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Use of Bayesian belief networks to help understand online audience

By Waldek Jaronski, Josee Bloemer, Koen Vanhoof, Geert Wets, Limburgs Universitair Centrum and Universitaire Campus Gebouw D


Abstract. Online businesses possess of high volumes web traffic and transaction data. Often, also valuable data regarding visitor opinions and attitudes towards the service and the website itself are available by means of online surveys. Additionally, sociodemographic data can provide characteristics of the audience, help differentiate between customer segments and understand drivers of loyalty with respect to each segment. Faced with the potentially rich body of the three kinds of information, companies urgently seek thereby for methods to analyze them in an efficient and insightful manner. The contribution of the present work consists of the application of Bayesian network technology for the joint analysis of all these data of the aforementioned dimensions that results in meaningful and valuable marketing knowledge. At the same time, the outlined solution yields also interesting practical results helping to understand better what is really going on on the website. 1

Year: 2009
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