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19. “Extreme” Cherry Picking Behavior: Prevalence, Determinants and Impact on Retailers’ Profit
Research Team: Dinesh Kumar Gauri (PhD student, University at Buffalo) and Debabrata Talukdar (University at Buffalo). Authors listed alphabetically.
Details: In this study, the focus is on that segment of consumers who visits a retailer to buy just the deeply discounted items (viz., those items that are put on ‘loss leader’ pricing by the retailer), there by essentially generating negative profit contribution for the retailer. Since loss leader pricing strategy is a very popular promotion strategy for the grocery retailers, it becomes very important for research to focus on the following issues: (1) Estimate the size of the extreme cherry picking segment across different stores in different market contexts; (2) Identify the key market characteristic drivers of such segment size by performing an aggregate level analysis at the store level; (3) Identify the key consumer characteristic drivers of the extreme cherry picking behavior by performing a disaggregate level analysis at the consumer level; and (4) Estimate the incremental net positive impact, if any, of loss leader pricing strategy on the bottom-line of the retailer, which is likely to be the most critical information of interest to a retailer.
20. Evaluating Spatial Performance of a Supermarket Store: Insights and Implications.
Research Team: Andrei Strijnev (University of Texas, Dallas) and Debabrata Talukdar (University at Buffalo). Authors listed alphabetically.
Details: In this research, we seek to address the issue of how a retail grocery store could decide on setting “efficient” targets to evaluate its spatial sales performance. The wide prevalence of “frequent shopper” loyalty card programs by grocery retail chains means that most grocery retail stores collect data that can be used to trace back the spatial distribution of its sales. While such data will thus allow a store to evaluate its spatial sales performance over time, it does not provide any insight into how the store’s spatial sale performance compares to total sales potential in a given spatial unit. The motivation and contribution of this research is to develop a decision model that can provide such insight to store managers. The methodology used will be Stochastic Frontier Analysis for efficient performance evaluation.
21. “Timing” the Market for Frequently Purchased Goods: Consumers’ Performance and Its Determinants.
Research Team: Amresh Kumar (PhD student, University at Buffalo) and Debabrata Talukdar (University at Buffalo). Authors listed alphabetically.
Details: The backdrop for this study is the widely prevalent within-store inter-temporal price variation in markets of frequently purchased goods. Naturally, an interesting question in these markets is whether consumers form price expectations and “lie in wait” to take advantage of the inter-temporal price variations. Past research has indeed found evidence of consumers strategically shifting their purchase decisions (timing and quantity) to take such an advantage. The evidence has come in the form of purchase acceleration (or, “stockpiling”) and deceleration behaviors at individual consumer level, and in the form of pre-promotion and post-promotion dips in sales at store level. 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 strategy. The goal of this research is to undertake such a study, which is similar to studies for evaluating performance of “market timing strategy” in financial equity markets.
22. Opportunity Costs of SKU Reductions for Grocery Supermarkets: Insights and Implications for Retailers’ Product Assortment.
Research Team: Amresh Kumar (PhD Student, University at Buffalo) and Debabrata Talukdar (University at Buffalo). Authors listed alphabetically.
Details: Since the 1990s, there has been a significant proliferation in SKUs in supermarkets as both manufacturers and retailers saw it as a strategic way for increasing respective market shares. However, 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 by retailers obviously hinges on their ability to have a good understanding of opportunity costs of SKU reductions. The goal of this study is to develop a heuristic approach to empirically estimate such opportunity costs based on the typical scanner data available to a retailer from loyalty card programs, and without requiring a priori data from actual reductions of SKUs under analysis. The opportunity cost estimate will take 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.
23. Benchmarking Retail Store Performances Based on “Efficient” Rather than “Average” Performance.
Research Team: K. Sudhir (Yale University) and Debabrata Talukdar (University at Buffalo). Authors listed alphabetically.
Details: Companies set performance targets for many types of employees (e.g., sales force, store managers, hedge fund/mutual fund managers and production managers). In this research project, we seek to address the issue of how a retail chain should decide on performance targets for store managers operating in markets with diverse store characteristics (demographics, competition, store size, etc.). A common approach is to use a regression model where the performance is explained using explanatory variables such as demographic, competitive and other store characteristics. The fitted values from the regression are then used to set performance targets for store managers. But benchmarking obtained through such analysis produces performance objectives based on the “average” performance of their employees, and do not provide incentives for employees to excel. Management would prefer to infer the “efficient frontier,” the best possible performance given the store characteristics, and then use that for benchmarking. Intuitively, we can get such benchmarks by fitting a regression-type model only on stores that are operating with the greatest efficiency. Methodologically, this will require using a linear programming based approach called “Data Envelopment Analysis”.
24. Determinants of New Brand Success.
Research Team: Ram Bezawada (University at Buffalo), Vijay Ganesh Hariharan (PhD Student, University at Buffalo) and Debabrata Talukdar (University at Buffalo). Authors listed alphabetically.
Details: What determines the relative success of new brand introductions? What determines the relative propensity on the part of consumers to try and repeat purchase new brands? Here, at the aggregate level, we will look across categories to see how competitive structure, nature of extension (line or category), the relative market position of the introducing firm, etc affect the new brand market share. At the disaggregate level, we will look across categories to see how consumer differences in category consumption patterns, purchase of other brands of the introducing firm in the same or different category (kind of “firm/brand equity”), etc. affect who adopts the new brand more.
25. Optimal Pricing Policies under Demand Dependencies across Categories.
Research Team: Dinesh Kumar Gauri (PhD student, University at Buffalo)and Debabrata Talukdar (University at Buffalo). Authors listed alphabetically.
Details: The objective of this is study to investigate the interdependencies in consumer demand across product categories and develop retailer pricing strategies under circumstances where demand for two or more product categories may be correlated. Our approach would be to estimate a series of demand functions that explicitly account for the cross-category dependence in household purchase behavior. The demand models would allow for heterogeneity across households due to observed (such as demographics) as well as unobserved factors. The result from the multi-category demand models would be taken as an input to set up a pricing optimization problem for the retailer. In particular, we would extend the standard category profit maximization problem to a multi-category setting to develop a normative pricing policy for the retailers. Our hope is that the models developed in the study can be used as a decision support system for retailers equipped with frequent shopper databases for better decision making.
26. Investigating the Inter Temporal Determinants of Store Traffic Performance using a Dynamic Linear Model.
Research Team: Rajesh K. Shah (University at Buffalo) and Ram Bezawada (University at Buffalo).
Details: Store traffic is the most visible performance measure for store managers as it directly relates to sales response and other marketing mix strategies. Therefore, store managers invest in marketing programs that lead to enhanced store traffic. Thus, the ability of a store to attract customers is an important attribute that can be used as a strategic variable to compete effectively. Moreover, a reliable estimate of store count will help the store to improve the efficiency of its operations and also manufacturers in better directing their promotional effort. Given the above, store traffic is one of the key variables for retailers around which other retail activities are usually centered. Although, issues relating to store traffic have been researched in the marketing literature, they have not been dealt with in a comprehensive manner. For instance, store traffic usually has been analyzed as an outcome of other retail specific activities such as the impact of store level promotions. Examples of the above include the influence of loss-leader items (Walters and MacKenzie 1988), feature promotions (Mulhern and Leone 1990, Bodapati 1999), grocery advertisements and store flyers (Urbany et al. 2000, Gijsbrechts et al. 2003) on store traffic. Moreover, these studies have not considered the dynamic nature of store traffic. Retail strategies with respect to key factors such as store traffic are set over longer time horizons (e.g., AMR 2000). Therefore, the inter-temporal nature of such factors and the manner in which they influence store traffic needs to be explored. In this paper, we address the above issue using a dynamic linear model to account for the evolution of store traffic. We use Bayesian parameter estimation and model selection criteria to model inter temporal effects of various retailer strategies. In addition to marketing mix variables such as features, loss leaders and prices; we also consider how other store specific characteristics such as assortment levels, produce quality, organic products influence store traffic. We further include the store descriptors like physical characteristics (e.g. size, formats, personnel) and non-traditional product offerings (e.g. presence of a bank, gas stations, café). Our results will better enable retail managers to understand multilevel-determinants of store traffic and develop strategies to monitor and enhance store traffic.
27. An Empirical Investigation of the Effect of the Time of Shopping on the Inter-Purchase Time of Grocery Products.
Research Team: Kalpesh Desai (University at Binghamton) and Dinesh Kumar Gauri (PhD student, University at Buffalo). Authors listed alphabetically.
Details: To ensure steady business, retailers prefer their shoppers to shop regularly for frequently purchased products i.e., they prefer shoppers to maintain consistency in their inter-purchase time of such products. Memory literature has found evidence of the time of day effect on recall of products e.g., orange juice (beer) is likely to be recalled more during morning vs. evening (evening vs. morning) (Unnava et al. 2006). However, there is no empirical investigation if similar effects occur in the purchase of grocery products. In this research we examine the inter-purchase timing behavior of consumers across various product categories and hypothesize a three-way interaction between shopper’s basket size X shopper’s “typical” shopping time X time-related products (e.g., orange juice is a morning product and beer is an evening product). Specifically, we posit there will be greater consistency in the inter-purchase time of “matched” (mismatched) products for small (large) basket shoppers. Matched products refer to morning products purchased in morning and evening products purchased in evening. We also expect inter-purchase time consistency for time independent products (e.g., bathroom tissues, laundry detergent) to be lower among small vs. large basket shoppers. In addition to the steady business managerial implication, findings from this research will help in selecting appropriate categories for promotions based on the time of the day and in micro-targeting promotions to shoppers based on their basket size and time of shopping.