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Using in-store ambulatory eye-tracking, the authors investigate the extent to which lateral and vertical biases drive consumers' attention in a grocery store environment. The data set offers a complete picture of both where the shopper is located and the shopper's field of view and visual fixations during the trip. The authors address two research questions: First, do shoppers have a higher propensity to pay attention to products on their left or right side as they traverse an aisle (i.e., is the right side the \"right\" side)? Second, do shoppers tend to pay more attention to products at their eye level (i.e., is eye level \"buy level\")? The authors utilize the exogenous variations in the direction by which shoppers traverse an aisle to identify lateral bias. The exogenous variation of shoppers' eye-level positions is used to identify vertical bias. The authors find that shoppers pay more attention to products on their right side when traversing an aisle. Contrary to many practitioners' belief, eye level is not \"buy level\"; rather, the product level that has the greatest propensity to capture shoppers' attention is approximately 14.7 inches below eye level (which is around chest level).

We examine three sets of established behavioral hypotheses about consumers’ in‐store behavior using field data on grocery store shopping paths and purchases. Our results provide field evidence for the following empirical regularities. First, as consumers spend more time in the store, they become more purposeful—they are less likely to spend time on exploration and more likely to shop/buy. Second, consistent with “licensing” behavior, after purchasing virtue categories, consumers are more likely to shop at locations that carry vice categories. Third, the presence of other shoppers attracts consumers toward a store zone but reduces consumers’ tendency to shop there.

Retailers and manufacturers are keenly interested in understanding unplanned consideration and purchase conversion, but data that capture in-store product consideration have been unavailable in the past. In the current research, the authors use in-store video tracking to collect a novel data set that records shopping behavior at the point of purchase, including product consideration. In conjunction with an entrance survey of purchase intentions, they conduct several descriptive analyses that focus on the incidence, category propensity, behavioral characteristics, and outcome of unplanned consideration. The results reveal several new empirical insights. First, the authors find significant category-level complementarities between planned items and unplanned considerations, which they capture using a latent category map. Second, planned consideration and unplanned consideration differ in key behavioral characteristics (e.g., likelihood of purchase, time of occurrence, number of product touches). Third, greater likelihood of purchase conversion is significantly associated with dynamic factors (e.g., remaining in-store slack, outcome of the previous consideration) and behavioral characteristics (e.g., number of displays viewed, distance to shelf, references to a shopping list). The authors conclude with a discussion of implications of these findings for research and shopper marketing.

Many data sets, from different and seemingly unrelated marketing domains, all involve paths —records of consumers' movements in a spatial configuration. Path data contain valuable information for marketing researchers because they describe how consumers interact with their environment and make dynamic choices. As data collection technologies improve and researchers continue to ask deeper questions about consumers' motivations and behaviors, path data sets will become more common and will play a more central role in marketing research. To guide future research in this area, we review the previous literature, propose a formal definition of a path (in a marketing context), and derive a unifying framework that allows us to classify different kinds of paths. We identify and discuss two primary dimensions (characteristics of the spatial configuration and the agent) as well as six underlying subdimensions. Based on this framework, we cover a range of important operational issues that should be taken into account as researchers begin to build formal models of path-related phenomena. We close with a brief look into the future of path-based models, and a call for researchers to address some of these emerging issues.

In this paper, we examine aesthetic color combinations in a realistic product self-design task using the NIKEiD online configurator. We develop a similarity-based model of color relationships and empirically model the choice likelihoods of color pairs as a function of the distances between colors in the CIELAB color space. Our empirical analysis reveals three key findings. First, people de-emphasize lightness and focus on hue and saturation. Second, given this shift in emphasis, people generally like to combine colors that are relatively close or exactly match, with the exception that some people highlight one signature product component by using contrastive color. This result is more consistent with the visual coherence perspective than the optimal arousal perspective on aesthetic preference. Third, a small palette principle is supported such that the total number of colors used in the average design was smaller than would be expected under statistical independence.

Typically, shoppers' paths only cover less than half of the areas in a grocery store. Given that shoppers often use physical products in the store as external memory cues, encouraging shoppers to travel more of the store may increase unplanned spending. Estimating the direct effect of in-store travel distance on unplanned spending, however, is complicated by the difficulty of collecting in-store path data and the endogeneity of in-store travel distance. To address both issues, the authors collect a novel data set using in-store radio frequency identification tracking and develop an instrumental variable approach to account for endogeneity. Their analysis reveals that the elasticity of unplanned spending on travel distance is 57% higher than the uncorrected ordinary least squares estimate. Simulations based on the authors' estimates suggest that strategically promoting three product categories through mobile promotion could increase unplanned spending by 16.1%, compared with the estimated effect of a benchmark strategy based on relocating three destination categories (7.2%). Furthermore, the authors conduct a field experiment to assess the effectiveness of mobile promotions and find that a coupon that required shoppers to travel farther from their planned path resulted in a substantial increase in unplanned spending ($21.29) over a coupon for an unplanned category near their planned path ($13.83). The results suggest that targeted mobile promotions aimed at increasing in-store path length can increase unplanned spending.

Peer-led outreach is a critical element of HIV and STI-reduction interventions aimed at sex workers. We study the association between peer-led outreach to sex workers and the time to utilize health facilities for timely STI syndromic-detection and treatment. Using data on the timing of peer-outreach interventions and clinic visits, we utilize an Extended Cox model to assess whether peer educator outreach intensity is associated with accelerated clinic utilization among sex workers. Our data comes from 2705 female sex workers registered into Pragati, a women-in-sex-work outreach program, and followed from 2008 through 2012. We analyze this data using an Extended Cox model with the density of peer educator visits in a 30-day rolling window as the key predictor, while controlling for the sex workers' age, client volume, location of sex work, and education level. The principal outcome of interest is the timing of the first voluntary clinic utilization. More frequent peer visit is associated with earlier first clinic visit (HR: 1.83, 95% CI, 1.75-1.91, p < .001). In addition, 18% of all syndrome-based STI detected come from clinic visits in which the sex worker reports no symptoms, underscoring the importance of inducing clinic visits in the detection of STI. Additional models to test the robustness of these findings indicate consistent beneficial effect of peer educator outreach. Peer outreach density is associated with increased likelihood of-and shortened duration to-clinic utilization among female sex workers, suggesting potential staff resourcing implications. Given the observational nature of our study, however, these findings should be interpreted as an association rather than as a causal relationship.

Retailers are interested in understanding the amount of attention grocery shoppers pay to price information at the point of purchase, as price attention is an important determinant of price perception and purchase behavior. We utilize in-store ambulatory eye-tracking devices to directly measure the extent to which shoppers pay attention to price information as they shop for and consider grocery items for purchase. We find that shoppers visually fixate on price information in roughly 62 % of their considerations. Interestingly, the propensity of price attention changes dynamically during the course of a shopping trip, following an “inverted-U” pattern which peaks about two-thirds of the way through the trip. In addition, while the presence of a price promotion and a larger number of price tags encourage higher levels of price attention, higher purchase frequency is associated with lower levels of price attention. Our findings have important implications for retailers’ pricing strategies. [Display omitted]

We identify gaps and propose several directions for future research in preference measurement. We structure our argument around a framework that views preference measurement as comprising three interrelated components: (1) the problem that the study is ultimately intended to address; (2) the design of the preference measurement task and the data collection approach; (3) the specification and estimation of a preference model, and the conversion into action. Conjoint analysis is only one special case within this framework. We summarize cutting edge research and identify fruitful directions for future investigations pertaining to the framework's three components and to their integration.

Movie studios often have to choose among thousands of scripts to decide which ones to turn into movies. Despite the huge amount of money at stake, this process—known as green-lighting in the movie industry—is largely a guesswork based on experts’ experience and intuitions. In this paper, we propose a new approach to help studios evaluate scripts that will then lead to more profitable green-lighting decisions. Our approach combines screenwriting domain knowledge, natural-language processing techniques, and statistical learning methods to forecast a movie’s return on investment (ROI) based only on textual information available in movie scripts. We test our model in a holdout decision task to show that our model is able to significantly improve a studio’s gross ROI.