Assistant Professor of Decision Sciences
021415; Field Experiments; Promotions; Big Data; Sparsity; Randomized field experiment;
The feasibility of using field experiments to optimize marketing decisions remains relatively unstudied.The authors investigate category pricing decisions that require estimating a large matrix of cross-product demand elasticities and ask the following question: How many experiments are required as the number of products in the category grows?The main result demonstrates that if the categories have a favorable structure, we can learn faster and reduce the number of experiments that are required: the number of experiments required may grow just logarithmically with the number of products.These findings potentially have important implications for the application of field experiments. Firms may be able to obtain meaningful estimates using a practically feasible number of experiments, even in categories with a large number of products.The authors also provide a relatively simple mechanism that firms can use to evaluate whether a category has a structure that makes it feasible to use field experiments to set prices.The authors illustrate how to accomplish this using either a sample of historical data or a pilot set of experiments. The authors also discuss how to evaluate whether field experiments can help optimize other marketing decisions, such as selecting which products to advertise or promote.