Offline Showrooms in Omni-Channel Retail_ Demand and Operational Benefits

This article was downloaded by: 31 On: 24 March 2017, At: 04:57Publisher: Institute for Operations Research and the Management Sciences (INFORMS)INFORMS is located in Maryland, USAManagement SciencePublication details, including instructions for authors and subscription information:Offline Showrooms in Omnichannel Retail: Demand andOperational BenefitsDavid R. Bell, Santiago Gallino, Antonio MorenoTo cite this article:David R. Bell, Santiago Gallino, Antonio Moreno (2017) Offline Showrooms in Omnichannel Retail: Demand and OperationalBenefits. Management SciencePublished online in Articles in Advance 23 Mar 2017. /10.1287/mnsc.2016.2684Full terms and conditions of use: /page/terms-and-conditionsThis article may be used only for the purposes of research, teaching, and/or private study. Commercial useor systematic downloading (by robots or other automatic processes) is prohibited without explicit Publisherapproval, unless otherwise noted. For more information, contact .The Publisher does not warrant or guarantee the articles accuracy, completeness, merchantability, fitnessfor a particular purpose, or non-infringement. Descriptions of, or references to, products or publications, orinclusion of an advertisement in this article, neither constitutes nor implies a guarantee, endorsement, orsupport of claims made of that product, publication, or service.Copyright 2017, INFORMSPlease scroll down for articleit is on subsequent pagesINFORMS is the largest professional society in the world for professionals in the fields of operations research, managementscience, and analytics.For more information on INFORMS, its publications, membership, or meetings visit Dby31on24March2017,at04:57.Forpersonaluseonly,allrightsreserved./journal/mnsc/MANAGEMENT SCIENCEArticles in Advance, pp. 123ISSN 0025-1909 (print), ISSN 1526-5501 (online)Oine Showrooms in Omnichannel Retail: Demand andOperational BenetsDavid R. Bell,a Santiago Gallino,b Antonio Morenoca Marketing Department, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104; b Tuck School of Business,Dartmouth College, Hanover, New Hampshire 03755; c Kellogg School of Management, Northwestern University, Evanston, Illinois 60208Contact: (DRB); (SG); (AM)Received: May 11, 2015Accepted: October 2, 2016Published Online in Articles in Advance:March 23, 2017/10.1287/mnsc.2016.2684Copyright: 2017 INFORMSAbstract. Omnichannel environments where customers shop online and oine at thesame retailer are ubiquitous, and are deployed by online-rst and traditional retailersalike. We focus on the relatively understudied domain of online-rst retailers and theengagement of a key omnichannel tactic; specically, introduction of showrooms (physicallocations where customers can view and try products) in combination with online fulll-ment that uses centralized inventory management. We ask whether, and if so, how, show-rooms benet the two most basic retail objectives: demand generation and operationaleciency. Using quasi-experimental data on showroom openings by WarbyP,the leading and iconic online-rst eyewear retailer, we nd that showrooms: (1) increasedemand overall and in the online channel as well; (2) generate operational spillovers tothe other channels by attracting customers who, on average, have a higher cost-to-serve;(3) improve overall operational eciency by increasing conversion in a sampling chan-nel and by decreasing returns; and (4) amplify these demand and operational benets indealing with customers who have the most acute need for the rms products. Moreover,the eects we document strengthen with time as showrooms contribute not only to brandawareness but also to what we term channel awareness as well. We conclude by elaboratingthe underlying customer dynamics driving our ndings and by oering implications forhow online-rst retailers might deploy omnichannel tactics.History: Accepted by Vishal Gaur, operations management.Keywords: experience attributes marketingoperations interface omnichannel retailing quasi-experimental methods retail operations showrooms1. IntroductionAs online-rst retailers open stores and showrooms,and traditional retailers ramp up their Internet pres-ence, it is vital for practitioners and researchers aliketo understand how such “omnichannel” initiativesaect demand and operational eciency. Omnichan-nel convergence reects the reality that while onlineis the fastest-growing component of retail in theUnited States,1 oine retailing still anchors the sec-tor. Both observations also apply to international mar-kets; China, for example, is on target to become thelargest global e-commerce retail market, but oineretail there also remains strong and signicant (TheEconomist 2013). Therefore, retailers of all types andin all locations increasingly interact with consumersthrough multiple touch points (Brynjolfsson et al. 2013,p. 23); in the global consumer economy, omnichannelretailers and buying experiences are the norm.Fundamentally, retailers are attracted to omnichan-nel strategies because online and oine channels dif-fer in their ability to deliver product informationand execute product fulllment, the two core channelfunctions (see, e.g., Coughlan et al. 2006, pp. 910).While information can be provided online or via phys-ical access to the product, and fulllment can be“online” (i.e., the product is shipped to the customerfrom a centralized location) or “oine” (i.e., the cus-tomer goes to the product), alternative combinationshave very dierent pros and cons for the retailer, andfor the customer. Figure 1 illustrates the four combina-tions of fulllment and information delivery made pos-sible by the digital economy (adapted from Bell et al.2014). By executing a wide palette of channel options,the retailer can counterbalance the weaknesses inher-ent in each single alternative.Extant omnichannel research (e.g., Anderson et al.2010, Avery et al. 2012) tends to examine the interplaybetween the upper-left and lower-right channelsi.e.,traditional retail and e-commerce. Conversely, we focuson the new and understudied phenomenon of show-rooms for online-rst brands (upper-right channel) andwhether and how they aect demand and operationaleciency in the core online channel (lower-right chan-nel) after the rm opens them.2An omnichannel retailer (unlike a single-channelcounterpart), caters to consumer heterogeneity in1InformationdeliveredOnlineOfflineDby31on24March2017,at04:57.Forpersonaluseonly,allrightsreserved.Offline Online2Figure 1. The Information and Fulllment MatrixFulfillmentBell, Gallino, and Moreno: Oine Showrooms in Omnichannel RetailManagement Science, Articles in Advance, pp. 123, 2017 INFORMSwhereas the latter (e.g., the feel of a shirt or look ofa pair of glasses), when presented or characterizedonline, may introduce signicant uncertainty for someconsumers.Practitioners and analysts also understand thatonline-rst retailers face signicant challenges incommunicating nondigital product attribute informa-tion to customers. Prominent industry commentatorG, for example, refers to the home samplingprogram by the leading online-rst eyewear brandWarby Parker, as follows: “That (home try-on) hashelped Warby Parker overcome one of the biggest hur-dles (italics added) for online fashion brands, gettingpeople to feel comfortable about their online purchase”preferences for whether the information and fulll-ment functions should be carried out online, oine,or in mixed online oine congurations. Some cus-tomers, for example, prefer the ease of access andshopping that comes from a fully online experience,whereas others prefer to physically sample the prod-uct before buying. Similarly, any discrepancy between“sampled” and purchased items that aects productreturns might also vary according to channel. Our goal,therefore, is twofold: (1) to identify and document anydemand and operational benets from showrooms,and (2) to elaborate on, and provide evidence for, thecustomer sorting behavior that they induce.Our paper is the rst, to our knowledge, to demon-strate that online-rst retailers can realize demandand operational eciency benets from opening show-rooms, and to elucidate the mechanism. These nd-ings are very important to online-rst retailers and toomnichannel research, for two reasons. First, show-rooms deliver tactile information into a market withoutaecting fulllment options for the customers there.Moreover, they are signicantly less operationally com-plex than conventional stores as, unlike stores, theyrequire neither periodic inventory replenishments noritem-level sales forecasts, as inventory is stored andmanaged from a centralized distribution center. Sec-ond, in a digital economy, showrooms become placeswhere events and experiences that happen there canbe further amplied through social media channels,thus increasing the overall impact of the brand (see Bell2014, Chap. 6).Our study also connects to key historical researchinsights on how the digital economy operates, as thediscrepancy in the ability of online and oine chan-nels to deliver certain types of product informationeectively has long been recognized as a key issue ine-commerce. Over 15 years ago, Lal and Sarvary (1999)drew a distinction between digital and nondigital prod-uct attributes and how information about each is com-municated in online and oine channels. The former(e.g., the price of a product or length of a book) suf-fer no loss of information when communicated online,1Traditionalretail2Showrooms3Research online,pick-up in-store4Online retail andpure-players(Kim 2012). A myriad of other retailers from B and C to W and Zrecognize that uncertainty about nondigital productattributes is a barrier to purchase for large segmentsof customers and therefore employ free two-way ship-ping, pop-up stores, extensive customer reviews, andrelated methods to combat it.We study showroom introductions using a propen-sity scoring approach on quasi-experimental obtaineddata from Warby Parker (Figure 2 is a screenshot ofthe website). Since its inception in February 2010 asan online-rst brand (WarbyP), the rm hasprogressively introduced showrooms in dierent loca-tions throughout the United States. This institutionalsetting is ideal for studying showrooms, for three rea-sons. First, eyewear has signicant nondigital or “tand feel” attributes such that many customers ndit a dicult category to buy online.3 Second, WarbyParker has always oered a nationwide product sam-pling program called “Home Try On” (HTO). UnderHTO, ve pairs of glasses (frames only) are deliveredto customers for inspection free of charge for ve days.This “intermediate” position between online (whereall product information is only available digitally) andshowrooms (where all product information is avail-able in person), helps us elucidate the customer sort-ing mechanism. Third, in all three channels (online,HTO, and showroom), fulllmentconditional upona purchaseis identical and from a centralized loca-tion. (As we elaborate in Section 2, in prior researchfulllment is not constant across channel options.)We contribute four new substantive ndings.First, showrooms increase sales within the tradingarea both overall and through the online channel. Esti-mated average eects of 7.4% and 2.9%, respectively,are statistically and economically signicant.4 Thisimplies that the showroom not only delivers sales inits own right but confers awareness and brand benetsthat drive incremental sales in the existing online channeland is therefore accretive from a demand perspective.We further show that the showroom is, on average, themost eective customer-acquisition channel and thatDby31on24March2017,at04:57.Forpersonaluseonly,allrightsreserved.Bell, Gallino, and Moreno: Oine Showrooms in Omnichannel RetailManagement Science, Articles in Advance, pp. 123, 2017 INFORMSFigure 2. Screenshot of the Warby Parker Website3Source. Used with permission from Warby Parker (WarbyP).the brand and awareness benets are not due to otherfactors, including concomitant advertising.Second, showrooms improve operational eciencyby increasing conversion in the sampling channel,discouraging “excessive” sampling, and decreasingreturns, within the trading area of the showroom.Regarding conversion, showrooms cause samplingchannel sales to fall 4.5% and sampling channel ordersto fall by 10%. Since orders fall more than sales do,there is an implied (and highly signicant) conversionincrease. This aggregate-level nding is mirrored inindividual-level data as well. Since each HTO costs therm about $15, repeated HTOs by an individual cus-tomer may be detrimental. Within the trading area ofshowrooms, the probability of repeated HTOs declines1.6% and the probability of individual-level conversionof try-ons to sales improves by 1%. Returns in the trad-ing area of a showroom decrease by 1%.Third, we elaborate the underlying customer behav-ior mechanism. Since the showroom attracts customerswith the highest haptic need, customers choosing toremain in other channels even though they had theoption of visiting a showroom are better aligned to them,which benets the rm. Because showrooms attractt-sensitive customers with the highest cost to serve,other channels are left with a more favorable consumermix and the rm enjoys signicant demand and opera-tional benets (in aggregate, and in the other channels)Dby31on24March2017,at04:57.Forpersonaluseonly,allrightsreserved.4as a result. We further illustrate this by computingimplicit breakeven points for showrooms versus theother two channels.Fourth, the benets to the rm are amplied whenserving customers who have the most acute need forthe product. The diopter measure of the correctionallens is an objective proxy for extent of product use,as customers with higher measures use their eyewearmore intensively. We show that the ability of the show-room to reduce overall return rates is amplied forthese types of products.5 Our temporal estimates implythat all of the demand and operational eciency bene-ts intensify with time, implying that showrooms con-tribute not only to brand awareness but also to whatwe term channel awareness, and that they deliver a last-ing impact on customers ability to align to the channelbest suited to them.The remainder of the paper is organized as fol-lows. Section 2 summarizes relevant prior researchand develops the positioning and unique contributionof our research. Section 3 describes the research set-ting, data, and our econometric approach and quasi-experimental design. Section 4 reports the overalldemand and eciency eects, and Section 5 elaborateson the underlying customer sorting mechanism.6 Sec-tion 6 discusses implications for practice and decisionmaking, and Section 7 concludes the paper.2. Background and Motivation2.1. E-Commerce and Omnichannel RetailingThe consumer Internet has evolved considerably sinceJe Bezos rst sold books online in 1994 and from the“Internet Retailing 1.0” boom and bust in the early2000s. Indeed, the evolution of the retail sector canbe viewed, in simple terms, with reference again toFigure 1. Pre-Internet, all retailing was characterized bythe upper-left quadrant; from the early 1990s, A and other “pure players” appeared to repre-sent the lower-right quadrant. Facing competition fromthese new players, traditional retailers (e.g., Crate andBarrel, Home Depot, Walmart, etc.) entered the lower-right quadrant via their own “.com” properties.Interestingly, the evolutionary path to quadrants 2(upper right) and 3 (lower left) diers by retailer originand type. Online-rst retailers with no legacy of oinepresence have been embracing the upper-right quad-rant via showrooms; traditional retailers, on the otherhand, have begun to think creatively about how best toleverage their physical real estate, and they naturallygravitate toward quadrant 3. A good example here isBOPS (or, Buy Online, Pick up in Store), a tactic favoredby many established retailers.When looking across rms, it is natural to thinkof players who existed only in quadrant 1 or 4 asin competition with each other. Unsurprisingly, earlyBell, Gallino, and Moreno: Oine Showrooms in Omnichannel RetailManagement Science, Articles in Advance, pp. 123, 2017 INFORMSacademic articles focused on understanding why con-sumers and rms might prefer online to oine, orvice versa. Some initial studies (e.g., Bakos 1997,Brynjolfsson and Smith 2000, Iyer and Pazgal 2003)explained how and why online retailers can reducesearch frictions for consumers and deliver lowerprices. Other articles (e.g., Balasubramanian et al.2003) showed why online sellers could be more conve-nient or oer more product variety (e.g., Brynjolfssonet al. 2009, Ghose et al. 2006).7 This literature expandedto consider location-based explanations for whetherconsumers prefer online or oine channels (e.g.,Forman et al. 2009), and structural aspects of local mar-kets including the physical distances customers musttravel to oine stores (e.g., Forman et al. 2009) and theextent to which target customers have minority prefer-ences and are underserved by oine sellers (Choi andBell 2011).While demand eects are natural candidates forstudy in omnichannel research, operations manage-ment researchers also addressed inventory manage-ment implications for rms, which are relevant toour setting. Netessin