Predictive Modelling and analytical Customer Relationship Management (aCRM) provide valuable information about customers and target audiences. They enable targeted controlling of customer acquisition, customer loyalty and cross- / up-selling, whereby processes can be optimized and profits maximized.

The aCRM / Predictive Modelling uses different statistical methods depending on the question. These methods analyse typical internal transactions at the customer level and are often supplemented by external fine-scaled information (e.g. purchasing power or social stratification).

Business benefits

  • Identification and description of different customer groups
  • Targeted addressing of individual customers according to customer value and preferred communication channel
  • Early recognition of potential terminating customers
  • Qualitative and quantitative identification of new-customer potentials
  • Satisfied customers due to right offers and right approaches
Analysis of customer structure and -profile

In customer analysis, data mining processes are used to determine the customer profile. This allows targeted addressing of customers for specified products via the right distribution channel. Scorecards provide the basis for informing about measures that are promising and to what extend.

Customer segmentation

Customer segmentation allows companies to get to know their customers better and address them on a personal basis. Customer analysis are often based on individual customer segments which were previously determined via customer segmentation.

Analysis of terminating customers

The analysis of terminating customers is a special form of customer analysis. It conduces the early detection of potential terminating customers and enables the company to initiate targeted actions in order to bind those costumers to the company. Suitable actions often can be derived directly from the analysis model.