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Title: Business oriented data analytics: theory and case studies.
Other Titles: Business oriented data analytics: theory and case studies.
Authors: Verbraken, Thomas; M0330093
Issue Date: 12-Sep-2013
Abstract: This PhD thesis focuses on predictive analytics in a business environment. Unlike explanatory modeling, which aims at gaining insight into structural dependencies between variables of interest, the objective of predictive analytics is to construct data-driven models that produce operationally accurate forecasts. Such a predictive analytics tool consists of two components, (1) data-driven models designed to predict future observations and (2) methods to assess the predictive power of such models. This dissertation focuses on a sub domain of predictive analytics: binary classification. Hence, two components are of interest: the classification models themselves, and the classification performance measures. We argue that profitability should be integrated into both components. Furthermore, we propose an approach which looks at benefits and costs, instead of misclassification costs alone.By focusing on benefits (and profit) rather than costs, we are staying closer to the business reality, and aid the adoption of classification techniques in the industry. Therefore, a profit-based classification performance measure is developed and applied to real life business cases. Moreover, an exploratory study on the incorporation of the profitability criterion into the model building step is presented. Finally, this PhD thesis discusses two case studies which clearly demonstrate the usefulness of data analytics in a business context.
Publication status: published
KU Leuven publication type: TH
Appears in Collections:Research Center for Management Informatics (LIRIS), Leuven

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