Offline Showrooms in Omni-channel Retail: Demand and Operational Benefits

David R. Bell; Santiago  Gallino; Antonio  Moreno

Article | Management Science

Offline Showrooms in Omni-channel Retail: Demand and Operational Benefits

by David R. Bell, Santiago Gallino and Antonio Moreno

Abstract

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, the 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.

Keywords: experience attributes; marketing–operations interface; omnichannel retailing; quasi-experimental methods; Retail operations; showrooms; Marketing Channels; Demand and Consumers; Performance Efficiency; Retail Industry;

Format: Print

Find at Harvard Purchase

Citation:

Bell, David R., Santiago Gallino, and Antonio Moreno. "Offline Showrooms in Omni-channel Retail: Demand and Operational Benefits." Management Science 64, no. 4 (April 2018): 1629–1651. (Winner of the 2014 POMS Applied Research Challenge. Workshop on Information Systems Economics Overall Best Paper Award 2014.)

The Indian banknote demonetization in 2016 was one of the most significant international events of that year. Overnight, 86% of Indian currency in circulation was declared invalid unless exchanged for new bills. The sudden and unexpected demonetization constituted a large shock to the entire Indian economy. One effect of the ensuing cash shortage was a large and sustained increase in the adoption and usage of digital payments. We use detailed sales data consisting of more than two and a half million transactions from a leading Indian online retailer to empirically investigate the effects of payment digitization on the online retail industry. We take advantage of the demonetization as a source of exogenous variation that induced a subset of consumers to switch to digital payments from more commonly used cash-on-delivery payments. We show that consumers who switch to digital payments maintain their purchase frequency but spend more and are less likely to return their purchases. Our findings show that firms in emerging markets may enjoy gains from consumer demand in addition to operational gains resulting from payment digitization.

Article | Production and Operations Management | October 2018

The Operational Value of Social Media Information

Ruomeng Cui, Santiago Gallino, Antonio Moreno and Dennis J. Zhang

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 mean absolute percentage error (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.

Keywords: social media; Machine learning; Information; Sales; Forecasting and Prediction;

Citation:

Cui, Ruomeng, Santiago Gallino, Antonio Moreno, and Dennis J. Zhang. "The Operational Value of Social Media Information." Special Issue on Big Data in Supply Chain Management. Production and Operations Management 27, no. 10 (October 2018): 1749–1774. View Details

Cite View Details Find at Harvard Purchase

Faculty & Research

Offline Showrooms in Omni-channel Retail: Demand and Operational Benefits

Abstract

Citation:

About the Author

More from the Author