Multivariate landing page optimization: Difference between revisions

Revision as of 12:59, 26 October 2018

Revision as of 13:06, 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:

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== Simulation (survey) execution ==

In simulation (survey) MVLPO execution, the foundation consists of advanced [[market research]] techniques.<ref>{{cite web |title=Consumer Driven Multivariate LandingPage Optimization:Overview, Issues, and Outlook |url=http://tir.ipsitransactions.org/2007/July/Paper%2003.pdf |accessdate=26 October 2018}}</ref> In the research phase, the respondents are directed to a survey that presents them with a set of experimentally designed combinations of a landing page. The respondents rate each version based on some factor (e.g., purchase intent). At the end of the research phase, [[regression analysis]] models are created either for individual pages or for the entire panel of pages. The outcome relates the presence or absence of page elements on the different landing page executions to the respondents’ ratings. These results can be used to synthesize new landing pages as combinations of the top-scoring elements optimized for subgroups or [[market segments]], with or without interactions.<ref>Alex Gofman, Howard Moskowitz, and Tonis Mets. 2009. Integrating Science into Web Design: Consumer Driven Website Optimization. The Journal of Consumer Marketing, 26(4): 286-298. {{doi|10.1108/07363760910965882}}.</ref>

Simulation execution has the following advantages: