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Evaluating the efficacy of websites is crucial to the improvement of the marketing tools efficacy (Scobey and Lingras 18). Although the connection between the two factors may seem rather loose, a closer consideration of the phenomena in question will show that a user-friendly interface creates premises for attracting an increasingly large number of customers. For these purposes, two key types of tests can be used (MacFarland 7). A detailed overview of the Yahoo! E-Commerce site ( Yahoo Stores Pricing and Plans par. 1) has displayed that the application of a multivariate testing system is strongly suggested due to a large number of details and the need to address a range of aspects of its functioning.
One must give credit to the A/B test as an analysis tool. According to the existing definition and the numerous pieces of evidence, it works perfectly as the means of assessing two versions of the same web page and, therefore, coming to a conclusion regarding their efficacy as far as the traffic flow is concerned: a simple way to test changes to a website page against the current design in order to determine which design produces positive results (Frick & Eyler-Werve 200), the specified test can be viewed as a tool for choosing between two existing alternatives. Therefore, for the sites with a simplistic design and a small array of elements, the A/B test can be considered the ultimate analysis tool.
However, in the cases that involve a detailed analysis of numerous changes made to a web page, the valuation of several elements thereof, of the consideration of three or more versions of the current web page, the application of the A/B testing tool becomes impossible. In the specified instances, the adoption of the multivariate tool is strongly recommended. The latter, in its turn, is typically identified as the a process by which more than one component of a website (or, a campaign in the offline environment) may be tested in a live environment (Bally 85). Seeing that the Yahoo! page in question has a large number of items, which require testing and, perhaps, certain changes in their design and usability, the adoption of the multivariate testing should be considered as an adequate step to be taken.
The multivariate test seems to be a much more appropriate choice for the site in question, as the evaluation of its efficacy presupposes assessing a range of variables. While the A/B test can be deemed as one of the most useful tools and elegant methods of assessing the performance of a site as well as its popularity rates among the target denizens of the population, it is still restricted in terms of the variables number. Particularly, only two of them can be assessed with the help of the A/B test (Siroker and Koomen 71). The Yahoo! e-commerce web page, in its turn, offers a much larger number of elements, each of them requiring a separate evaluation. Herein the significance of the multivariate test lies; unlike the A/B testing system, it offers the tools, which will help consider every single item. For instance, the web page in question has a header, a sign-in form, a link to the personal mailbox, a pricing table, and a link for a paid subscription. The items listed above are far too numerous to be handled with the help of an A/B test. Thus, the use of the multivariate tool is highly recommended.
Works Cited
Bally, Sven. Conversion Marketing. New York City, NY: Lulu.com. 2015. Print.
Frick, Tim and Kate Eyler-Werve. Return on Engagement: Content Strategy and Web Design Techniques for Digital Marketing. New York City, NY: CRC Press, 2014. Print.
MacFarland, Coin. Experiment!: Website Conversion Rate Optimization with A/B and Multivariate Testing. Berkeley, CA: New Riders, 2012. Print.
Scobey, Porter and Pawan Lingras. Web Programming and Internet Technologies: An E-Commerce Approach. Burlington, MA: Jones & Bartlett Publishers, 2012. Print.
Siroker, Dan and Pete Koomen. A/B Testing: The Most Powerful Way to Turn Clicks into Customers. New York City, NY: John Wiley & Sons, 2013. Print.
Yahoo Stores Pricing and Plans 2015. Web.
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