Enhanced customer targeting with multi-stage models: Predicting customer sales and profit in the retail industry

Abstract

PURPOSEThis article draws insights about consumer sales and profi t from the literature in the fi elds of marketing, fi nance, retail and fi nancial services, which are used to enhance and extend the traditional targeting models deployed in marketing departments. Specifi cally, this article uses two retail consumer data sets to build and validate multi-stage targeting models, which target more than simply customer response. The most effective of these multi-stage models uses product-specifi c profi t information to predict an individual customer ' s future sales and profi ts. To preview the results, ' two-stage ' models such as those that predict customer response and spend generated up to 12 per cent more sales than response models alone. ' Three-stage ' models that predict customer response, spend and profi t targeted customers with 17 per cent more profi t ABSTRACT Drawing on the marketing, fi nance and retail research literature, this article uses a multi-stage targeting methodology to enhance the targeting of high-revenue customers. Such an investigation is important for both research and practice, as many retail-marketing departments use traditional response models to identify the optimal customers for marketing communications, despite the fact that maximum revenue is the fi nancial objective for corporations rather than response . A methodology is deployed to build multi-stage models that target the most responsive, highest-spending and highest-profi t customers using customer-and product-level profi t data from a specialty retailer in the United States. Comparisons of the competing models show that multistage models identify customers with higher actual sales and profi t than both traditional response models and other forms of multi-stage models in which any two combinations of customer response, spend or profi t are predicted. Implications and limitations of this research are discussed in light of the results.

Predictive Analytics Driven Campaign Management Support Systems

“…They are driving new sources of revenue and reducing cost [9]. However, relatively little attention has targeted time-pressured business campaigns where predictive models are deployed and re-deployed, and models can and do decay over time both within and across campaigns [5], [21], [23], [24]. In this context, there is a significant opportunity to more closely link customer demand management with supply chain management in what is now commonly referred to as 'Demand Chain Management' (DCM).…”