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Multivariate landing page optimization using hierarchical bayes choice-based conjoint

 
: Schreiber, Stefanie; Baier, Daniel

:

Lausen, B. ; Gesellschaft für Klassifikation:
Data science, learning by latent structures, and knowledge discovery : Selected papers presented during the European Conference on Data Analysis (ECDA 2013), Luxembourg, 10-12 July 2013
Berlin: Springer, 2015 (Studies in classification, data analysis and knowledge organization)
ISBN: 978-3-662-44982-0 (Print)
ISBN: 978-3-662-44983-7 (Online)
ISBN: 3-662-44982-X
S.465-474
European Conference on Data Analysis (ECDA) <1, 2013, Luxembourg>
Englisch
Konferenzbeitrag
Fraunhofer FIT ()

Abstract
Landing pages are defined to be the home page of a website (e.g., an online shop) or a specific webpage that appears in response to an ad. Their design plays an important role in decreasing the number of visitors leaving the website without any activity (e.g., clicking a banner, purchasing a product). For improving landing pages, the traditional A/B testing approach offers a simple but limited solution to evaluate two different variants. However, recently, new approaches have been introduced. Webpages with multiple variations of website elements (e.g., navigation menu, advertising banners) generated through experimental designs are rated by customers (Gofman et al., J. Consum. Mark. 26(4):286–298, 2009).The paper explores a new approach for multivariate landing page optimization using hierarchical Bayes choice-based conjoint analysis (CBC/HB) that combines the potential to test a large number of variants with a short survey. The new approach is discussed and applied to improve the online shop of a popular German Internet pharmacy. Choice data are collected from a large sample of customers. From the results an optimal landing page is derived and implemented.

: http://publica.fraunhofer.de/dokumente/N-408867.html