Chain-store pricing and the structure of retail markets

This paper investigates competition between chain-stores and independents in the UK opticians' industry, using the relationship between the number of outlets present in a local market and the market size. Chain-stores are shown to have a significant effect on local market competition. In addition, the empirical approach is extended to allow inferences on the nature and extent of product differentiation. The results are broadly consistent with a model of vertical product differentiation in which chain-stores adopt national pricing strategies. The evidence suggests that the nature of competition between independent retailers depends on whether a chain-store is present.

A comparison of sales response predictions from demand models applied to store-level versus panel data

In order to generate sales promotion response predictions, marketing analysts estimate demand models using either disaggregated (consumer-level) or aggregated (store-level) scanner data. Comparison of predictions from these demand models is complicated by the fact that models may accommodate different forms of consumer heterogeneity depending on the level of data aggregation. This study shows via simulation that demand models with various heterogeneity specifications do not produce more accurate sales response predictions than a homogeneous demand model applied to store-level data, with one major exception: a random coefficients model designed to capture within-store heterogeneity using store-level data produced significantly more accurate sales response predictions (as well as better fit) compared to other model specifications. An empirical application to the paper towel product category adds additional insights. This article has supplementary material online.

Drawing association rules between purchases and in-store behavior:an extension of the market basket analysis

Shopping behavior is often exclusively studied through consumer purchases, since they are an easily measurable ouput. Still, the observation of in-store physical behavior (paths, moves and actions) is crucial, as is the quantification of its impact on purchases. Using an innovative PDA tool to precisely record and time stamp consumer’s moves and gestures, we extend the classical Market Basket Analysis (MBA) by integrating this new kind of information. We draw associations not only from purchases but also from in-store consumer moves and actions. We compare results of our new method with classical MBA results and show a significant improvement.

Exploring the link between In-store physical shopping behavior and purchases

Only little research investigates the relationship between consumer purchases and in-store physical shopping behavior, largely because of the difficulty involved with reconciling a precise observation of in-store behavior with a robust statistical analyses of the data. Using an innovative data collection method, this article determines that physical shopping behavior manifests itself along two main dimensions: shopping width (behavioral scope throughout the store) and shopping depth (specific store elements). Both dimensions have strong impacts on purchases: the former tends to influence the number of items bought, and the latter affects the price of purchased items, depending on the product category.

Drawing association rules between purchases and in-store behaviour:an extension of the market basket analysis

Shopping behavior is often exclusively studied through consumer purchases, since they are an easily measurable ouput. Still, the observation of in-store physical behavior (path, moves and actions) is crucial, as is the quantification of its impact on purchases. Using an innovative PDA tool to precisely record and time stamp consumers' moves and actions, we extend the classical Market Basket Analysis (MBA) by integrating this new information: associations between product categories are measured not only from purchases but also from consumer physical behavior. We compare results of our new method with classical MBA results and show a significant improvement.

Leadership propensity and sales performance among sales personnel and managers in a speciality retail store setting

We propose that specialty store managers, as well as outside sales personnel attached to the store, have selling responsibilities. In addition, we propose that sales personnel, as well as store managers, should have a propensity for leadership, which reflects an individual's enduring disposition to exhibit leadership within the context of his or her organizational roles. In two studies, we develop a new individual difference measure of propensity to lead and investigate its nomological validity within a specialty retail store environment. As predicted, leadership propensity was predictive of self-rated sales performance and a proclivity to identify prospects through cold calls to close sales, to reveal customer orientation, and to exhibit organizational citizenship behavior. We found that propensity to lead did not differ between salespeople and retail store managers, but we found that the respondent's role moderated the relationship between propensity to lead and supervisor performance ratings. Study limitations and managerial implications of this heretofore unidentified trait of salespeople are discussed.

Estimating the SCAN*PRO model of store sales: HB, FM or just OLS?

In this paper we investigate whether consideration of store-level heterogeneity in marketing mix effects improves the accuracy of the marketing mix elasticities, fit, and forecasting accuracy of the widely-applied SCAN*PRO model of store sales. Models with continuous and discrete representations of heterogeneity, estimated using hierarchical Bayes (HB) and finite mixture (FM) techniques, respectively, are empirically compared to the original model, which does not account for store-level heterogeneity in marketing mix effects, and is estimated using ordinary least squares (OLS). The empirical comparisons are conducted in two contexts: Dutch store-level scanner data for the shampoo product category, and an extensive simulation experiment. The simulation investigates how between- and within-segment variance in marketing mix effects, error variance, the number of weeks of data, and the number of stores impact the accuracy of marketing mix elasticities, model fit, and forecasting accuracy. Contrary to expectations, accommodating store-level heterogeneity does not improve the accuracy of marketing mix elasticities relative to the homogeneous SCAN*PRO model, suggesting that little may be lost by employing the original homogeneous SCAN*PRO model estimated using ordinary least squares. Improvements in fit and forecasting accuracy are also fairly modest. We pursue an explanation for this result since research in other contexts has shown clear advantages from assuming some type of heterogeneity in market response models. In an Afterthought section, we comment on the controversial nature of our result, distinguishing factors inherent to household-level data and associated models vs. general store-level data and associated models vs. the unique SCAN*PRO model specification.

A framework for evaluating semantic metadata

Because poor quality semantic metadata can destroy the effectiveness of semantic web technology by hampering applications from producing accurate results, it is important to have frameworks that support their evaluation. However, there is no such framework developedto date. In this context, we proposed i) an evaluation reference model, SemRef, which sketches some fundamental principles for evaluating semantic metadata, and ii) an evaluation framework, SemEval, which provides a set of instruments to support the detection of quality problems and the collection of quality metrics for these problems. A preliminary case study of SemEval shows encouraging results.

An infrastructure for acquiring high quality semantic metadata

Because metadata that underlies semantic web applications is gathered from distributed and heterogeneous data sources, it is important to ensure its quality (i.e., reduce duplicates, spelling errors, ambiguities). However, current infrastructures that acquire and integrate semantic data have only marginally addressed the issue of metadata quality. In this paper we present our metadata acquisition infrastructure, ASDI, which pays special attention to ensuring that high quality metadata is derived. Central to the architecture of ASDI is a verification engine that relies on several semantic web tools to check the quality of the derived data. We tested our prototype in the context of building a semantic web portal for our lab, KMi. An experimental evaluation comparing the automatically extracted data against manual annotations indicates that the verification engine enhances the quality of the extracted semantic metadata.

Evaluation of a heating system using a heat pump and heat store

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GEO Label – quality information interrogation tool for geospatial datasets:towards effective visualization of quality metadata

Although the importance of dataset fitness-for-use evaluation and intercomparison is widely recognised within the GIS community, no practical tools have yet been developed to support such interrogation. GeoViQua aims to develop a GEO label which will visually summarise and allow interrogation of key informational aspects of geospatial datasets upon which users rely when selecting datasets for use. The proposed GEO label will be integrated in the Global Earth Observation System of Systems (GEOSS) and will be used as a value and trust indicator for datasets accessible through the GEO Portal. As envisioned, the GEO label will act as a decision support mechanism for dataset selection and thereby hopefully improve user recognition of the quality of datasets. To date we have conducted 3 user studies to (1) identify the informational aspects of geospatial datasets upon which users rely when assessing dataset quality and trustworthiness, (2) elicit initial user views on a GEO label and its potential role and (3), evaluate prototype label visualisations. Our first study revealed that, when evaluating quality of data, users consider 8 facets: dataset producer information; producer comments on dataset quality; dataset compliance with international standards; community advice; dataset ratings; links to dataset citations; expert value judgements; and quantitative quality information. Our second study confirmed the relevance of these facets in terms of the community-perceived function that a GEO label should fulfil: users and producers of geospatial data supported the concept of a GEO label that provides a drill-down interrogation facility covering all 8 informational aspects. Consequently, we developed three prototype label visualisations and evaluated their comparative effectiveness and user preference via a third user study to arrive at a final graphical GEO label representation. When integrated in the GEOSS, an individual GEO label will be provided for each dataset in the GEOSS clearinghouse (or other data portals and clearinghouses) based on its available quality information. Producer and feedback metadata documents are being used to dynamically assess information availability and generate the GEO labels. The producer metadata document can either be a standard ISO compliant metadata record supplied with the dataset, or an extended version of a GeoViQua-derived metadata record, and is used to assess the availability of a producer profile, producer comments, compliance with standards, citations and quantitative quality information. GeoViQua is also currently developing a feedback server to collect and encode (as metadata records) user and producer feedback on datasets; these metadata records will be used to assess the availability of user comments, ratings, expert reviews and user-supplied citations for a dataset. The GEO label will provide drill-down functionality which will allow a user to navigate to a GEO label page offering detailed quality information for its associated dataset. At this stage, we are developing the GEO label service that will be used to provide GEO labels on demand based on supplied metadata records. In this presentation, we will provide a comprehensive overview of the GEO label development process, with specific emphasis on the GEO label implementation and integration into the GEOSS.