Online businesses possess high volumes of web traffic and transaction data. Often valuable data regarding visitor opinions and attitudes towards the service and the website itself are also 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 intelligent methods to analyze data in an efficient and insightful manner. The contribution of the present work consists of the introduction of an innovative and promising method 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 suggested solution yields also interesting practical results helping to better understand what is really going on at the website.
Data mining is the automated search for hidden previously unknown and interesting knowledge from large databases. This paper describes the use of data mining in the domain of customer satisfaction studies more specifically to tackle the problem of identifying latent dissatisfied customers. To do this an association rule algorithm is being used to identify the characteristics of dissatisfied customers. Satisfied customers with approximately identical characteristics might have a high probability to become dissatisfied in the near future. This knowledge can be used by domain experts to develop action plans in order to prevent these customers from actually becoming dissatisfied.