Identifying latent dissatisfied customers
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.
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