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Dissertation in Progress
Dissertation in Progress
Retail Promotions: Consumers’ Effectiveness in Availing them and Retailers’ Success in Using them.
Research Team: Dinesh Kumar Gauri (PhD student, University at Buffalo).
Details: Weekly price promotions are a pervasive feature of grocery retailing. A.T. Kearney (2005) estimates that retailers spend from 5 to 10% of their gross revenues in a category on promotions. A.C. Nielsen (2006) estimates that U.S. retailers spent 26.7 billion dollars on promotions in 2005. Given such widespread use and the magnitude of the dollars spent, managers and academicians have a great interest in understanding how consumers react to retail price promotions and how it affects retailer profitability. In designing promotions and allocating the limited resources across various promotion types, retailers also have to know which performance measure is being affected most and by which type of promotion. In this dissertation, we investigate the role of retailer promotion in the market place from both consumers’ and retailers’ perspectives. The first part focuses on identifying the drivers of and gains from different patterns of “cherry picking” behavior of individual consumers. The rest of the dissertation analyzes the relative effects of various types of promotional pricing strategies (e.g. loss leader, feature) on the different dimensions (traffic, sales, profit) of performance measures at aggregate (store) and disaggregate levels (category).
Specifically, the first essay attempts to answer three questions: First, how effective are the temporal, spatial and spatio-temporal price search strategies in obtaining lower prices? Second, what is the impact of alternative price search strategies on retailer profit? Finally, what are the predictors of household decisions to perform either spatial or temporal price search, both or neither? Using both survey and scanner data, we find that: Households that claim to search spatio-temporally avail about ¾ of the available savings on average; even those that claim not to systematically search on either dimension avail about ½ of the available savings. The negative effect of cherry picking on retailer profits is not as high as is generally believed. Geography and opportunity costs are useful predictors of a household’s price search pattern. In the second essay we focus on the size, determinants and profit impact of the “extreme” cherry picking segment of consumers.
In the third essay, we study the effects of various kinds of promotional pricing strategies offered by the store on its performance (traffic, sales and profit) at aggregate (store) level and whether such effects translates to disaggregate levels (category). Based on this research, among the few key things, we will be able to study the: (1) Overall impact of loss leader pricing strategy and feature pricing strategy on store performance; (2) Estimate the cross-category relationship between promotional pricing and performance. The scope of our analysis is unique both in terms of the types of promotions and the breadth of categories covered.
Use of Retail Scanner Data to Gain Strategic Market Insights: Two Applications.
Research Team: Amresh Kumar (PhD Student, University at Buffalo), Debabrata Talukdar (University at Buffalo).
Details: The general feeling in the industry has been that grocery supermarkets have been quite successful in stimulating loyalty card usage by their customers, but not much in leveraging the true benefits of loyalty card programs in using the transactional level and consumer information data from such programs to develop value-added insights for strategic decision making. There in lies the motivation of this dissertation research. The goal of this research is to demonstrate how the typical data generated from retailers’ loyalty card program can be used to gain powerful insights that are relevant not only in the context of “marketing dashboard” applications for retailers but also in terms of better understanding of retail market dynamics in academic research. Specifically, the research is organized into two essays focusing on the use of such data in the context of two distinct strategic areas for retailers: (1) price promotions; and (2) product assortment.
The backdrop for the first essay is the evidence from past research that finds consumers strategically shifting their purchase decisions (timing and quantity) to take advantage of the widely prevalent within-store inter-temporal price variation in grocery markets. However, there has been hardly any systematic study to investigate the revealed effectiveness of such strategic purchase behaviors on the part of consumers in essentially “timing” the market. The insights from such a study hold considerable implications for retailers pricing and segmentation strategy. The first essay undertakes such a study, which is similar to studies for evaluating performance of “market timing strategy” in financial equity markets. We use a scanner panel data base of over 1229 households that consists of nearly half a million purchase incidences over two years and 254 store-keeping units (SKUs) across 27 product categories. We develop multiple measures to estimate consumers’ revealed performance in “timing” the market and also to identify the primary determinants of such performance in terms of consumer, SKU and category specific characteristics. A major variable of interest here is the role of consumer learning on performance.
In the second essay, we shift our focus on gaining strategic insights into product assortments issues for grocery retailers. In recent years, the higher costs of carrying a large assortment of SKUs as well as competition from lower cost, lower assortment-carrying discount retailers have led retailers to look for more efficient assortment strategies by eliminating low-selling SKUs. A key input to formulating such strategies obviously hinges on the ability to have a good understanding of opportunity costs of SKU reductions. The second essay develops a heuristic approach to empirically estimate such opportunity costs based on the typical retail scanner data available, and without requiring a priori data from actual reductions of SKUs under analysis. The opportunity cost estimate takes into account not only the direct cost from lost profit that is directly being generated by a SKU, but also the indirect cost from its elimination. We then analyze the nature of distribution of the estimated opportunity costs of SKU reductions across product assortments and its determinants in terms of category, store and market characteristics. Finally, we investigate a key relevant dimension of underlying consumer behavior – their revealed SKU choice set size – across distinct category, store and market contexts. For our empirical analyses, we use a novel data set comprising all transaction data over 52 weeks across 14 product categories in 34 stores of a regional grocery supermarket chain.