USING PROBABILITY MODELS FOR FAST-MOVING CONSUMER GOODS SALES FORECASTING BASED ON TRANSACTIONAL DATA
Authors
Keywords
probability models, customer-base analysis, transactional data
Summary
The interest in modelling the structure of consumer preferences and consumer choice behaviour in order to predict future purchase decisions has a long and rich tradition in marketing. Finding a practical solution to this problem is becoming increasingly critical with the increased availability of customer transactional individual-level databases. The focus of this paper is on the application of specific class of probability models that are well-suited to meet the rising challenges a marketing manager faces. Our objective is to define, estimate and a priori validate a predictive probability model by aggregating the individual-level purchase history (i.e. purchase frequency and recency). We demonstrate the relatively high performance of NDB and BG/NDB models using transactional data of a cohort of new customers. This supports our thesis statement that probability models are very suitable for identifying and forecasting purchasing behaviour. The availability of precise individual-level predictions concerns any serious attempt to derive customer lifetime value on a systematic basis.
Pages: 28
Price: 3 Points
