Abstract:

Today’s business environment is going through fundamental changes. Market saturation, increased commoditisation, shorter product life cycles and above all increased competition and business risks are influencing the way organisations see and plan to approach their customers. Customer asset value is increasingly becoming a key topic and relationship marketing is rapidly gaining importance. At the same time data warehousing technology is enabling companies to store and access large amounts of (hopefully) clean data on their customers. Knowledge discovery algorithms allow the detection of hitherto unknown patterns within these data. This paper shows how the combination of “soft” data from customer surveys with “hard” data from databases can be combined within one single data model. This data model will allow powerful customer analyses, such as segmentations based on needs, profitability and attrition. Additionally causal modelling to find drivers of retention, forecasting up-selling potential, prospect acquisition and return on quality analyses will be reviewed.