Using Segmentation to Improve Sales Forecasts Based on Purchase Intent: Which “Intenders” Actually Buy?
研究通过细分方法提升基于购买意向的销售预测准确性，发现统计细分（如CART、判别分析）优于简单聚类，并识别出不同细分群体在相同意向水平下的实际购买率差异。
The authors investigate whether the use of segmentation can improve the accuracy of sales forecasts based on stated purchase intent. The common current practice is to prepare a sales forecast by using purchase intent and observed historical patterns in purchase rates given level of intent. The authors show that the accuracy of sales forecasts based on purchase intent can be improved by first using certain kinds of segmentation methods to segment the panel members. The main empirical finding is that more accurate sales forecasts appear to be obtained by applying statistical segmentation methods that distinguish between dependent and independent variables (e.g., CART, discriminant analysis) than by applying simpler direct clustering approaches (e.g., a priori segmentation or K-means clustering). The results further reveal that meaningful segments are present and identifiable that vary in their subsequent purchase rates for a given level of intent. This identification has important implications for areas such as target marketing, as it indicates which customer segments will actually fulfill their intentions. One key substantive finding is that households in a (demographic/product-usage-based) segment having an a priori high propensity to purchase are also more likely to fulfill a positive intention to purchase.