Short course description

For effective decision making, it is crucial to evaluate the consequences or impact of specific actions or policies, whether it’s the impact of pricing strategies on sales or of employee training on productivity. This course provides an introduction to cutting-edge data-driven impact evaluation methods—a critical tool for supporting decision-making within companies and organizations.

What to expect

• 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; 5) “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!