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Multivariate landing page optimization (MVLPO) is a specific form of landing page optimization where multiple variations of visual elements (e.g., graphics, text) on a webpage are evaluated. For example, a given page may have ''k'' choices for the title, ''m'' choices for the featured image or graphic, and ''n'' choices for the company logo. This example yields ''k×m×n'' landing page configurations. The first application of an experimental design for MVLPO was performed by Moskowitz Jacobs Inc. in 1998 as a simulation/demonstration project for LEGO. MVLPO did not become a mainstream approach until 2003 or 2004. Multivariate landing page optimization can be executed in a live (production) environment, or through simulations and market research surveys. == 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,〔Alex Gofman. 2006. Emergent Scenarios, Synergies, And Suppressions Uncovered within Conjoint Analysis. Journal of Sensory Studies, 21(4): 373-414. 〕 uncovers attitudinal segmentation, and allows for databasing across tests and over time. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Multivariate landing page optimization」の詳細全文を読む スポンサード リンク
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