Using Segmentation to Improve Sales Forecasts Based on Purchase Intent: Which “Intenders” Actually Buy?

Abstract

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.