 To model, or not to model: Forecasting for customer prioritizationChun-Yao Huang International Journal of Forecasting, 2012, vol. 28, issue 2, 497-506 Abstract: 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-X% 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. Keywords: Heuristics; Probabilistic models; Forecast accuracy; Customer prioritization; Marketing (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:28:y:2012:i:2:p:497-506 DOI: 10.1016/j.ijforecast.2011.04.004 Access Statistics for this article International Journal of Forecasting is currently edited by R. J. Hyndman More articles in International Journal of Forecasting from Elsevier |
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