Frequent-user patients: reducing costs while making appropriate discharges

Homeless patients who lack access to the health resources they need to maintain their health on their own pose a challenge for hospitals: Premature discharge of such patients can result in their being readmitted to the hospital in a short time, leading to higher costs for the hospital. Hospitals can address this problem by developing clear, effective homeless discharge policies and by developing ongoing relationships with appropriate medical respite care providers. A hospital also can benefit from spearheading an initiative to develop a medical respite program, enlisting the assistance of other community stakeholders.     

Infrastructure investment and Canadian manufacturing productivity

This paper examines the relationship between Canadian public infrastructure and private output using a Constant Elasticity and Substitution-Translog (CES-TL) cost model to describe the interaction of the public and private sectors.

We find public capital a substitute for private capital within the Canadian manufacturing sector. Additionally, the services of public capital enhance the productivity of private capital. Canadian manufacturing costs are characterized by economies of scale, indicating that less than optimal plant sizes dominated Canadian manufacturing sector during the study period. Advances in disembodied technical progress are also indicated.     

Integrating the Dirichlet-multinomial and multinomial logit models of brand choice

This paper discusses the interpretative benefits that arise from merging the Dirichlet-multinomial (DM) model as a loyalty variable in the multinomial logit (MNL) model of brand choice. The estimated parameters of this hybrid model compare favorably to those of a pure DM model (with no marketing mix variables) as well as those of a standard MNL model with an exponentially smoothed loyalty measure. The hybrid model offers an index of consumer heterogeneity and estimates of each brand's preference share, adjusted for the effects of price and promotional activities. We illustrate the unique interpretations offered by these parameters across four different product categories, showing how changes in heterogeneity (across categories or model specifications) are closely related to changes in the overall impact of marketing mix variables.The author is grateful to Bruce Hardie, Barbara Kahn, Jim Lattin, Len Lodish, Bob Meyer, and Don Morrison for their comments and suggestions. He appreciates the support of Doug Honnold and Information Resources, Inc., for providing the data and computational facilities used in this paper.     

Assessing the impact of JIT using economic theory

This paper combines ideas that are well founded in the production and inventory management literature, with analytical approaches that have been long established in the economic theory literature, to reveal and explore production-function characteristic differences between JIT producers and non-JIT producers among electronic firms in Ontario, Canada. The methodology employed is the estimation of the CES-TL total cost system. Our primary conclusion is that JIT firms are more cost-efficient and appear to be distinct from the non-JIT group. This conclusion is supported by: (1) the fact that, in most cases, the elasticities calculated from the two groups of firms are significantly different; (2) the fact that the cost elasticity with respect to output is lower for the JIT firms than for the non-JIT firms, indicating that the former are better able to capture economies of scale and density; (3) the difference between the elasticities of factor productivity, with respect to output changes, shows the JIT firms as being more labor- and materials-saving than the non-JIT firms.     

When worlds collide: The implications of panel data-based choice models for consumer behavior

Panel data, both diary and scanner, have been analyzed by marketing scientists for over thirty years. One of the important uses of panel data is to better understand consumer behavior by developing and testing hypotheses using the revealed preference data rather than experimental data that uses only self-reported behavior or behavior in a simulated choice environment. The purpose of this paper is to suggest areas of research where panel data can be used to better understand the underlying behavior of the panel members.     

An exploratory look at supermarket shopping paths

We present analyses of an extraordinary new dataset that reveals the path taken by individual shoppers in an actual grocery store, as provided by RFID (radio frequency identification) tags located on their shopping carts. The analysis is performed using a multivariate clustering algorithm not yet seen in the marketing literature that is able to handle data sets with unique (and numerous) spatial constraints. This allows us to take into account physical impediments (such as the location of aisles and other inaccessible areas of the store) to ensure that we only report feasible centroid paths. We also recognize that time spent in the store plays an important role, leading to different cluster configurations for short, medium, and long trips. The resulting three sets of clusters identify a total of 14 “canonical path types” that are typical of grocery store travel, and we carefully describe (and cross-validate) each set of clusters. These results dispel certain myths about shopper travel behavior that common intuition perpetuates, including behavior related to aisles, end-cap displays, and the “racetrack.” We briefly relate these results to previous research (using much more limited datasets) covering travel behavior in retail stores and other related settings.     

Probability Models for Customer-Base Analysis

As more firms begin to collect (and seek value from) richer customer-level datasets, a focus on the emerging concept of customer-base analysis is becoming increasingly common and critical. Such analyses include forward-looking projections ranging from aggregate-level sales trajectories to individual-level conditional expectations (which, in turn, can be used to derive estimates of customer lifetime value). We provide an overview of a class of parsimonious models (called probability models) that are well-suited to meet these rising challenges. We first present a taxonomy that captures some of the key distinctions across different kinds of business settings and customer relationships, and identify some of the unique modeling and measurement issues that arise across them. We then provide deeper coverage of these modeling issues, first for noncontractual settings (i.e., situations in which customer “death” is unobservable), then contractual ones (i.e., situations in which customer “death” can be observed). We review recent literature in these areas, highlighting substantive insights that arise from the research as well as the methods used to capture them. We focus on practical applications that use appropriately chosen data summaries (such as recency and frequency) and rely on commonly available software packages (such as Microsoft Excel).