- Non-technical, but informative introduction to impact evaluation for assessing the effect of interventions (e.g. discounts) on business outcomes (e.g. sales) for decision support
- Different evaluation designs: 1) experiments (A/B testing); 2) “instrumental variable” designs for fixing “broken” experiments; 3) “selection-on-observables” designs based on groups with and without intervention that are similar in observed characteristics; 4) “difference-in-differences” designs based on groups with comparable time trends of business outcomes; “regression discontinuity” designs based on indices (e.g. customer score) which determine the receipt of an intervention (e.g. fidelity card)
- Machine learning-based impact evaluation for detecting and optimally targeting customer segments for which interventions are particularly effective (e.g. loyal customers)
- Business cases and practical examples with real data using graphical interfaces in web applications or the no-code software “BigML” – no programming required!
- Prof. Dr. Martin Huber, University of Fribourg
Subject Area Co-ordinator
- Prof. Dr. Michael Burkert, University of Fribourg
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