Creating Micro-Marketing Pricing Strategies Using Supermarket Scanner Data

Abstract

Micro-marketing refers to the customization of marketing mix variables to the store-level. This paper shows how prices can be profitably customized at the store-level, rather than adopting a uniform pricing policy across all stores. Historically, there has been a trend by retailers to consolidate independent stores into large national and regional chains. This move toward consolidation has been driven by the economies of scale associated with these larger operations. However, some of these large chains have lost the adaptability of independent neighborhood stores. Micro-marketing represents an interest on the part of managers to combine the advantages of these large operations with the flexibility of independent neighborhood stores. A basis for these customized pricing strategies is the result of differences in interbrand competition across stores. These changes in interbrand competition are measured using weekly store-level scanner data at the product level. Obviously, this presents a huge estimation problem, since we wish to measure substitution between each product at a store-level. For a chain with 100 stores and 10 products in a category we would need to estimate over 100,000 parameters. To reliably estimate these individual store differences we phrase our problem in a hierarchical Bayesian framework. Essentially, each store-level parameter can be thought of as a combination of chain-level and random store-specific effects. The improvement in estimating this model comes from exploiting the common chain-level component. In addition, we relate these store-specific changes to demographic and competitive characteristics of the store's trading area, which helps explain why these differences are present. These estimated differences in price response are in turn used to set store-level prices. To simplify and focus the problem we limit our attention to everyday price changes (i.e., the prices of products that are not advertised). There are many marketing variables that can be adjusted at a storelevel (e.g., promotions and product assortments); the reason we concentrate upon everyday pricing is driven by its importance in the marketing mix, that most profits are earned on products sold at their everyday price, and the amenability of everyday prices to store-level customizations. A limitation of this approach is that it yields only a partial solution to the retailer's global optimization problem. A challenge for the retailer in implementing micro-marketing pricing strategies is to retain a consistent image while altering prices that adapt to neighborhood differences in demand. Our approach is to search for price changes that leave image unchanged. We argue that a sufficient condition for holding the input to store image constant from everyday pricing is to hold average price and revenues at their current levels. We implement this condition by introducing constraints into the profit maximization problem. Future research into store choice may yield more efficient conditions. A benefit of holding the retailer's image constant is that it does not require costly new information about competitors and promotional activity. Instead, retailers are able to derive these store-level customizations based largely upon scanner data. This is very advantageous since this information is already being collected and is readily available. Our results indicate that micro-marketing pricing strategies would be profitable and could increase gross profit margins by 4 percent to 10 percent. When these gross profit gains are considered after administrative and operating costs are taken into account, they could increase operating profit margins by 33 percent to 83 percent. These gains come from encouraging consumers through everyday price changes to switch to more profitable bundles of products, and not through overall price changes at the chain-level. These results show that the information contained in the retailer's store-level scanner data is an under-utilized resource. By exploiting this information using newer and more powerful computational techniques managers can better appreciate its value. The implication is that profits could be increased and gains can be made by using this information as the basis for micro-marketing.