The authors empirically explore how consumers update beliefs about a store's overall expensiveness. They estimate a learning model of store price image (SPI) formation with the impact of actual prices linked to category characteristics, on a unique dataset combining store visit and purchase information with price perceptions of the same consumers. The results identify characteristics driving categories' store-price signaling power, for different store formats. ‘Big ticket’ categories, with a narrow price range, strongly shape consumers' store price beliefs, while (volatile) prices of frequently or deeply promoted categories are less influential. At traditional supermarkets, consumers anchor and elaborate on prices of storable categories bought in large quantities, and where quality differentiation is high. For hard discounters, however, SPI is mostly shaped by frequently bought categories with narrow assortments. Interestingly, categories' SPI signaling power is not proportional to their share-of-wallet at either type of chain. Managers can use these results to identify ‘Lighthouse’ categories that signal low prices, yet make up a small portion of store spending and in which price cuts do not overly hurt revenue.
- store price image
- price perceptions
- product category characteristics
- Bayesian learning