Shining in the center:central gaze cascade effect on product choice

Consumers' tendency to choose the option in the center of an array and the process underlying this effect is explored. Findings from two eye-tracking studies suggest that brands in the horizontal center receive more visual attention. They are more likely to be chosen. Investigation of the attention process revealed an initial central fixation bias, a tendency to look first at the central option, and a central gaze cascade effect, progressively increasing attention focused on the central option right prior to decision. Only the central gaze cascade effect was related to choice. An offline study with tangible products demonstrated that the centrally located item within a product category is chosen more often, even when it is not placed in the center of the visual field. Despite widespread use, memory-based attention measures were not correlated with eye-tracking measures. They did not capture visual attention and were not related to choice. © 2012 by JOURNAL OF CONSUMER RESEARCH, Inc.

Decomposing the sales promotion bump accounting for cross-category effects

Extant research on the decomposition of unit sales bumps due to price promotions considers these effects only within a single product category. This article introduces a framework that accommodates specific cross-category effects. Empirical results based on daily data measured at the item/SKU level show that the effects of promotions on sales in other categories are modest. Between-category complementary effects (20%) are, on average, substantially larger than between-category substitution effects (11%). Hence, a promotion of an item has an average net spin-off effect of (20 - 11 =) 9% of its own effect. The number of significant cross-category effects is low, which means that we expect that, most of the time, it is sufficient to look at within-category effects only. We also find within-category complementary effects, which implies that competitive items within the category may benefit from a promotion. We find small stockpiling effects (6%), modest cross-item effects (22%), and substantial category-expansion effects (72%). The cross-item effects are the result of cross-item substitution effects within the category (26%) and within-category complementary effects (4%). Approximately 15% (= 11% / 72%) of the category-expansion effect is due to between-category substitution effects of dependent categories.

Ownership effects in consumers' brand extension evaluations

In this paper, we examine how brand ownership status affects consumers’ evaluation of brand extensions, using an experiment. In evaluating both brand extension and parent brands, brand owners differ from both nonowners and nonusers of a brand's product category in important ways. While the functional similarity between a brand and its extension impacts on all three groups’ brand extensions, its effects on nonowners and nonusers are more significant than those on brand owners. For brand owners, the most important consideration in their evaluation of brand extensions seems to be the image consistency between a brand and its extensions. Furthermore, there is an interaction effect between brand image consistency and product similarity for brand owners, whereas this effect is nonexistent for nonowners and nonusers.

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.

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.

Does online information drive offline revenues? Only for specific products and consumer segments!

While many offline retailers have developed informational websites that offer information on products and prices, the key question for such informational websites is whether they can increase revenues via web-to-store shopping. The current paper draws on the information search literature to specify and test hypotheses regarding the offline revenue impact of adding an informational website. Explicitly considering marketing efforts, a latent class model distinguishes consumer segments with different short-term revenue effects, while a Vector Autoregressive model on these segments reveals different long-term marketing response. We find that the offline revenue impact of the informational website critically depends on the product category and customer segment. The lower online search costs are especially beneficial for sensory products and for customers distant from the store. Moreover, offline revenues increase most for customers with high web visit frequency. We find that customers in some segments buy more and more expensive products, suggesting that online search and offline purchases are complements. In contrast, customers in a particular segment reduce their shopping trips, suggesting their online activities partially substitute for experiential shopping in the physical store. Hence, offline retailers should use specific online activities to target specific product categories and customer segments.

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.