When one has a large universe of possible combinations to test, "experimental design" allows you to figure out the most appropriate and efficient subset of creative permutations to sample.

Design of Experiments, or DOE, is a field of study that provides information about variables and their interactions, and has provided breakthrough solutions in systems as varied as manufacturing and biology. By using DOE software and techniques, engineers, scientists and researchers in virtually any industry can optimize responses and discover winning combinations. Optimost has pioneered the application of DOE principles to website testing and landing page optimization.

DOE, using multivariable testing, is superior to traditional one-factor-at-a-time experimentation (A/B testing), which ordinarily requires many more time-consuming test runs to obtain less-than-perfect knowledge of a process. Because one-factor-at-a-time can't reveal critical variable interactions, it offers a shaky substitute for a “designed” experiment. This means, for example, researchers using a “disjoint” method might never learn that red ink and blue ink interact to make purple—a severe oversight.

DOE has been used by marketers for many years and has been catalyzed in part by the writings of Genichi Taguchi. DOE includes a number of different types of designs or forms of a multivariate test, including Full Factorial Design, Fractional Factorial Design, and Optimal Design.

Using modern computing power, the Internet, and a proprietary methodology adapted from the field of Design of Experiments, Optimost is able to deliver a robust, multivariable testing platform that can perform website testing for a virtually unlimited number of variations of key web pages for website optimization.

Optimal Design | Taguchi | A/B Testing | Design Comparison | Glossary of Terms

Other Design of Experiments Web Resources

Efficient Experimental Design with Marketing Research Applications (PDF) (Warren F. Kuhfeld, Randall D. Tobias, and Mark Garratt)

Experimental Design, Efficiency, Coding, and Choice Designs (PDF) (Warren F. Kuhfield)

Presentation of Applicability and Limitation of Taguchi (PDF) (National Institute of Standards)

Overview of Experimental Design Methodologies (National Institute of Standards)