Read the book, and it was a good read--some excellent takeaways from marketing campaigns that are still in recent memory.

However, what attracted me to the book was how the author modified the classic design of experiments methodology to test ad effectiveness across multiple media platforms, and the book only provided some surface details on how that occured (AB Splits). I hope they write a follow-up book that will really explain a marketer could perform basic tests of ad effectiveness with the design of experiments (they can keep their ROMO methodology under wraps unless hired as a consultant).

I agree that it would be useful to get more details.

I would agree, they did touch on the basics however; I think the point was to really only providing a glance of what is out there. Experimental design is just that. There is no one right way until you try it; this can also be part of the 10% that we as marketers would love to have, to try new and innovative approaches. This includes research, testing, and implementation. I am a firm believer that testing is essential and the old traditional "it will work because it always has" has pretty much gone to the wayside.

The return on marketing objectives is something that is taught in quite a number of MBA marketing programs (sometimes under different names) and this is a powerful tool. Marketing Evolution has a defined process that that utilizes return on marketing objectives and the interesting note is they have developed a way to make the measurable across many platforms. This was a great book and it would be very interesting to read another one of their books.

In the simpliest form design of experiments is a single test versus a control or an A/B test. Design of expirements provides for efficient ways to effectively test the interaction of various variables and levels. Think channels (TV, Radio, online, etc. and spend (GRPs/TRPs).

DoE can be applied to creative designs (color, size, copy, headline, call to action, reply mechanism, etc) and product development, positioning and pricing (e.g., conjoint analysis). It is possible to test thousands/millions of combinations in a single test design using fractional factorial orthogonal arrays.

In the early 90's, I deployed about 45 direct marketing models for clients while at Epsilon. I commonly did A/B tests and DOE. For more reading:

Intro info
http://en.wikipedia.org/wiki/Design_of_experiments

How big should your test sample be?
http://en.wikipedia.org/wiki/Significance_testing

More advanced (testing design combinations)
http://en.wikipedia.org/wiki/Taguchi_methods

Search here for academic papers on DOE, or using it:
http://citeseer.ist.psu.edu/cs

Good luck,
http://www.linkedin.com/in/gregmakowski

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