Although practitioners attribute significant sales growth to category management, many believe more potential lies untapped. This paper suggests improvements through the use of consumer behavior research as a supplement to point-of-purchase scanner information. In particular, we outline several concepts and theories with special promise in six decision areas of category management, suggesting opportunities for both future research and industry application. An empirical demonstration of one such opportunity is presented showing how two consumer behavior concepts – context effects and categorization theory – reveal insights relevant to item placement decisions within category management that would not be revealed by scanner data.
Since the early 1990s retailers, most notably grocers, and manufacturers alike have increasingly embraced a new process strategy – category management – which shifts the manager's focus from individual brands to the overall performance of a product category. The Category Management Report, prepared by the Joint Industry Project on Efficient Consumer Response (hereafter, CMR 1995), states that this change produces enhanced business results by focusing on delivering consumer value. Supporting this new strategy, academic researchers have modeled and predicted increased profits ( Zenor, 1994 and Basuroy et al., 2001). These predictions are being realized in the industry where significant dollar sales growth is attributed to its adoption—an average of 16 percent for retailers and 10 percent for manufacturers (Cannondale Associates 2003). However, these gains are largely attributed to stripping waste from the system and shifting volume across brands or time periods, rather than driving incremental consumption or trading shoppers up to higher value, higher margin items (Cannondale Associates 2002). Hence, category management is still seen as striving to reach its potential (Gregory 2001). One area where changes may improve performance is the structure of the relationship between the retailer and its lead supplier (Gruen and Shah, 2000). This paper suggests that more progress is possible through the use of consumer behavior research as a supplement to the insights derived from point-of-sale scanner data.
Table 1 provides an overview of this perspective. The first two columns are adapted from the CMR (1995) and summarize the decision stages a manager faces in applying category management. As noted in column two, the completion of each stage requires significant insight into the motivations, perceptions, and behaviors of the target consumers. To address these requirements, we add a third column which points to particular theories and concepts that could be incorporated into studies that would reveal the influential factors arising before, during and after the consumer enters the store. For managers, the insights may provide greater illumination of the problem at hand, thereby providing additional, different solutions that, ideally, enhance results. For academics, this third column highlights fertile areas of future research.To illustrate one such opportunity, we present an empirical demonstration of how two particular consumer behavior concepts – context effects and categorization theory – reveal insights relevant to item placement decisions (Stage 1 – category definition – in the category management process of Table 1) that would not be revealed by scanner data. Before presenting the demonstration and discussing its implications, the next section will review the relevance of these two concepts within the retail setting.
The current paper contributes to the growing body of academic literature on category management by demonstrating that potential gains exist through the use of consumer behavior research in the category management process. While Table 1 indicates a variety of potential areas of research, we explored the relevance of context effects and categorization theory to Stage 1 decisions concerning category definition (item placement). In particular, we demonstrate that a consumer's item evaluation process (the attribute importance weights used to form preferences) may differ depending on the product class’ categorization (context). In addition, the attribute importance weights used in some categories may also be influenced by prior exposure to the product class in another categorization. Importantly, scanner data does not capture the category from which an item is selected, therefore these insights cannot be revealed from this source.
