Multivariate testing

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In statistics, multivariate testing or multi-variable testing is a technique for testing hypotheses on complex multi-variable systems, especially used in testing market perceptions.[1]


In internet marketing

In internet marketing, multivariate testing is a process by which more than one component of a website may be tested in a live environment. It can be thought of in simple terms as numerous A/B tests performed on one page at the same time. A/B tests are usually performed to determine the better of two content variations, multivariate testing can theoretically test the effectiveness of limitless combinations. The only limits on the number of combinations and the number of variables in a multivariate test are the amount of time it will take to get a statistically valid sample of visitors and computational power.

Multivariate testing is usually employed in order to ascertain which content or creative variation produces the best improvement in the defined goals of a website, whether that be user registrations or successful completion of a checkout process (that is, conversion rate). Dramatic increases can be seen through testing different copy text, form layouts and even landing page images and background colours. However, not all elements produce the same increase in conversions, and by looking at the results from different tests, it is possible to identify those elements that consistently tend to produce the greatest increase in conversions.[2]

Testing can be carried out on a dynamically generated website by setting up the server to display the different variations of content in equal proportions to incoming visitors. Statistics on how each visitor went on to behave after seeing the content under test must then be gathered and presented. Outsourced services can also be used to provide multivariate testing on websites with minor changes to page coding. These services insert their content to predefined areas of a site and monitor user behavior.

In a nutshell, multivariate testing can be seen as allowing website visitors to vote with their clicks for which content they prefer and will stand the most chance of them proceeding to a defined goal. The testing is transparent to the visitor with all commercial solutions capable of ensuring that each visitor is shown the same content on every visit.

Some websites benefit from constant 24/7 continuous optimization as visitor response to creatives and layouts differ by time of day/week or even season.

Multivariate testing is currently an area of high growth in internet marketing as it helps website owners to ensure that they are getting the most from the visitors arriving at their site. Areas such as search engine optimization and pay per click advertising bring visitors to a site and have been extensively used by many organisations but multivariate testing allows internet marketeers to ensure that visitors are being shown the right offers, content and layout to convert them to sale, registration or the desired action once they arrive at the website.

There are two principal approaches used to achieve multivariate testing on websites. One being Page Tagging; a process where the website creator inserts Javascript into the site to inject content variants and monitor visitor response. Page tagging typically tracks what a visitor viewed on the website and for how long that visitor remained on the site together with any click or conversion related actions performed. Page tagging usually needs to be done by a technical team and typically cannot be accomplished by a web marketer.[3]. Later refinements on this method allow for a single common tag to be deployed across all pages, reducing deployment time and removing the need for re-deployment between tests.

Companies known to employ a tag based method of multivariate testing are: Conversion Works, Adobe, Business Intelligence Group GmbH (B.I.G.), Amadesa, Maxymiser, Google Website Optimizer, Vertster and Autonomy Corporation

The second principal approach used does not require page tagging. By establishing a DNS-proxy or hosting within a website's own datacenter, it is possible to intercept and process all web traffic to and from the site undergoing testing, insert variants and monitor visitor response. In this case, all logic sits server rather than browser-side and after initial DNS changes are made, no further technical involvement is required from the website point of view. SiteSpect is known to employ this method of implementation.

Multivariate testing can also be applied to email body content and mobile web pages.

Design of Experiments

Statistical testing relies on design of experiments. Several methods in use for multivariate testing include:

  1. Discrete choice and what has mutated to become choice modeling is the complex technique that won Daniel McFadden the Nobel Prize in Economics in year 2000. Choice modeling models how people make tradeoffs in the context of a purchase decision. By systematically varying the attributes or content elements, one can quantify their impact on outcome, such as a purchase decision. What is most important are the interaction effects uncovered, which neither the Taguchi methods nor Optimal design solve for[4].
  2. Optimal design involves iterations and waves of testings. Optimal design allows marketers the ability not only to test the maximum number of creative permutations in the shortest period of time but also to take into account relationships, interactions, and constraints across content elements on a website.[citation needed] This allows one to find the optimal solution unencumbered by limitations.
  3. Taguchi methods: with multiple variations of content in multiple locations on a website, a large number of combinations need to be statistically tested and medium/low traffic websites can take some time to get a large enough sample of visitors to decide which content gives the best performance. For example, if 3 different images are to be tested in 3 locations, there are 27 combinations to test. Taguchi methods (namely Taguchi orthogonal arrays) can be used in the design of experiments in order to reduce the variations but still give statistically valid results on individual content elements[5]. Taguchi uses fractional factorial designs.

See also


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