Multivariate Landing Page Optimization - Overview

Overview

Multivariate landing page optimization is based on experimental design (e.g., discrete choice, conjoint analysis, Taguchi methods, IDDEA, etc.), which tests a structured combination of webpage elements. Some vendors (e.g., Memetrics.com) use a "full factorial" approach, which tests all possible combinations of elements. This approach requires a smaller sample size—typically, many thousands—than traditional fractional Taguchi designs to achieve statistical significance. This quality is one reason that choice modeling won the Nobel Prize in 2000. Fractional designs typically used in simulation environments require the testing of small subsets of possible combinations, and have a higher margin of error. Some critics of the approach question the possible interactions between the elements of the webpages, and the inability of most fractional designs to address this issue.

To resolve the limitations of fractional designs, an advanced simulation method based on the Rule Developing Experimentation (RDE) paradigm was introduced. RDE creates individual models for each respondent, discovers any and all synergies and suppressions among the elements, uncovers attitudinal segmentation, and allows for databasing across tests and over time.