Issues in the estimation and application of latent structure models of choice

Our paper provides a brief review and summary of issues and advances in the use of latent structure and other finite mixture models in the analysis of choice data. Focus is directed to three primary areas: (1) estimation and computational issues, (2) specification and interpretation issues, and (3) future research issues. We comment on what latent structure models have promised, what has been, to date, delivered, and what we should look forward to in the future.     

Evaluation of subsidiary marketing performance: combining process and outcome performance metrics

Issues in evaluating marketing performance and devising appropriate metrics for measurement have taken center stage in marketing thought and practice in recent years. We propose an empirical model that enables a multinational enterprise (MNE) to assess the marketing performance of its subsidiaries, taking into explicit consideration the fact that tactical actions by subsidiaries contribute to the creation of assets that can be harnessed for marketing outcomes. Thus, our model captures the asset creation abilities of marketing expenditures and also takes in to account the environmental differences of the context in which each MNE subsidiary operates. We evaluate comparative, overall, and process-level (creation of market assets and market yield) marketing performance in the context of multi-country operations. This simultaneous examination of marketing process and marketing outcome performance enables a global corporation to gain strategic, operational, and diagnostic insights into the performance of its subsidiaries. Our approach is empirically illustrated with an evaluation of the marketing performance of subsidiaries of a large global corporation.     

Assessing the Market Value of Real Estate Property with a Geographically Weighted Stochastic Frontier Model

In this study we consider the problem of sellers, buyers and real estate appraisers in determining the price for a house, taking into account the characteristics of the house and its location as well as the goals of these three different parties. The appraiser's job is to determine the fair market value of the house, while the buyer and seller want to find, respectively, the lowest and highest feasible price for it. We combine recent developments in geography and econometrics to develop an approach that determines local estimates of property values from the perspectives of the buyer, seller and appraiser, taking into account the characteristics of the house as well as its location. We illustrate our approach analyzing closing prices in one residential real estate market.     

Incorporating choice dynamics in models of consumer behavior

This paper presents a framework for organizing and discussing factors influencing consumer choice dynamics, how these factors may be incorporated into models of buyer behavior and problems that may arise in estimating such models. The paper identifies research issues and delineates possible approaches. Proceedings of Session on Choice Dynamics at the Banff Symposium on Consumer Decision-Making and Choice Behavior. All authors share equally in content and remaining errors.     

Country of origin: A competitive advantage?

Country of origin has been identified in the literature as an important cue that might be used by global marketers to influence consumers' valuation of the brand. Its effect on consumer perceptions, affect and behavioral intentions has been widely documented, based on consumer surveys and laboratory experiments. Despite this empirical evidence, we argue that country of origin is only one extrinsic cue among many extrinsic and intrinsic cues available to the consumer in a real purchase situation. Furthermore, in real life, consumers are likely to engage in some level of information search, which would further dilute the country of origin effect in the marketplace. Based on these arguments, we conclude that country of origin might not necessarily lead to a competitive (dis)advantage in terms of a price premium or discount. For a sample of products, we show that the objective product quality varies significantly by country of origin, and that these differences are consistent with extant research on country of origin effects on consumers' perceptions. After controlling for quality differences across brands, we demonstrate that marketers from different countries charge prices that are justified by differences in product quality. Price premiums or discounts are therefore explained by differences in product quality rather than the image effect produced by the country of origin cue.     

Challenges and opportunities in high-dimensional choice data analyses

Modern businesses routinely capture data on millions of observations across subjects, brand SKUs, time periods, predictor variables, and store locations, thereby generating massive high-dimensional datasets. For example, Netflix has choice data on billions of movies selected, user ratings, and geodemographic characteristics. Similar datasets emerge in retailing with potential use of RFIDs, online auctions (e.g., eBay), social networking sites (e.g., mySpace), product reviews (e.g., ePinion), customer relationship marketing, internet commerce, and mobile marketing. We envision massive databases as four-way VAST matrix arrays of Variables?×?Alternatives?×?Subjects?×?Time where at least one dimension is very large. Predictive choice modeling of such massive databases poses novel computational and modeling issues, and the negligence of academic research to address them will result in a disconnect from the marketing practice and an impoverishment of marketing theory. To address these issues, we discuss and identify the challenges and opportunities for both practicing and academic marketers. Thus, we offer an impetus for advancing research in this nascent area and fostering collaboration across scientific disciplines to improve the practice of marketing in information-rich environment.     

Viewer Preference Segmentation and Viewing Choice Models for Network Television

Abstract

Individual viewing decisions have a direct impact on the media planning of television advertisers and, consequently, on the revenues of the major television networks. This paper represents an attempt to better understand these decisions. We use Nielsen people meter data to build a perceptual space for programs. That space is then used to develop models explaining viewers' decision to watch television and their choice of programming. The program-choice model is a clusterwise logit model which searches for segments with similar viewing preferences. A segment-level logit model is then used to model the on-off decision. These models can be used by advertisers and advertising agencies to understand the viewing audience better, and thus to help guide their advertising media placement decisions. The models can also help television networks design programs and program schedules that are more attractive to viewers (and thus advertisers).     

Sequential market basket analysis

Market basket analysis (MBA) is a powerful and common practice in modern retailing that has some limitations stemming from the fact that it infers purchase sequence from joint-purchasing data. However, internet retailers automatically collect purchase-sequence data from their shoppers, and new technology is available for traditional (bricks and mortar) retailers to do the same, making it possible to analyze purchase sequences, rather than inferring them from joint purchases. This study first compares and contrasts traditional market basket analysis with a sequential extension, and then proposes a framework for purchase-sequence analysis, which is illustrated utilizing shopping trip data from one grocery store.     

Chain-wide and store-level analysis for cross-category management

When planning and implementing their price-promotions strategy, retail chain managers face the typical dilemma of “thinking globally, but acting locally.” In other words, they must plan their strategy, keeping in mind the global chain-level impact of their promotions, to deliver on the commitments made to manufacturers. At the same time, managers need to make sure that the implementation of such strategy takes into account the fact that each store caters to a different market with different needs and responses to marketing programs. Moreover, the retail chain manager must consider not only how the promotion of a brand affects competing brands and total category sales, but also how it could affect sales in other categories.Our proposed model addresses these two important aspects of chain-wide and store-level cross-category analysis. First, our proposed factor-regression model takes store differences and longitudinal market shifts into account, thereby providing the retail chain manager with unbiased global, chain-level estimates. It also provides stable local estimates of cross-category promotion effects at the store level. Second, while allowing this flexibility, our proposed model is parsimonious enough over existing alternatives, making it particularly useful for chain-wide and store-level cross-category analysis.We apply the proposed model to store-level data from one retail chain, comparing it with several competing approaches, and demonstrate that it provides the best balance between flexibility and parsimony. Most importantly, we show that the proposed model provides useful insights regarding cross-category effects at the chain-level, for individual stores, and their patterns across stores.