We propose a new model to describe consideration, consisting of a multivariate probit model component for consideration and a multinomial probit model component for choice, given consideration. The approach allows one to analyze stated consideration set data, revealed consideration set (choice) data or both, while at the same time it allows for unobserved dependence in consideration among brands. In addition, the model accommodates different effects of the marketing mix on consideration and choice, an error process that is correlated over time, and unobserved consumer heterogeneity in both processes.We attempt to establish the validity of existing practice to infer consideration sets from observed choices in panel data. To this end, we collect data in an on-line choice experiment involving interactive supermarket shelves and post-choice questionnaires to measure the choice protocol and stated consideration levels. We show with these experimental data that underlying consideration sets can be reliably retrieved from choice data alone.Next, we estimate the model on IRI panel data. We have two main results. First, compared with the single-stage multinomial probit model, promotion effects are larger when they are included in the consideration stage of the two-stage model. Second, we find that consideration of brands does not covary greatly across brands once we account for observed effects.

I n this paper, we propose and operationalize a new method for optimizing shelf arrangements. We show that there are important dependencies between the layout of the shelf and stock-keeping unit (SKU) sales and marketing effectiveness. The importance of these dependencies is further shown by the substantive profit gains we obtain with our proposed shelf optimization approach. The basis of our model is a standard sales equation that explains sales using item-specific marketing effect parameters and intercepts. In a Hierarchical Bayes (HB) fashion, we augment this model with a second layer that relates the effect parameters to shelf and SKU descriptors. We also take into account potential endogeneity of facings. After estimating the parameters of the two-level model using Bayesian methodology, we carefully investigate the dependencies of SKU sales and SKU marketing effectiveness on the shelf layout. Next, we search for the shelf arrangement that maximizes the expected total profit using simulated annealing (SA). We appear to be able to increase profits for all the stores analyzed, and our approach appears to outperform well-known rules of thumb.

To stimulate sales of sustainable products, such as organic and fair trade products, retailers need to know whether their in-store instruments effectively enhance market shares. This study uses sales data and a multilevel modeling approach to explain the market shares of sustainable products according to shelf layout factors, price level, price promotions, and consumer demographics. It argues that the effect of these variables differs between organic versus fair trade products, as buying motives might differ, organic buyers tend to be more loyal, and price is a more informative signal of quality for organic products. Results show that the number of facings has a positive relationship with the market share of fair trade brands, but not with the market share of organic brands. The same holds for the price difference with the leading brand, which is important for fair trade brands but not for organic brands. In contrast, an arrangement of the product category by brand is associated with higher market share for organic brands but not for fair trade brands. Additionally, placement at eye level and clustering of items benefits both types of sustainable brands, whereas they appear to be not very sensitive to price promotions. Finally, higher sales of sustainable products are found in areas where the customer base is older and has a higher education level.

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In this paper, we answer the question why statistics journals get lower impact factors than journals in other disciplines. We analyze diffusion patterns of papers in several journals in various academic fields. To obtain insights into the diffusion of the citation counts of the papers, the data are analysed with the Bass model, leading to values for the time-to-peak that can be used to compare the speeds of diffusion paper citations of the different disciplines. Estimation results show that for statistics journals, it takes significantly more years to reach their peak. To further investigate diffusion, we also compute the percentages of the total number of citations a paper has after 2 or 3 years.Again, it appears that statistics journals have slower citation diffusion than journals in other disciplines. We conclude with some suggestions to reduce this disparity.