Interaction Mining: Making Business Sense of Customers Conversations through Semantic and Pragmatic Analysis

Via the Web a wealth of information for business research is ready at our fingertips. Analyzing this – unstructured - information, however, can be very difficult. Analytics has become the business buzzword distinguishing traditional competitors from ‘analytics competitors’ who have dramatically boosted their revenues. The latter competitors distinguish themselves through “expert use of statistics and modeling to improve a wide variety of functions” (Davenport, 2006, p. 105). However, not all information lends itself to statistics and models. Actually, most information on the Web is made for, and by, people communicating through ‘rich’ language. This richness of our language is typically missed or not adequately accounted for in (statistical) analytics (e.g. Text-mining) - and so is its real meaning - because it is hidden in semantics rather than form (e.g. syntax). In our efforts of turning unstructured data into structured data, important information – and our ability to distinguish ourselves from competitors - gets lost.