Turning Datamining into a Management Science Tool: New Algorithms and Empirical Results
提出一种结合市场响应模型与数据挖掘的新算法，用于SKU级战术规划预测。基于95家零售连锁的促销数据，该算法在49%的促销活动中准确预测了所需商品箱数，并在82%的活动中误差在±1箱以内。
This article develops and illustrates a new knowledge discovery algorithm tailored to the action requirements of management science applications. The challenge is to develop tactical planning forecasts at the SKU level. We use a traditional market-response model to extract information from continuous variables and use datamining techniques on the residuals to extract information from the many-valued nominal variables, such as the manufacturer or merchandise category. This combination means that a more complete array of information can be used to develop tactical planning forecasts. The method is illustrated using records of the aggregate sales during promotion events conducted by a 95-store retail chain in a single trading area. In a longitudinal cross validation, the statistical forecast (PromoCast™) predicted the exact number of cases of merchandise needed in 49% of the promotion events and was within ± one case in 82% of the events. The dataminer developed rules from an independent sample of 1.6 million observations and applied these rules to almost 460,000 promotion events in the validation process. The dataminer had sufficient confidence to make recommendations on 46% of these forecasts. In 66% of those recommendations, the dataminer indicated that the forecast should not be changed. In 96% of those promotion events where “no change” was recommended, this was the correct “action” to take. Even including these “no change” recommendations, the dataminer decreased the case error by 9% across all promotion events in which rules applied.