Multivariate landing page optimization: Difference between revisions

Revision as of 09:51, 4 November 2016

Revision as of 12:57, 26 October 2018

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== 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.{{cn}}

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

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== Live environment execution ==

In live environment MVLPO execution, a special tool makes dynamic changes to a page so that visitors are directed to different executions of landing pages created according to an experimental design. The system keeps track of the visitors and their behavior—including their [[conversion rate]], time spent on the page, etc. Once sufficient data has accumulated, the system estimates the impact of individual components on the target measurement (e.g., conversion rate).<ref>{{cite web |title=Conversion Rate Optimierung |url=https://www.sumasearch.de/conversion-rate-optimierung |language=de}}</ref>

Live environment execution has the following advantages: