Instant Customer Base Analysis: Managerial Heuristics Often “Get it Right”
比较了随机客户基础分析模型（如Pareto/NBD）与企业常用的简单启发式方法，发现简单方法在多数管理决策上表现不差，仅在全客户群未来购买预测上略逊，建议谨慎使用当前模型。
Recently, academics have shown interest and enthusiasm in the development and implementation of stochastic customer base analysis models, such as the Pareto/NBD model and the BG/NBD model. Using the information these models provide, customer managers should be able to (1) distinguish active customers from inactive customers, (2) generate transaction forecasts for individual customers and determine future best customers, and (3) predict the purchase volume of the entire customer base. However, there is also a growing frustration among academics insofar as these models have not found their way into wide managerial application. To present arguments in favor of or against the use of these models in practice, the authors compare the quality of these models when applied to managerial decision making with the simple heuristics that firms typically use. The authors find that the simple heuristics perform at least as well as the stochastic models with regard to all managerially relevant areas, except for predictions regarding future purchases at the overall customer base level. The authors conclude that in their current state, stochastic customer base analysis models should be implemented in managerial practice with much care. Furthermore, they identify areas for improvement to make these models managerially more useful.