Dinesh K. Gauri

The Temporal and Spatial Dimensions of Price Search: Insights from Matching Household Survey and Purchase Data

Price promotions are pervasive in grocery markets. A household can respond to price promotions by effectively cherry-picking through (1) spatial price search across stores and (2) temporal price search across time. However, extant research has analyzed these two dimensions of price search only separately and therefore underestimates both the consumer response to price promotions and the impact of promotions on retail profit. In this article, the authors introduce an integrated analysis of spatial and temporal price search. They seek answers to three questions: First, what are the predictors of household decisions to perform either spatial or temporal price search? Second, how effective are the temporal, spatial, and spatiotemporal price search strategies in obtaining lower prices? and Third, what is the impact of alternative price search strategies on retailer profit? The authors use a unique data collection approach that combines household surveys with observed purchase data to address these questions. Their key results are as follows: Geography (the spatial configuration of store and household locations) and opportunity costs are useful predictors of a households price search pattern. Households that claim to search spatiotemporally avail approximately three-quarters of the available savings on average. Households that search only temporally save about the same as those that search only spatially. The negative effect of cherry-picking on retailer profits is not as high as is typically believed.

Role of word of mouth in online store loyalty

Comparing online store ratings with other e-store loyalty factors.

Benchmarking Performance in Retail Chains: An Integrated Approach

Standardizing performance expectations across different outlets within a chain, differing in their individual features, their consumers, and the nature of competition they face, can be an onerous task. We develop an integrated, nonlinear, block group-level market share model of store expectations that draws upon the existing trade area as well as store performance literatures. By incorporating and normalizing a large number of external and internal factors impacting performance, we are able to offer a means for the retailer to determine equitable standards. The model is estimated using a variation of the maximum-likelihood estimation, on a data set fashioned from several sources and aggregated at the block group and store levels. Finally, we propose a set of indices that allows us to evaluate relative performances of stores and regions given the competitive environments they face. We find that a block group-level model offers a better fit, as well as significantly richer implications, than a traditional store-level model. Results show that a significant number of stores operate well below their expected levels, an insight not obvious from the raw numbers used to report store statistics to upper management.

Measuring the Efficiency of Category-Level Sales Response to Promotions

In this study, we focus on measuring the efficiency of category-level sales response to promotions across various categories and stores. Our heterogeneous stochastic frontier model allows us to attribute portions of this efficiency to specific characteristics of the stores and categories. Using our full PEM (promotional efficiency frontier) model, we analyze the efficiency of 20 frequently bought categories of a supermarket retailer and apply it to store-category-level data. We find that the average efficiency of category and store sales response across all categories and stores is 84.34%, with low values in categories such as spreads and fresh seafood and high values in categories such as frozen entrees and meat. We find that the variation in efficiency of this sales response can be attributed to specific store and category characteristics such as selling area of store, distance to competition, number of stock-keeping units in the category, and average interpurchase time. Unobserved heterogeneity is capt...

Empirical Evidence of Revenue Management in the Cruise Line Industry

Revenue management (RM) has received considerable attention from both academic and business professionals. It encompasses several techniques regarding capacity allocation, pricing, and resource management of fixed, time-sensitive capacity. RM can be roughly divided into two categories defined by the control mechanism that increases revenue: capacity allocation or price optimization. Our work falls in the latter category. In our model, we allow for partial substitutability among products (e.g., a customer making a purchase decision may consider multiple alternatives—different departure dates, different destinations, different cabin types). We also include marketing expense in addition to prices as a lever for increasing revenue. These features are relevant to dynamic pricing in practice. The method is illustrated with booking data from a cruise company, yielding optimal advertising and prices for 300 products. The application of the model results in an increase in revenue in the range of 8%–20%.