Using a proprietary data set, we analyze the impact of the implementation of a "buy-online, pick-up-in-store" (BOPS) project. The implementation of this project is associated with a reduction in online sales and an increase in store sales and traffic. These results can be explained by two simultaneous phenomena: (1) additional store sales from customers who use the BOPS functionality and buy additional products in the stores (cross-selling effect) and (2) the shift of some customers from the online to the brick-and-mortar channel and the conversion of noncustomers into store customers (channel-shift effect). We explain these channel-shift patterns as an increase in "research online, purchase offline" behavior enabled by BOPS implementation, and we validate this explanation with evidence from the change of cart abandonment and conversion rates of the brick-and-mortar and online channels. We interpret these results in light of recent operations management literature that analyzes the impact of sharing inventory availability information. Our analysis illustrates the limitations of drawing conclusions about complex interventions using single-channel data. Using a proprietary dataset, we analyze the impact of the implementation of a "buy-online, pickup-in-store"(BOPS) project. The implementation of this project is associated with a reduction in online sales and an increase in store sales and traffic. These results can be explained by two simultaneous phenomena: (1) additional store sales from customers who use the BOPS functionality and buy additional products in the stores (cross-selling effect) and (2) the shift of some customers from the online to the brick-and-mortar channel and the conversion of noncustomers into store customers (channel-shift effect). We explain these channel shift patterns as an increase in "research online, purchase offline" (ROPO) behavior enabled by BOPS implementation, and we validate this explanation with evidence from the change of cart abandonment and conversion rates of the brick-and-mortar and online channels. We interpret these results in light of recent operations management literature that analyzes the impact of sharing inventory availability information. Our analysis illustrates the limitations of drawing conclusions about complex interventions using single-channel data.

Omnichannel environments where customers shop online and offline at the same retailer are ubiquitous, and are deployed by online-first and traditional retailers alike. We focus on the relatively understudied domain of online-first retailers and the engagement of a key omnichannel tactic; specifically, introduction of showrooms (physical locations where customers can view and try products) in combination with online fulfillment that uses centralized inventory management. We ask whether, and if so, how, showrooms benefit the two most basic retail objectives: demand generation and operational efficiency. Using quasi-experimental data on showroom openings by WarbyParker.com , the leading and iconic online-first eyewear retailer, we find that showrooms: (1) increase demand overall and in the online channel as well; (2) generate operational spillovers to the other channels by attracting customers who, on average, have a higher cost-to-serve; (3) improve overall operational efficiency by increasing conversion in a sampling channel and by decreasing returns; and (4) amplify these demand and operational benefits in dealing with customers who have the most acute need for the firm’s products. Moreover, the effects we document strengthen with time as showrooms contribute not only to brand awareness but also to what we term channel awareness as well. We conclude by elaborating the underlying customer dynamics driving our findings and by offering implications for how online-first retailers might deploy omnichannel tactics. This paper was accepted by Vishal Gaur, operations management.

While the value of using social media information has been established in multiple business contexts, the field of operations and supply chain management have not yet explored the possibilities it offers in improving firms' operational decisions. This study attempts to do that by empirically studying whether using publicly available social media information can improve the accuracy of daily sales forecasts.We collaborated with an online apparel retailer to assemble a dataset that combines (1) detailed internal operational information, including data on sales, advertising, and promotions, as well as (2) publicly available social media information obtained from Facebook. We implement a variety of machine learning methods to forecast daily sales. We find that using social media information results in statistically significant improvements in the out‐of‐sample accuracy of the forecasts, with relative improvements ranging from 12.85% to 23.23% over different forecast horizons. We also demonstrate that nonlinear boosting models with feature selection, such as random forests, perform significantly better than traditional linear models. The best‐performing method (random forest) yields an out‐of‐sample MAPE of 7.21% when not using social media information and 5.73% when using social media information is used. In both cases, this significantly improves the accuracy of the company's internal forecasts (a MAPE of 11.97%). Combining these empirical results, we provide recommendations for forecasting sales in general as well as with social media information.

No abstract

The authors study how faster delivery in the online channel affects sales within and across channels in omnichannel retailing. The authors leverage a quasi-experiment involving the opening of a new distribution center by a U.S. apparel retailer, which resulted in unannounced faster deliveries to western U.S. states through its online channel. Using a difference-in-differences approach, the authors show that online store sales increased, on average, by 1.45% per business-day reduction in delivery time, from a baseline of seven business days. The authors also find a positive spillover effect to the retailer’s offline stores. These effects increase gradually in the short-to-medium run as the result of higher order count. The authors identify two main drivers of the observed effect: (1) customer learning through service interactions with the retailer and (2) existing brand presence in terms of online store penetration rate and offline store presence. Customers with less online store experience are more responsive to faster deliveries in the short run, whereas experienced online store customers are more responsive in the long run.