When we go to a search engine and type in our search queries, what do we want to achieve? Perhaps for us search engine marketers, we wish that everyone who enters a keyword in Google and Yahoo! would end up buying something, making downloads or signing up newsletters. Of course it’s not. A lot of people use search engines to make a research and comparison checks. A study from Penn State classifies web search engine user activity as primarily informational or transactional searching.
This is the finding concluded by Jim Jansen, assistant professor in Penn State’s College of Information Sciences and Technology, worked with IST undergraduate Danielle Booth and Amanda Spink of Queensland University of Technology.
Informational searching involves looking for a specific fact or topic, navigational searching seeks to locate a specific Web site and transactional searching looks for information related to buying a particular product or service.
Out of more than 1.5 million queries from hundreds of thousands of search engines users were analyzed, findings showed that about 80 percent of queries are informational and about 10 percent each are for navigational and transactional purposes.
Jansen and his colleagues arrived at those results by selecting random samples of records and analyzing query length, the order of the query in the session and the search results. These fields helped the team develop an algorithm that classified the searches with a 74-percent accuracy rate.
“Other results have classified comparatively much smaller sets of queries, usually manually,” Jansen said. “This research aimed to classify queries automatically. Our findings have broad implications for search engines and e-commerce if they can classify the user intent of queries in real time. This is why we wanted a computational undemanding algorithm. It proves the 80/20 rule that 80 percent of the cases can be achieved with these clear-cut methods.”
The paper “Determining the informational, navigational and transactional intent of Web queries” will appear in the May 2008 issue of Information Processing & Management. The article is currently available online.
The Penn State researcher said he plans to continue this research using a more complex algorithm that will hopefully yield a 90-percent accuracy rate using similar searching criteria.