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.

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.

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-lightingin 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. 1 We thank the AE and the three anonymous reviewers for their valuable and insightful comments on our manuscript. We are, of course, responsible for the contents of this paper.2 From Storyline to Box Office: A New Approach for Green-Lighting Movie Scripts AbstractMovie 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 which 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 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 improve a studio's gross return-on-investment significantly.

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.

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