Use of Bayesian belief networks to help understand online audience Waldek Jaronski, Josee Bloemer, Koen Vanhoof and Geert Wets Limburgs Universitair Centrum, Universitaire Campus - gebouw D B-3590 Diepenbeek, Belgium e-mail: {waldemar.jaronski,jose.bloemer,koen.vanhoof, geert.wets}@luc.ac.be 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. Introduction Today’s online companies face a major concern in losing their audience. Recent empirical studies by Mainspring and Bain & Company [16] have demonstrated that the average customer must shop four times at an online store before the store profits from that customer. Confronted with low customer retention rates, marketing managers of online companies try to find answers to the following questions: “What drives loyalty at my website?, What are the shortcomings of the service of my website?, What are the most important characteristics of my target audience?” Very often online e-businesses possess a high volume of web traffic- and transaction data. Also valuable information regarding visitor opinions and attitudes towards the service and the website itself are available by surveying online. Additionally, sociodemographic data can help to differentiate between customer segments. A combination of all these data may help to understand the drivers of loyalty with respect to different segments [e.g., 15]. However, to our knowledge, a specialized tool for the optimal analysis of such data is virtually non-existent. Standard tools based on aggregate website statistics fail to deliver superior knowledge facilitating taking strategic decisions. Companies are urgently seeking for intelligent methods to analyze the data in an efficient and insightful manner. The contribution of the present work