Prof. Artinger at Symposium on Forecasting
Prof. Dr. Florian Artinger, Head of Program, B.A. Digital Business & Management at Berlin International, will participate in the 42nd International Symposium on Forecasting, which is taking place from July 10th to July 13th, 2022 in Oxford, England.
In his keynote address, he will talk about the most important findings with regards to his research project “Predicting Revenues with the Multiplier Heuristic”.
About the International Symposium on Forecasting
The International Symposium on Forecasting, presented by the International Institute of Forecasters (IIF) provides the opportunity to interact with the world’s leading forecasting researchers and practitioners. The attendance is large enough so that the best in the field are attracted, yet small enough that everyone is able to meet and discuss one-on-one. The Symposium offers a variety of networking opportunities, through keynote speaker presentations, academic sessions, workshops, and social programs. In addition, representatives of leading publishing, software, and other related companies are on hand to discuss their most recent offerings.
About the research project
Predicting Revenues with the Multiplier Heuristic
Forecasts by statistical and machine learning methods are usually regarded as superior over those made by experts. Nonetheless, many experts still rely on simple heuristics. Are there conditions under which expert forecasts based on heuristics can match the performance of statistical and machine learning methods? We examine the case of predicting revenues per customer in 20 data sets where experts rely on the following heuristic: multiply the revenue observed in the first days by a constant. We find that with limited sample size and a shorter prediction horizon that the heuristic can perform on par or even outperform statistical and machine learning methods. Given unpredictable changes over time, the heuristic even performs on par given very large samples and a longer prediction horizon. The results provide insights when to rely on managerial judgment and when on statistical and machine learning methods.