Traffic from search engines is important for most online businesses, with the majority of visitors to many websites being referred by search engines. Therefore, an understanding of this search engine traffic is critical to the success of these websites. Understanding search engine traffic means understanding the underlying intent of the query terms and the corresponding user behaviors of searchers submitting keywords. In this research, using 712,643 query keywords from a popular Spanish music website relying on contextual advertising as its business model, we use a k-means clustering algorithm to categorize the referral keywords with similar characteristics of onsite customer behavior, including attributes such as clickthrough rate and revenue. We identified 6 clusters of consumer keywords. Clusters range from a large number of users who are low impact to a small number of high impact users. We demonstrate how online businesses can leverage this segmentation clustering approach to provide a more tailored consumer experience. Implications are that businesses can effectively segment customers to develop better business models to increase advertising conversion rates. IntroductionMany websites rely on search engines to drive substantial portions of their traffic. Major search engines such as Google, Bing, and Yahoo! use complex algorithms to determine the relevance of a page (Brin & Page, 1998). Websites that appear on the first page of the search results are likely to get more traffic because most users click on first-page results (Jansen & Spink, 2004). These search engines not only drive new visitors, but research has shown that repeat visitors use search engines as navigational tools (Jansen, Spink, & Pedersen, 2005). With search engines being the primary point of entry to the web for many people, the traffic from search engines is vitally important to websites. For online businesses, a visitor to their website could mean a sale, ad revenue, user registration, or exposure to branding.In the context of web searching, the set of terms for which a user searches is called the query. If a user enters a query and then clicks on a result, these query terms are embedded within the URL that is passed from the search engine to the website. This URL is called the referral URL, and the query terms within the referral URL are called the referral keywords. The webpage pointed to by the link the user clicks is called the landing page. Both the referral URL and referral keywords provide important information to the website owner. Examples of such information include where traffic is coming from (i.e., which search engine, for example), what topics searchers are most interested in, and how a particular landing page is indexed by the search engines. Therefore, it is important to understand and study the search keywords and search phrases that are bringing people from the search engines to the websites (Hackett & Parmanto, 2009). When analyzed appropriately, these referral keywords can provide insightful information about user be...
In this research, we analyze the referral queries and associated site‐search queries at the session level from searchers coming from web search engines. Findings are based on a random sample of 10,000 from a total of 327,261 searching sessions of an online Spanish entertainment business collected over the course of a five month period from March 23, 2012 to August 26, 2012. We find six searching strategies that are correlated with the type of referral keywords (i.e., search terms) used at the major search engine. Of the six, the three major searching strategies are (1) the explorers who submit a broad query on the major search engine and then submit multiple broad queries on the site‐search engine, (2) the navigators who submit a query to the major search engine that is part of a URL and then submit specific queries to the site‐search engine, and (3) the persisters who submit the exact type of query on both the search engine and the site search. Implications for this research include developing better internal searching features, sponsored search keyword generation, and personalization of website content.
In this research study, the authors investigate the association between external searching, which is searching on a web search engine, and internal searching, which is searching on a website. They classify 295,571 external – internal searches where each search is composed of a search engine query that is submitted to a web search engine and then one or more subsequent queries submitted to a commercial website by the same user. The authors examine 891,453 queries from all searches, of which 295,571 were external search queries and 595,882 were internal search queries. They algorithmically classify all queries into states, and then clustered the searching episodes into major searching configurations and identify the most commonly occurring search patterns for both external, internal, and external-to-internal searching episodes. The research implications of this study are that external sessions and internal sessions must be considered as part of a continuous search episode and that online businesses can leverage external search information to more effectively target potential consumers.
In this research, we investigate the relationship between external search on a major search engine and the subsequent internal search on an individual web site. Insights in the relationship can be a competitive advantage for websites. We use 295,271 searching sessions of an online Spanish entertainment business collected over a five month period. We develop a classification scheme for external and internal search queries using the referral query as the starting point.Using an n-gram approach, we identify query patterns for 295,271 searching episodes. We aggregate and identify six searching patterns. The three major searching strategies are Explorers (47%, a broad query for external search and then multiple broad queries during internal search), Navigators (16%, a navigational query for external search and then specific queries during internal search), and Acquirers (15%, transaction queries for both external and internal search). The remaining three patterns are Shifters (12%), Persisters (7%), and Orienteers (3%). Identification of searching patterns and related content can be a competitive advantage for websites dependent on providing relevant, fresh, and locatable information. Site Search -the use of a search engine typically built by the web domain or web host that allows the user to search for content only to that particular website Organic Traffic -visits referred by a major search engine based on relevance listings rather than ads Landing Page -the page that a user is directed to after clicking on a listing on the search engine results page Bounce Rate -the percentage of one page visits (i.e., the user left the site from the landing page) Time on Site -the duration of a visit to the site Referral Keyword (query) -the terms that the user typed in the search engineSponsored search -targeted, relevance-based advertisements that are displayed alongside major search engine results (e.g., Google AdWords)
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