We study the effects of the introduction of cross-channel functionalities on the overall sales dispersion of retailers and the implications of these effects for inventory management. To do that, we analyze data from a leading U.S. retailer who introduced a “ship-to-store” (STS) functionality that allows customers to ship products to their local store free of charge when those products are not available in their local store. Based on the fact that stores prioritize carrying products for which local demand is high, we test the hypothesis that introducing the STS functionality increased the retailer’s overall sales dispersion. We find that, on average, the contribution of the 90% lowest-selling products to total sales increased by 0.75 percentage points, increasing sales dispersion. Calibrating conventional inventory-ordering models, we show that to respond optimally to the observed increase in dispersion, the retailer would need to increase its cycle and safety inventories by approximately 2.7%. Our paper points out the effect of an increasingly important retail phenomenon (channel integration) on a key factor for inventory management (sales dispersion). This paper was accepted by Vishal Gaur, operations management.
Omni-chanel 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 omni-channel 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 omni-channel tactics.
Problem definition: How much, if at all, does training in product features increase a sales associate’s sales productivity? Academic/practical relevance: A knowledgeable retail sales associate (SA) can explain the features of available product variants and give a customer sufficient confidence in the customer’s choice or suggest alternatives so that the customer becomes willing to purchase. Although it is plausible that increasing an SA’s product knowledge will increase sales, training is not without cost and turnover is high in retail, so most retailers provide little product-knowledge training. Methodology: We partner with two firms and collect data on more than 50,000 SAs who had access to training. We assemble a detailed data set of the training history and individual sales productivity over a two-year period. We conduct econometric analysis to quantify the causal effect of training on sales. Results: For SAs who engaged in training, the sales rate increases by 1.8% for every online module taken, which is a much higher benefit than the direct or indirect costs associated with this training. Brand-specific training has a larger effect on the focal brand; however, there is a positive effect on other brands the SA sells. We also assess how the training benefit varies depending on the SA’s tenure, sales rate prior to training, and number of modules taken. Managerial implications: We present evidence of a novel training mechanism that can be extremely attractive to retailers. Online training tools, such as the one we study, have two characteristics that should not be overlooked. First, it is the brands, not the retailers, that create, develop, and pay for the training content. Second, the incentives are such that SAs invest their own time, rather than time on the job, to train, and this makes the retailer’s investment in the training a profitable proposition.
Problem definition: How should retail staffing levels be set? While cost of labor is well understood, the revenue implications of having the right staffing level are hard to estimate. Moreover, these implications vary by store; hence, staffing levels should vary as well. Academic/practical relevance: We provide a novel method for setting store associate staffing at the individual store level. We discuss a field implementation that tested this methodology. Methodology: We 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. Using historical analysis as a guide, we validate these results by changing the staffing levels in a few test stores. We implement the results chain-wide and measure the impact in a large specialty retailer. Results: We find that the implementation validates predictions of the historical analysis. The implementation in 168 stores over six months produces a 4.5% revenue increase and a nearly $7.4 million annual profit increase. The impact of staffing level on revenue varies greatly by store. Managerial implications: Our paper makes three contributions to academic literature and to retail practice. First, we describe a process by which retailers can improve the most common industry practice: set store labor to be proportional to forecasted store revenue. Our proposed approach systematically sets the labor level in each store. Second, we demonstrate the effectiveness of that process via a field test and then via chain-wide implementation over a six-month time period. Finally, most retailers set store labor at the same level across stores, proportionate to revenue. We show that this is not the best approach because the revenue impact of store labor varies by store. The stores in our study that could benefit from relatively more labor were those with high potential demand, closely located competition for that demand, and experienced store managers. Overall, we provide the first simple but rigorous, field-tested approach that any retailer can use to increase revenue and profitability through better labor management.
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.
Online retail has become more prominent around the world in the last decade. As a result, online retailers' website performance is increasingly important. Previous literature has extensively studied customer sensitivity to service speed and wait times in offline services. In “Need for Speed: The Impact of In-Process Delays on Customer Behavior in Online Retail,” Gallino, Karacaoglu, and Moreno extend this literature to online retail. They study the impact of delays in online retail on customer behavior. They estimate sizable negative effects of website slowdowns on online sales and conversion rates. Moreover, they explore how customer sensitivity to online delays varies throughout customers' shopping journeys. They find that the impact of waiting times varies along the different stages of the shopping journey, with customers becoming more sensitive to slowdowns at the checkout stage. Their findings have implications for website design decisions. This research is especially relevant in the current regulatory environment with ongoing policy debates about net neutrality.
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.
Millions of nanostores serve bottom-of-the-pyramid consumers in emerging markets. Their suppliers, consumer packaged goods (CPG) companies, struggle with high operational costs that largely stem from shopkeepers' liquidity constraints. We empirically investigate whether suppliers can improve operational performance by allowing nanostore shopkeepers to delay order payment by a short period of time. We term this delayed payment alternative "order-based trade credit" (OBTC) and examine the key trade-off that suppliers face when transacting with it. While OBTC can create efficiency gains when selling and delivering products to nanostores, it is risky, as shopkeepers might default on their credit lines. By leveraging data from a nanostore supplier offering OBTC, we assess the effect of this novel policy on the operational performance of the supplier through a difference-in-differences approach. We find that OBTC leads to substantial gains for nanostore suppliers across a range of important operational drivers. Therefore, the benefits of OBTC compensate the risk that suppliers take in financing shopkeepers' inventory under a wide range of scenarios.
Online retailers often offer free shipping threshold policies: customers who purchase more than a threshold amount are not charged an additional fee for shipping. This paper provides a data-driven analytical model to (i) assess the profitability of a retailer's current shipping threshold policy and (ii) identify the best freeshipping threshold policy for a retailer. The model is estimated from actual transaction and product return data. The model explicitly accounts for changes in customer shopping behavior due to a free shipping threshold, including strategically adding items to a shopping basket to receive free shipping, which we call orderpadding, and the subsequent adjustment in product return decisions. Roughly speaking, according to our model, a retailer that offers a free shipping threshold policy should set the threshold slightly abovethe average shopping basket amount. We calibrate our model to data from an online apparel retailer and determine that its decision to offer a lower free shipping threshold reduced its profitability considerably.This result is robust to a number of assumptions regarding the impact on long-run sales and possible price adjustments. We conclude that free shipping threshold policies are profitable only under a limited set of restrictive conditions.
