Marshall L. Fisher

Marshall L. Fisher, Santiago Gallino, Jun Li, A Step-by-Step Guide to Real-Time Pricing in Harvard Business Review, November-December 2023.

Abstract: In today’s fast-paced world of digital retailing, the ability to revise prices swiftly and on a large scale has emerged as a decisive differentiator for companies. Many retailers now track competitors’ prices via systems that scrape rivals’ websites and use this information as an input to set their own prices manually or automatically. A common strategy is to charge X dollars or X percent less than a target competitor. However, retailers that use such simple heuristics miss significant opportunities to fine-tune pricing. Some companies are now applying machine-learning models to guide their pricing decisions, but even these retailers tend to take an overly limited approach. They try to match or undercut competitors’ prices without taking into account factors such as whether rivals are out of stock or how consumers make their purchasing decisions. In this article, we present a step-by-step process for dynamic pricing that focuses on building computer models that consider not just competitor pricing but also product availability and customer behavior to recommend optimal prices in real-time.

Marshall L. Fisher and Santiago Gallino, The Future of Retail Grocery Store Labor.

Abstract: We report here the results of a study to learn about the evolving store labor practices of leading (mostly grocery) retailers, including the current state of practice, what problems the retailers are struggling with currently, and implications for the industry. We first report on a series of interviews with senior executives at 15 grocery retailers and then present a tabulation of results of a written survey completed by 60 retail executives in 10 countries. An Appendix provides a survey of relevant published literature, the questionnaire sent to interviewees to guide our discussions with them, the written survey questions and a tabulation of survey responses segmented by U.S. retailers, non U.S. retailers and academics.   There are three main insights from our study: 1) labor market conditions have been challenging in recent months, and this is not likely to change in the near future; 2) retailers need to rethink their approach to online grocery; and 3) thinking about labor as an asset to leverage rather than a cost to bear can be a way forward.

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.

Marshall L. Fisher, Santiago Gallino, Serguei Netessine, Retailers Are Squandering Their Most Potent Weapons in Harvard Business Review, January - February 2019.

Abstract: Instead of viewing salespeople as an expendable cost, store managers should treat them as an asset in their battle with e-tailers.

Marshall L. Fisher, Santiago Gallino, Serguei Netessine (2018), Does Online Training Work in Retail?, M&SOM.

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.

Marshall L. Fisher, Santiago Gallino, Serguei Netessine (2018), Setting Retail Staffing Levels: A Methodology Validated with Implementation, .

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, Antonio (Toni) Moreno-Garcia, Marshall L. Fisher (Working), Does Inventory Influence Demand? Exploring Billboard and Scarcity Effects.

Marshall L. Fisher and Ramnath Vaidyanathan (Working), A Demand Estimation Procedure for Retail Assortment Optimization with Results from Implementations.