The essence of analytical CRM consists in learning from past customer interactions to improve future actions. The data that is collected can be later analysed to illustrate all important aspects of the customer interaction process. In the past, most CRM projects restricted the collected data to company internal process (transactional) data. The author stresses that internal data alone does not cover all important aspects of the customer interaction and further demonstrates that major information dimensions must be covered by other data sources, mainly by market research.Three different practical ways to combine (anonymous) market research data with (personalized) internal data are reviewed, respecting the restrictions of privacy protection and ESOMAR guidelines.
Customer satisfaction analyses often suffer from the fact that it is difficult to compress the large amount of gathered data material into relevant, concrete action recommendations for decision makers, whereby the practical significance and operative convertibility of the results is particularly disputed. The suggested solution basis describes how a model of factors influencing customer satisfaction, from which initial measures can be derived, can be produced using a special kind of neural network based on empirically gathered data. Relevant, precise action recommendations are derived by testing the measures using a neural network as a simulation tool. This reduces the volume of information in a customer satisfaction survey to a list of measures to be implemented by the decision maker.