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10. Scaling Response Parameters: A Multivariate Probit Approach.
Research Team: Sri Devi Duvvuri (University of Iowa) and Minakshi Trivedi (University at Buffalo).
Details: The goal of this research is to compare consumer response parameters across categories and regions to study implications of different variances in mean responses while specifying random coefficient choice models. In recent years, there has been an interest in exploring this assumption (Train 2003, Louviere et. al. 2005) with Swait and Louviere (1993) pointing out that a comparison of estimated parameters across segments and/or data sets might confound the results. In this research, we explore these issues using a multivariate probit model to estimate response sensitivities. We use scanner panel data across several categories from different regions to study these effects.
11. Consumer Preference Evolution in New Product Categories: A Multivariate Analysis of Organic Brands.
Research Team: Ram Bezawada (University at Buffalo), Minakshi Trivedi (University at Buffalo) and Deanna Wang (San Francisco State University).
Details: In this paper, we seek to understand how consumer perceptions and attitudes towards new products evolve using both survey data as well as actual purchases through scanner panel data. We thus relate attitudinal data and behavioral intent data with revealed preference data, and assess the extent of consumer preference evolution in new products at both category level and brand level. We use the organic food market which has grown rapidly over the over the past few years and was estimated to be around $20 billion in 2005, to test our model.
12. An Empirical Analysis of the Determinants of the Pricing Strategy and Format of a Retail Store.
Research Team: Dinesh Kumar Gauri (PhD student, University at Buffalo) and Minakshi Trivedi (University at Buffalo).
Details: Two powerful and highly effective strategic tools that retailers possess relate to pricing and store format decisions. From the several choices available for each decision, a retailer can choose any combination. Past research has focused mainly on consumer response to such pricing and/or formatting strategies. Surprisingly, particularly given the nature of the competitive environment the retailer faces, not much attention has been given to the issue of what makes the retailer choose a particular price/format combination in the first place. This then, is the focus of our work. Given that any price/format structure would be specific to a location and face a specific set of competitive conditions, regional and competitive factors would obviously play an important part in determining a particular price/format strategy. Based on our unique data set covering all the grocery retailers across six states, we use a multinomial logit model to study the determinants of the price/format strategy for the retailers. Results show that some combinations are more similar than others, and that the competitor’s strategy strongly influences the strategy for the entrant.
13. The Impact of Multi-Category Purchasing: The More the Merrier?
Research Team: Minakshi Trivedi (University at Buffalo)
Details: Multi category shopping by consumers is a well researched phenomenon, particularly in recent years when a combination of greater data availability and increased computing power has made the analysis far more interesting as well as much less time consuming. Not much of this research, however, looks into the issue of multi category purchasing from the retailer’s point of view, nor into the inherent presumption of ‘the more categories, the better’. Particularly when cross category purchasing is frequently motivated through promotions and rewards, additional category sales are often made at less than ‘full price.’ Is the cost of inducing consumers into additional category purchasing, in a situation where the average consumer carries 7 store (loyalty) cards and participates in several frequency programs simultaneously, too high? Is there then, a certain number or range of categories that retailers would find it optimal to reach for, beyond which the benefits decline and the opportunity cost of the promotional investment becomes too high? We propose a dynamic optimization model with the retailer’s objective being to maximize customer profitability over various categories.
14. Cumulative Sum Methods for Spatial Surveillance.
Research Team: Peter A. Rogerson (University at Buffalo), Minakshi Trivedi (University at Buffalo) and Sharmistha Bagchi-Sen (University at Buffalo)
Details: There is often interest in monitoring health within a study region where data are available for a number of sub regions. One way to carry out monitoring is to maintain separate cumulative sum charts over time for each region. A drawback of this approach is that it does not account for the possibility of clusters occurring on larger spatial scales. In this paper, we describe how monitoring may be carried out for neighborhoods constructed around each sub region. Separate charts may be kept for each sub region and its surrounding neighborhood. However, adjusting cusum thresholds for the number of sub regions is conservative, as nearby regions will have correlated charts. Here, these correlations are accounted for; an adjustment for the number of effectively independent charts is made using the theory of smoothed Gaussian random fields, and the approach is evaluated.
15. An Empirical Investigation of the Pareto Principle in the Supermarket Industry Stores.
Research Team: Jeremy Campbell (UG student University at Buffalo), Dinesh Kumar Gauri (PhD student, University at Buffalo), Debabrata Talukdar (University at Buffalo) and Minakshi Trivedi (University at Buffalo).
Details: The conventional wisdom of the Pareto Principle, more commonly known as the “80/20 rule”, has seen applications across a variety of contexts. Our study focuses on a market segmentation aspect which holds that about 80% of a firm’s sales come from only about 20% of its customers. Such “concentration” of sales revenue from a relatively small group of customers has obvious strategic implications for firms’ decisions regarding target marketing and customer service resource allocation. In this study, we conduct a detailed empirical investigation to investigate the robustness of the 80/20 rule at various levels of aggregation: (1) store level; (2) individual product category levels within a store; and (3) individual leading brands across various product categories within a store.
16. An Integrated Model to Explain Customer Relationships.
Research Team: Sekar Raju (University at Buffalo), and Professor H. Rao Unnava (Ohio State University).
Details: The marketing literature has focused a lot on understanding the role satisfaction plays in building brand loyalty and ensuring repeat purchases. However, data from the field suggests that satisfaction surveys do not do a good job in explaining repeat purchase or the likelihood of customer defection. This research project attempts to integrate other components that affect the decision to continue a relationship with a store/firm. Specifically, we plan to integrate satisfaction with sunk costs and interdependencies between the customer and the store/firm. This model of customer relationship is expected to predict customer loyalty and repeat purchases better than existing models.
17. Objective vs. Subjective Choice Variety: Have Traditional Objective Measures Overestimated (or Underestimated) Choice Variety?
Research Team: Kalpesh Desai (University at Binghamton) and Minakshi Trivedi (University at Buffalo). Authors listed alphabetically.
Details: The past two decades have seen the development of a rich stream of literature in variety seeking in both behavioral as well as modeling domains. Both sub-streams have adopted an “outside-in” perspective to measuring choice variety. That is, using a researcher–defined measure of choice variety, consumers are classified as more vs. less variety seeking based on their purchase history. It is not clear, however, if the consumers choosing the alternatives “subjectively” see these alternatives as different. Even though few studies have used subjective measures of choice variety, no concurrence with objective measures has been sought. The primary objective of this research, then, is to reconcile these two measures of choice variety and address the following fundamental question: Have traditional objective measures of choice variety overestimated or underestimated the choice variety for high variety seekers? Discrepancy between the objective and subjective measures of choice variety hold important implications for market structure differences between the high and low variety seeking segments that prior research has not examined. In addition, the discrepancy also holds important implications for the extent of stimulation provided by brand switching and the role of assortment variety in influencing choice of high variety seekers.
18. Effect of Store Brand Patronage on Store Patronage.
Research Team: K. Sudhir (Yale University) and Debabrata Talukdar (University at Buffalo). Authors listed alphabetically.
Details: We investigate the relationship between a household's store brand patronage and store patronage through its impact on store revenues and profits. The nature of the relationship will help answer the question: Do store brands contribute to greater store differentiation or to greater price sensitivity in the market? Our initial analyses (published in Review of Industrial Organization, 2004) show support for the store differentiation argument. We are currently working on a more comprehensive analysis.