Research Interests: retailing, supply chain management
Marshall L. Fisher, Santiago Gallino, Jiaqi (Joseph) Xu (2019), The Value of Rapid Delivery in Omnichannel Retailing, Journal of Marketing Research.
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 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 term as the result of higher order count. The authors identify two main drivers of the observed effect: customer learning through service interactions with the retailer, and 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 term, while experienced online store customers are more responsive in the long term.
Abstract: A knowledgeable retail sales associate (SA) can explain the features of available product variants and give the customer sufficient confidence in her choice or suggest alternatives so that she becomes willing to purchase. Although it is plausible that increasing an SA’s product knowledge will increase sales, training is not costless and turnover is high in retail, so most retailers provide little or no training. Thus, an important question is how much, if at all, does training increase an SA’s sales productivity? To answer this question, we partnered with Experticity, a firm that provides online, self-guided training modules for retail SAs, and Dillard’s, a leading department store chain whose more than 50,000 SAs had access to the Experticity modules. We assembled a data set of the training history and sales productivity of Dillard’s associates over a two-year period. We found that as SAs engaged in training over time, their sales rate increased by 1.8 percent for every online module taken, which is a much higher benefit than the direct or indirect costs associated with this training. We also found that willingness to engage in voluntary training was an indicator of raw talent; those SAs who engaged in training were 20 percent more productive prior to any training and 46 percent more productive after training than those who took no training. Surprisingly, brand-specific training did not significantly affect the sales of the focal brand but did improve overall sales of all brands. Our evidence of successful online learning may be of general interest given that, to date, analysis of massive open online courses has shown poor engagement by participants and questionable outcomes.
Abstract: We describe a three-step process that a retailer can use to set retail store sales staff levels. First, use historical data on revenue and planned and actual staffing levels by store to estimate how revenue varies with the staffing level at each store. We disentangle the endogeneity between revenue and staffing levels by focusing on randomly occurring deviations between planned and actual labor. Second, using historical analysis as a guide, we validate these results by changing the staffing levels in a few test stores. Finally, we implement the results chain-wide and measure the impact. We describe the successful deployment of this process with a large specialty retailer. We find that 1) the implementation validates predictions of the historical analysis, including the use of the variation between planned staffing and actual staffing as an exogenous shock, 2) implementation in 168 stores over a 6-month period produces a 4.5% revenue increase and a nearly $7.4 million annual profit increase, after accounting for the cost of the additional labor, and 3) the impact of staffing level on revenue varies greatly by store, and therefore staffing levels should also vary, with more sales staff relative to revenue assigned to those stores where sales staff have the greatest impact on revenue. Specifically, we found the largest impact of store labor in stores with the largest average basket sizes, located in regions with good growth potential, facing certain competitors (e.g., Wal-Mart), and run by long-serving managers.
Marshall L. Fisher, Santiago Gallino, Jun Li (2017), Competition-Based Dynamic Pricing in Online Retailing: A Methodology Validated with Field Experiments, Management Science.
Abstract: A retailer following a competition-based dynamic pricing strategy tracks competitors' price changes and then must answer the following questions: (1) Should we respond? (2) If so, respond to whom? (3) How much of a response? (4) And on which products? The answers require unbiased measures of price elasticity as well as accurate estimates of competitor significance and the extent to which consumers compare prices across retailers. There are two key challenges to quantify these factors empirically: first, the endogeneity associated with almost any type of observational data, where prices are correlated with demand shocks observable to pricing managers but not to researchers, and second, the absence of competitor sales information, which prevents efficient estimation of a full consumer-choice model. We address the first issue by conducting a field experiment with randomized prices. We resolve the second issue by exploiting the retailer's own and competitors' stockouts as a source of variation to the consumer choice set, in addition to variations in competitors' prices. We estimate an empirical model capturing consumer choices among substitutable products from multiple retailers. Based on the estimates, we propose and test a best-response pricing strategy through a carefully controlled live experiment that lasts five weeks. The experiment documents an 11 percent revenue increase while maintaining a margin above a retailer-specified target.
Andrés Catalán and Marshall L. Fisher (Draft), Assortment Allocation to Distribution Centers to Minimize Split Customer Orders.
Santiago Gallino, Richard Kum-yew Lai, Marshall L. Fisher (Working), Does Inventory Have a Scarcity Effect? New Evidence Using Extreme Weather For Exogenous Variation.
Marshall L. Fisher (2009), Rocket Science Retailing: The 2006 Philip McCord Morse Lecture, Operations Research, Vol. 57, No. 3, May-June 2009, pp. 527-540.
Abstract: Retailing is a huge industry. In the United States, retail business represents about 40% of the economy and is the largest employer. Retail supply chain management is still more art than science, but this is changing rapidly as retailers begin to apply analytic models to the huge volume of data they are collecting on consumer purchases and preferences. This industry-wide movement resembles the transformation of Wall Street that occurred in the 1970s when physicists and other "rocket scientists" applied their analytic skills to investment decisions. The Consortium for Operational Excellence in Retailing (COER) (codirected by Ananth Raman, Harvard Business School, and myself) is a group of academics working with about 50 leading retailers to assess their progress towards rocket science retailing and to accelerate that progress through selected research projects. After some brief comments on the current state of industry practice in retail supply chain management, this paper will describe examples of COER research in four areas: assortment planning, pricing, inventory optimization, and store execution.
Several forces, ranging from technology that has dramatically reduced the cost of communication, to political developments such as the opening up of China, Vietnam, and Eastern Europe, have created an avalanche of outsourcing and offshoring and lead to supply chains that stretch halfway around the world. This course will study the many questions that arise in the management of such global supply chains, including: Which design and production activities to do in-house and which to outsource? Where to locate various activities around the world? How to forecast the many factors that influence these decisions, including inflation in cost factors such as labor and freight, and the likelihood of future government regulation or political instability? How to keep the supply chain flexible so as to adapt to change? How to manage a geographically disbursed supply chain, including what relationships to have with vendors to ensure low cost, high quality, flexibility, safety, humane labor practices and respect for sustainability of the environment? The course is highly interactive, using case discussions in most classes and senior supply chain executives in many sessions. Grades are based one-third each on class participation, indivudla write-ups of the discussion questions for 3 of the class sessions, and a course paper.
This course is highly recommended for students with an interest in pursuing careers in: (1) retailing and retail supply chains; (2) businesses like banking, consulting, information technology, that provides services to retail firms; (3) manufacturing companies (e.g. P&G) that sell their products through retail firms. Retailing is a huge industry that has consistently been an incubator for new business concepts. This course will examine how retailers understand their customers' preferences and respond with appropriate products through effective supply chain management. Supply chain management is vitally important for retailers and has been noted as the source of success for many retailers such as Wal-mart and Home Depot, and as an inhibitor of success for e-tailers as they struggle with delivery reliability. See M. L. Fisher, A. Raman and A. McClelland, "Rocket Science Retailing is Coming - Are You Ready?," Harvard Business Review, July/August 2000 for related research.
New Wharton research shows why it pays for retailers to staff stores with the right number of well-trained employees.Knowledge @ Wharton - 2019/02/14