کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
997564 | 1481453 | 2012 | 10 صفحه PDF | دانلود رایگان |
Simple heuristics are usually deemed to be inferior to more complicated models. Although recent studies have demonstrated the usefulness of some forecasting heuristics, the questions of why and when a heuristic would work remain unaddressed. This study aims to answer such “why” and “when” questions by looking empirically at the specific context of forecasting for customer prioritization. Based on widely-applied probabilistic models, a series of simulations reveal that: (1) we are not usually able to identify the future top-XX% of customers in a customer base accurately, even if we know the exact data generation process; (2) a simple heuristic can perform as well as a probabilistic model even if the model maps the data generation process exactly; (3) the relative performances of the model and the heuristics can be explained by several easily-obtainable descriptive statistics. The heuristic works because the minimal information it relies upon is relatively robust and relevant in a random world.
Journal: International Journal of Forecasting - Volume 28, Issue 2, April–June 2012, Pages 497–506