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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.<ref name="isbn0-13-613668-0">{{cite book
|author=Howard R. Moskowitz
|coauthors=Alex Gofman
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|date=2007-04-11
|pages=272
|isbn=0-13-613668-0}}</ref>
|url=http://www.ftpress.com/articles/article.aspx?p=1015178
|title=Improving the ‘Stickiness’ of Your Website
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* Capable of testing the effect of variations as a real-life experience.
* Generally transparent to visitors.
* Relatively simple and inexpensive to execute (e.g., [[Google Optimizer]]).
=== Disadvantages ===
''Note: These disadvantages are applicable mostly to the live environment tools available prior to Google Optimizer
* High cost.
* Increased complexity involved in modifying a production-level website.
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