The Third Wave of Marketing Intelligence
Raymond R. Burke
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Raymond R. Burke: Kelley School of Business, Indiana University
A chapter in Retailing in the 21st Century, 2010, pp 159-171 from Springer
Abstract During the last 25 years, marketing research in retail settings has been transformed by technological change. The first wave of change occurred when retailers adopted point-of-sale (POS) systems with UPC barcode scanning. This provided companies with real-time data on purchase transactions and accurate estimates of product sales and market share. Retailers used this information in combination with shelf space allocation and product inventory information to measure the productivity of their stores. By modeling these data as a function of causal variables, such as product price, display activities, and feature advertising, marketers were able to assess the performance and profitability of their marketing investments (e.g., Blattberg and Neslin 1990). UPC scanning served as the foundation for syndicated research services such as A.C. Nielsen and Information Resources, and led to the development of brand and category management. Scanner data are in widespread use today and support many critical business decisions. The second wave of change occurred when retailers started to track and analyze the purchases of individual shoppers. Some retailers, especially in the grocery industry, launched frequent shopper and customer loyalty programs to collect these data (see, e.g., chapter by Reinartz in this book). Shoppers who participate in such programs typically identify themselves with loyalty cards at the point of sale in exchange for price discounts or other incentives. Companies can also identify repeat customers by requesting their telephone numbers, capturing information from credit and debit cards, reading “cookies” stored on their computer disk drives, etc. This information is often combined with geodemographic and behavioral data from other public and private sources to create a profile and purchase history for each customer or household. These data can be used to estimate customer value and loyalty, measure individual-level response to direct mail and other targeted promotions, and conduct shopping basket analyses to identify product complementarities among other applications (Berson, Smith, Thearling 2000; Ravi, Raman, Mantrala this book). Once again, innovation led to the emergence of new industries (data mining, data warehousing) and new practices (customer relationship management, or CRM).
Keywords: Shopping Process; Shopping Experience; Shopping Environment; Feature Advertising; Market Intelligence (search for similar items in EconPapers)
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