The knowledge domain of e-commerce is characterized by informational asymmetry1 Yang, Kun (2006): A conceptual framework for semantic web-based ecommerce. o.O.2006. . The information sources for the situation analysis include internal company databases and other sources under the direct control of the company, and external sources such as websites, social media services, public databases or other Internet services2 Meimaris, Marios; Vafopoulos, Michalis N. (2012): Knowledge-BasedSemantification of Business Communications in ERP Environments. In: SSRN Journal.o.O. 2012..

Manually searching for relevant information on the internet is time-consuming and inefficient due to the large number of services and offers. The number of external information sources has grown rapidly, so that even the operators of the services are not able to fully index and link all the content3 Philipp Ciechanowicz (2007): Die Infrastruktur von Suchmaschinen am FallbeispielGoogle. In: Heinz Lothar Grob und Gottfried Vossen (Hg.): Entwicklungen im Web 2.0aus technischer, ökonomischer und sozialer Sicht. Münster (51), S. 197–206. . The information is therefore extracted using contextual analysis or automated keyword search 4 Antoniou, G.; van Harmelen, Frank (2008): A semantic Web primer. 2. Aufl.Cambridge 2008 .

By setting cookies , it is possible to determine whether the individual visitor is visiting the trading establishment for the first time or whether it is a repeat visit 5 Erlhofer, Sebastian (2011): Suchmaschinen-Optimierung. Das umfassende Handbuch ;[Grundlagen, Funktionsweisen und Ranking-Optimierung ; Planung und Durchführungfür Google und Co. ; Konversionsraten steigern, Google AdWords, Web Analytics ; dasStandardwerk, vollständig überarbeitet]. 5. Aufl. Bonn 2011. . Furthermore, the use of cookies allows individual search queries to be uniquely assigned to the visitor.

Hypertext Transfer Protocol ( HTTP ) also makes it possible to determine the website that redirected the visitor to the e-commerce application ("referrer") .

Individual page views or HTTP requests are typically logged in log files. These contain the IP address, time, and other details of the request .

By using Javascript, it is possible to determine the technical characteristics of the devices (e.g. screen resolution, browser version, possibly also manufacturer and model of the device) that the visitor used to access the website . Web 2.0 services offer consumers the opportunity to exchange information in forums, chats, blogs, etc. 6 Netzer, O.; Feldman, R.; Goldenberg, J.; Fresko, M. (2012): Mine Your OwnBusiness: Market-Structure Surveillance Through Text Mining. In: Marketing Science31 (3), S. 521–543. There are many different types of Web 2.0 offerings (see Figure 2). Consumer interactions in Web 2.0 leave behind large amounts of product- and company-related information on the internet, such as product experiences, expectations, and suggestions for product design. This “user-generated content” could include information about the market structure, demand and competitive situation.

However, most of the product and company-related information in these services is in the form of text or natural language and is therefore not directly accessible to automatic evaluation7 Hepp, Martin F. (2004): Product Representation in the Semantic Web. In: SSRNJournal. o.O. 2004. . Furthermore, most forums where the products are discussed do not contain quantitative evaluations (such as the number of "stars"), which makes operationalizing this information even more difficult.