Advancements to Help Analyze Your Data

  |  June 28, 2005   |  Comments

What's faster,better, more accurate, and less expensive in analytics technology.

If you ever used Microsoft Excel to produce graphs, I have some great news. That news is one of two items I want to cover today regarding technology advancements that help you analyze customer data.

Better Graphs Than Excel

The first news concerns the graphic software modules available from Purple Insight. In a very smart move, the company split its software offering into separate components with different capabilities. Anyone who produces graphs with Excel can now produce amazing graphs from the MineSet software for a very reasonable fee. If you use Excel for graphing, purchase the data visualization software.

I've been a MineSet software fan for many years. Silicon Graphics developed it in the '90s, back when the company was producing graphics for such movies as Jurassic Park. So somewhere under the hood of MineSet software is the engine that produced the dinosaurs you saw in that film.

This means you can use the dinosaur software in a good way, very differently from what's implied in those ubiquitous Microsoft dinosaur ads. The Jurassic Park graphical software engine is available to people who don't have a budget that matches what Universal Studios spent 12 years ago. It's a great example of how technology is improving and becoming available to more people at a fraction of its original cost.

Oh, and you can even download a free software trial... another good use of technology!

Advanced Analysis for Online Campaigns

Progress is also being made in the use of advanced analysis to improve online marketing campaigns. The guys at Poindexter Systems are doing their best to increase the use of predictive modeling in the pursuit of better marketing return on investment (ROI).

Their system collects data from a marketer's online ad campaign and uses it to improve campaign results. Nothing new about that, of course. But instead of using traditional reporting to decide how the campaign should be "tuned," their system puts the data into a predictive model to look at many different combinations and cuts of variables.

By using a decision-tree type of analysis, the system will look across many different ads, sites, days, times, and so forth to determine what the top performing combinations of those variables are. They then alter the campaign based on this analysis.

As I discussed in a recent column, this approach will improve campaign results more than the traditional reporting approach to campaign analysis. A modeling approach analyzes more possibilities and information, which always yields better results.

They haven't yet figured out a good way to incorporate testing many different creative elements, but this is a logical next step. Nevertheless, using this advanced analytical technique is another way marketers are advancing in technology to analyze consumer data.

When Digital TV Is Ubiquitous

If you want to look a bit into the future, the above approach will eventually be used to optimize digital TV advertising. Data collected from households regarding programming and ad response will allow advertisers and marketers to optimize campaigns in ways not possible today.

(That is, if anyone's still watching TV. I wonder how much someone got paid to come up with the idea of a TV show in which the viewer watches someone else play cards?)

Venture capitalists think there might be something to this. A small number of decently funded companies are working to develop systems to accomplish this kind of campaign management and optimization.

We'll see which ones get it right and survive.

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ABOUT THE AUTHOR

Brian Teasley Brian Teasley is the leader of Teasley, a consultancy that helpsadvertisers, marketers and advertising agencies use data and analysis toimprove their marketing campaigns. Brian has over 14 years experience inengineering and marketing, and has worked for numerous Fortune 100companies. Brian also teaches a marketing course at New York University. Heholds a M.S. degree in Applied Statistics from Iowa State University and aBA in Mathematics and a BA in Mathematics and Statistics from St. OlafCollege.

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