisticated analytics that go far beyond standard OLAP and reporting.

A hot trend in business intelligence is big business in a somewhat unlikely place, namely DDB Matrix, a spinoff of advertising agency DDB Worldwide, itself a subsidiary of the giant ($9.5 billion in revenue) advertising holding company Omnicon Group, in New York.

The ad business is certainly no stranger to numbers, but neither is it the place you would normally look for clues to the future of BI. Ad executives like to stay more on the creative, intuitive side of things, a gut feel approach as opposed to a quantified slicing and dicing of massive stores of data. But DDB Matrix is pushing the envelope by rolling out sophisticated analytics that go beyond standard OLAP and reporting.

This is precisely what DDB Worldwide created DDB Matrix to do in 1999. You won't find typical ad men and women working in the Chicago-based spinoff. CEO Doug Hughes boasts that he doesn't know a thing about the creative side of the ad business. What he does know, along with most of his staff, is "econometrics," the area of statistics dealing with change over time due to multiple causes.

It's the kind of thing that goes beyond traditional BI, according the Kurt Schlegel, analyst with the Meta Group in Stamford, Conn. Schlegel puts OLAP and reporting squarely in the realm of what most people understand as BI, but he also says this is fast changing. "One of the big trends this year is rolling more data mining and predictive analytics into BI tools." Some of the vendors to watch here, he says, are SAS, SPSS, and Fair Isaacs. Of these, he says SAS is the leader in predictive analytics.

DDB Matrix, in fact, uses SAS to build a separate data warehouse for each of its clients. The data warehouse resides solely in the SAS database -- no Oracle, DB2 or SQL Server boxes need apply. This, according to Eleanor Taylor, manager of Business Intelligence at SAS, makes it possible to optimize the data for advanced analytics. The SAS database is a scaled-down relational structure that lacks features such as rollbacks, commits, and recovery that are essential to transactional systems. BASE SAS does the data manipulation and integration -- all the standard ETL (Extract, Transform, and Load) stuff -- so essential to building and maintaining a clean data warehouse.

The heavy lifting happens in the SAS ETS (Econometric Time Series) package. It is here that DDB Matrix clients can get answers to questions such as, "How important was color in the print ad that ran in the Wall Street Journal on Tuesday?" One of the features in ETS that would likely come into play here is ARIMA (Auto Regressive Integrated Moving Average). The auto regressive function is the more familiar, statistical regression tool that allows users to analyze what has happened in the past and make predictions. "Integrated" refers to the ability to look for cycles, and the Moving Average uses random walk analyses to refine predictions.

None of these fancy algorithms are of much use without good data, and lots of it. This is why, according to DDB Matrix's Hughes, his company often starts small with a client, adding features as the data set expands. "Applications can grow to be quite large," he says. "For example, with Dell we have, over the last seven years, built a warehouse with more than 1.5 million records containing data on all ads, print, radio, cable TV, etc. The application now has thousands of lines of custom code. This database started out quite small."

The goal is always to help the client get more bang for each buck spent on advertising. An executive at a client site, for example, might generate a report that shows print to be the best medium for one geographic area, while television is the best choice in another.

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