Offline Showrooms and Customer Migration in Omni-Channel Retail

Transcription

1 Offlne Showrooms and Customer Mgraton n Omn-Channel Retal Davd R. Bell Marketng Department, The Wharton School, Unversty of Pennsylvana, Phladelpha, PA 19104, davdb@wharton.upenn.edu Santago Gallno Tuck School of Busness, Dartmouth College, Hanover, NH 03755, santago.gallno@tuck.dartmouth.edu Antono Moreno Manageral Economcs and Decson Scences, Kellogg School of Management, Northwestern Unversty, Evanston, IL 60208, a-morenogarca@kellogg.northwestern.edu Omn-channel envronments where customers shop onlne and offlne at the same retaler are ubqutous and are deployed by tradtonal retalers and onlne-frst retalers alke. As such, they rase mportant new questons regardng ther mpact on demand generaton and operatonal effcency. We focus on the relatvely understuded doman of onlne-frst retalers and one key way that they establsh an offlne presence; specfcally, by ntroducng showrooms (physcal locatons where customers can see and try the products) n combnaton wth onlne fulfllment usng centralzed nventory management. We examne how a channel structure comprsng offlne delvery of nformaton to customers coupled wth onlne logstcs and fulfllment of orders mpacts the exstng core onlne channel and a samplng channel. Usng quas-expermental data on showroom openngs by WarbyParker.com, the leadng and conc onlne-frst eyewear retaler, we fnd that showrooms: (1) ncrease demand overall and n the onlne channel as well, (2) mprove overall operatonal effcency by ncreasng converson n the samplng channel and by decreasng returns, (3) generate operatonal spllovers to the other channels by attractng customers who, on average, have a hgher cost-to-serve, and (4) amplfy benefts to the frm n dealng wth those customers who have the most acute need for the product. Moreover, these effects strengthen wth tme as showrooms contrbute not only to brand awareness but also to what we term channel awareness as well. Our fndngs are robust to numerous alternatve model specfcatons and sample selecton procedures; we conclude by elaboratng the underlyng customer dynamcs that drve our fndngs and by offerng mplcatons for omn-channel growth by onlne-frst retalers. Key words : Omn-Channel Retalng; Showrooms; Experence Attrbutes; Propensty Scorng; Quas-Expermental Methods; Retal Operatons Hstory : March 03,

2 2 Bell, Gallno and Moreno: Offlne Showrooms and Customer Mgraton n Omn-Channel Retal 1. Introducton As tradtonal retalers ramp up ther Internet presence and onlne-frst retalers open stores and showrooms, t s vtal to understand how omn-channel strateges affect consumer demand and operatonal effcency. Omn-channel convergence reflects the fact that whle onlne s the fastest growng component of retal n the Unted States (accordng to Forrester Research the market wll grow from $231b n 2013 to $370b n 2017 on CAGR of 10 percent 1 ), offlne retalng stll anchors the sector. Both observatons also apply to nternatonal markets; Chna, for example, s on target to become the largest global e-commerce retal market, but offlne retal there also remans strong and sgnfcant. 2 Therefore, retalers of all types and n all locatons ncreasngly nteract wth consumers through multple touch ponts (Brynjolfsson et al. 2013, p. 23); n the global consumer economy omn-channel retalers and buyng experences are becomng the norm. Fundamentally, retalers are attracted to omn-channel strateges because onlne and offlne channels dffer n ther ablty to delver product nformaton and execute product fulfllment, the two core channel functons (see, for example, Coughlan et al. 2006, p. 9-10). Informaton can be provded onlne or va physcal access to the product, whle fulfllment can be onlne (.e., the product s shpped to the customer from a centralzed locaton) or offlne (.e., the customer goes to the product). Fgure 1 llustrates the 4 combnatons of fulfllment and nformaton delvery made possble the dgtal economy (Bell et al. 2014). In a key pont of departure from exstng omn-channel research (e.g., Anderson et al. 2010; Avery et al. 2012) whch examnes the nterplay between the upper left and lower rght channels, we focus on how the showroom (upper rght channel) affects demand and operatonal effcency for the core onlne channel (bottom rght channel). An omn-channel retaler (unlke a sngle-channel counterpart), caters to consumer heterogenety n preferences for whether the nformaton and fulfllment functons should be carred out onlne, offlne, or n mxed onlne-offlne confguratons. Some customers, for example, prefer the ease of access and shoppng that comes from a fully onlne experence, whereas others prefer to physcally sample the product before buyng. An mportant mxed confguraton for onlne-frst brands s the showroom,.e., a zero-nventory store that combnes physcal access to product nformaton wth onlne fulfllment usng centralzed nventory management. When the showroom opens, t generates a shock n the product nformaton avalable to customers n the showroom s tradng area. In ths paper, we focus on how and why a showroom affects demand from these customers and operatonal effcency n servng them. Our goal s twofold: (1) to document the market mpact of

3 Bell, Gallno and Moreno: Offlne Showrooms and Customer Mgraton n Omn-Channel Retal 3 Fulfllment Offlne Onlne Informaton Delvered Offlne Onlne 1 3 Tradtonal Retal Research Onlne, Pck-Up In-Store 2 4 Showrooms Pure-Players Fgure 1 The Informaton and Fulfllment Matrx ths nnovaton, and (2) to elaborate on, and provde evdence for, the customer channel mgraton mechansm t nduces. Specfcally, we demonstrate that the market mpacts are consstent wth channel mgraton (from onlne and samplng, to offlne) by customers who have the greatest need for a tactle experence. Our paper s the frst, to our knowledge, to demonstrate that retalers can realze demand and operatonal effcency benefts from stores that carry no nventory,.e., showrooms. Showrooms delver tactle nformaton nto a market wthout affectng fulfllment optons for customers. It s mportant to understand the mpact of showrooms because they are sgnfcantly less operatonally complex than conventonal stores. Unlke stores, showrooms do not need to receve perodc nventory replenshments, nor do they requre sales forecasts for each product at each ndvdual locaton gven that nventory s stored and managed from a centralzed dstrbuton center. Furthermore, we fnd that the shock n avalable product nformaton arsng from the ntroducton of showrooms delvers operatonal effcency to retalers by, for example, lowerng returns overall, and by further reducng returns from customers wth the most complex product needs. The dscrepancy n the ablty of onlne and offlne channels to delver certan types of product nformaton has long been recognzed as a key ssue n E-commerce research and practce alke. Over 15 years ago, Lal and Sarvary (1999) drew a dstncton between dgtal and non-dgtal product attrbutes and how nformaton about each s communcated n onlne and offlne channels. The former, e.g., the prce of a product or length of a book, suffer no loss of nformaton when communcated onlne, whereas the latter, e.g., the feel of a shrt or look of a par of glasses, when presented or characterzed onlne, may ntroduce sgnfcant uncertanty for some consumers.

4 4 Bell, Gallno and Moreno: Offlne Showrooms and Customer Mgraton n Omn-Channel Retal Practtoners and analysts also understand that onlne-frst retalers face sgnfcant challenges n communcatng non-dgtal product attrbute nformaton to customers. Leadng ndustry commentator GgaOm.com, for example, refers to the home samplng program by the fashon eyewear brand WarbyParker.com as follows: That (home try-on) has helped Warby Parker overcome one of the bggest hurdles (talcs added) for onlne fashon brands, gettng people to feel comfortable about ther onlne purchase. 3 A myrad of other retalers from Bonobos.com and Casper.com to Wayfar.com and Zappos.com recognze that uncertanty about non-dgtal product attrbutes s a barrer to purchase for large segments of customers, and therefore employ free two-way shppng, pop-up stores, extensve customer revews, and related methods to combat t. A related lterature n E-commerce examnes relatve search frctons n onlne and offlne markets (e.g., Brynjolfsson and Smth 2000), locaton-based explanatons for whether consumers prefer onlne or offlne channels (e.g., Forman et al. 2009), and the mplcatons of tradtonal offlne store ntroductons for onlne demand (e.g., Anderson et al. 2010; Avery et al. 2012). We contrbute to ths lterature by demonstratng, for an onlne-frst retaler, why showrooms whch nject nformaton nto a market wthout affectng fulfllment mpact sales, converson, and returns n the exstng channels. Furthermore, we explan why customer channel mgraton s the underlyng mechansm, and for whch types of customers and buyng contexts, e.g., smple versus complex products, that the effects are felt most strongly. We ascertan the effects of showroom ntroductons usng a propensty scorng approach on quasexpermental data from WarbyParker.com, the leadng onlne-frst fashon eyewear brand (Fgure 2 s a screenshot of the webste). Snce ncepton n February 2010, Warby Parker has progressvely ntroduced showrooms n dfferent locatons throughout the Unted States. We delberately selected ths nsttutonal settng due to three mportant features that allow us to properly solate what happens when addtonal nformaton alone s njected nto a local market, va the ntroducton of a new showroom. Frst, eyewear has sgnfcant non-dgtal or ft and feel attrbutes such that many customers fnd t a dffcult category to buy onlne. 4 Second, Warby Parker began as an onlne-only retaler that has always offered consumers a natonwde product samplng program called Home Try On (HTO). Under HTO fve pars of glasses (frames only) are delvered to customers for nspecton free of charge for fve days. HTO s therefore an ntermedate poston between onlne (where all product nformaton s only avalable dgtally) and showrooms (where all product nformaton s avalable n person). Thrd, Warby Parker s an nnovator n the use of showrooms and by desgn, fulfllment from a centralzed locaton, condtonal on a purchase, s dentcal for all channels (onlne, HTO, and showroom). 3 See for detals. 4 When WarbyParker.com entered the $22 bllon US eyewear market n February 2010, only 1-2 percent of all sales n the category occurred onlne.

5 Bell, Gallno and Moreno: Offlne Showrooms and Customer Mgraton n Omn-Channel Retal 5 Fgure 2 WarbyParker.com We contrbute three new substantve fndngs. Frst, showrooms ncrease sales wthn the tradng area both overall and through the onlne channel as well. Estmated average effects of 7.4 percent and 2.9 percent, respectvely, are statstcally and economcally sgnfcant. 5 Ths mples that the showroom not only delvers sales n ts own rght, but also confers awareness and brand benefts that drve ncremental sales n the exstng onlne channel. We further show that the showroom s, on average, the most effectve customer acquston channel and that the brand and awareness benefts are not due to other factors, ncludng concomtant advertsng. Second, showrooms mprove operatonal effcency by ncreasng converson n the samplng channel and decreasng returns. Regardng converson, samplng channel orders fall 1 percent whle 5 We subject these and all other fndngs to robustness checks by varyng model specfcaton, covarate selecton, and data ncluson crtera and verfy that our substantve fndngs hold n all cases. Summares are reported throughout the paper and complete fndngs are avalable from the authors, upon request.

6 6 Bell, Gallno and Moreno: Offlne Showrooms and Customer Mgraton n Omn-Channel Retal samplng channel sales fall by a lesser amount, 4.5 percent. Ths mpled (and sgnfcant) converson ncrease observed n the aggregate, s mrrored n ndvdual-level data. Wthn the tradng area of showrooms, the probablty of repeated HTOs declnes 1.6 percent and the probablty of ndvdual-level converson of try-ons to sales mproves by 1 percent. We elaborate later n more detal, but these fndngs pont to the underlyng customer mechansm the showroom attracts customers wth the hghest haptc need leavng those remanng n the samplng channel even though they had the opton of vstng a showroom, better algned to t. We further valdate benefcal mgraton.e., customers who mght have ordered multple HTOs now end up n the showroom, and algnment,.e., the customers who choose to stay wth the HTO or onlne channels are better suted to those channels, by documentng a sgnfcant reducton n products returns from onlne channel purchases made wthn the catchment area of a showroom. In short, because showrooms attract ft-senstve customers wth the hghest cost to serve, other channels are left wth a more favorable consumer mx and the frm enjoys sgnfcant demand and operatonal benefts (n aggregate and n the other channels) as a result. Thrd, these benefts to the frm are amplfed when servng customers who have the most acute need for the product. Usng the dopter measure of the correctonal lens as an objectve proxy for ntensty of product use, we show that the ablty of the showroom to reduce overall return rates s heghtened for these types of products. 6 Relatedly, we fnd that all of the demand and operatonal effcency benefts ntensfy wth tme, mplyng that showrooms contrbute not only to brand awareness but also to what we term channel awareness as well. Specfcally, showrooms have a lastng mpact on customers ablty to algn to the channel best suted to them. Fnally, whle our overrdng goal s to document new and nterestng average treatment effects on demand and operatonal effcency, we apprecate that mportant nsghts can be drawn from explorng heterogenety n effect szes, condtonal upon showroom locaton and other factors. 7 In the concludng sectons of the paper, we report that the aforementoned patterns hold at the ndvdual showroom level, and comment on possble ratonales for varaton n effect magntudes. The remander of the paper s organzed as follows. Secton 2 summarzes relevant pror research and develops our conjecture on customer channel mgraton. Secton 3 descrbes the research settng, data, and our econometrc approach and quas-expermental desgn. Secton 4 reports the overall demand and effcency effects and Secton 5 elaborates on the underlyng customer mgraton mechansm. 8 Secton 6 dscusses mplcatons for practce and decson makng and Secton 7 concludes the paper. 6 Naturally, we verfy that baselne return rates are hgher, as they should be, for the more complex sales of hgh dopter lenses. 7 We thank an anonymous revewer for suggestng these analyses. 8 A more formal model of customer decson makng that s consstent wth the observed effects s outlned n Appendx 2.

7 Bell, Gallno and Moreno: Offlne Showrooms and Customer Mgraton n Omn-Channel Retal 7 2. Background and Motvaton 2.1. E-Commerce and Omn-Channel Retalng The consumer Internet has evolved consderably snce Jeff Bezos frst sold books onlne n 1994 and from the Internet Retalng 1.0 boom and bust n the early 2000s. Indeed, the evoluton of the retal sector can be vewed, n smple terms, wth reference agan to Fgure 1. Pre-Internet all retalng took place n the upper left quadrant; actvtes then evolved n the lower rght wth Amazon.com and other pure players leadng the way. Tradtonal retalers (e.g., Crate & Barrel, Home Depot, Walmart, etc.) also developed a dgtal footprnt n the lower rght quadrant va ther own.com propertes. Most recently, practtoners have come to understand that onlne and offlne channels present dfferent costs and benefts to customers and that an omn-channel approach embracng more than smply these two optons s mperatve. Interestng, the evolutonary path to quadrants 2 (upper rght) and 3 (lower left) dffers by retaler type. Tradtonal retalers wth legacy real estate have nnovated ntatves lke BOPS that leverage offlne fulfllment coupled wth new ways of delverng product nformaton onlne. Dgtal-frst retalers couple exstng centralzed onlne fulfllment wth new ways of delverng product nformaton offlne (ths s of course our focus and we elaborate further below). The emergent practtoner vewpont s mrrored by developments n academc research. Early work by academcs focuses on understandng why consumers and frms mght prefer onlne to offlne, or vce versa. Some ntal studes (e.g., Bakos 1997; Brynjolfsson and Smth 2000; Iyer and Pazgal 2003) explaned how and why onlne retalers can reduce search frctons for consumers and delver lower prces. Other artcles (e.g., Balasubramanan et al. 2003) showed why onlne sellers could be more convenent or offer more product varety (e.g., Brynjolfsson et al. 2009; Ghose et al. 2006). Operatons management researchers addressed nventory management mplcatons for frms. Netessne and Rud (2006), for example, derve the condtons under whch retalers prefer to drop-shp rather than to hold nventory. In related work, Randall et al. (2006) fnd that onlne retalers sellng hgh margn products and offerng less varety are more lkely to hold nventory and Gallno et al. (2016) study the consequences of channel ntegraton for sales dsperson and nventory management. Structural aspects of markets ncludng the physcal dstances customers must travel to offlne stores (e.g., Forman et al. 2009) and the extent to whch target customers have mnorty preferences and are underserved by offlne sellers (Cho and Bell 2011) also shape onlne-offlne preferences. 9 Holdng prce, assortment, and delvery tmes to consumers constant, one can also observe marked 9 The nterested reader can fnd a comprehensve revew of these and other fndngs n Nesln and Shankar (2009).

8 8 Bell, Gallno and Moreno: Offlne Showrooms and Customer Mgraton n Omn-Channel Retal dfferences n the realzed sales dstrbuton delvered by onlne versus other channels. Brynjolfsson et al. (2011), for example, show that because consumers n onlne channels engage n drect product search and access revews, the onlne channel exhbts a relatvely more dffuse sales dstrbuton, compared to a catalog channel. That s, consumers are more lkely to purchase products n the tal of the so-called Long Tal (Anderson 2006). More recently, Wang and Goldfarb (2015) show that offlne stores delver a bllboard effect that helps to drve onlne sales, especally n locatons where the brand s relatvely unknown. Shrver and Bollnger (2015) provde a structural analyss and fnd that newly opened offlne stores can eat nto onlne sales, n locatons that wthn close proxmty to the store E-Commerce and Consumer Uncertanty When Buyng Onlne Dependng upon the channel used, consumers may lack full nformaton pror to makng a purchase. Anderson et al. 2009, p. 408, for example, note that... ft s not fully observed by the customer pror to purchase... [n] retal settngs where customers select from a catalog or Internet ste wthout beng able to fully nspect the product. Full nspecton s of course very mportant to some customers when the product has non-dgtal attrbutes. Therefore, customers nablty to touch and feel products wth non-dgtal attrbutes before buyng through catalogs or onlne can: (1) act as deterrent to purchase, and (2) ncrease operatonal costs should they return products after experencng a dscrepancy between the expected and delvered product. 10 Ths uncertanty matters more n some product categores and more for some segments than others. Andy Dunn, CEO of leadng onlne-frst fashon apparel retaler Bonobos.com (see Lee and Bell 2013 for more detals) justfes hs expanson nto offlne stores by notng: There are stll people who want to touch and feel (talcs added) clothng before they purchase. 11 Lkewse, our concepton of ft uncertanty s related to prevous modelng work by Matthews and Persco (2007), who study the tradeoff between havng customers learn about product ft ex ante, or by usng the product on a tral bass, and to Swnney (2011) who looked at how strategc consumers behave when product value s uncertan. The ratonale for our complementary emprcal work s that showrooms are mechansms for delverng nformaton and reducng ft uncertanty for some customers. To the best of our knowledge, our study s the frst to document the effect of quadrant 2 retal actvtes on demand and operatonal effcency for onlne-frst retalers. 10 Whle we focus on the offlne showroom as a source of nformaton and method for resolvng uncertanty on nondgtal attrbutes, a number of other papers study the complementary phenomenon of brck and mortar retalers provdng nformaton though the onlne channel. See, for example, van Nerop et al. (2011) and Pauwels et al. (2011). 11 See and for detals.

9 Bell, Gallno and Moreno: Offlne Showrooms and Customer Mgraton n Omn-Channel Retal 9 In the operatons management lterature Yn et al and Cachon et al analyze nventory dsplay strateges but do so n conventonal offlne channels only. Studes of ths type, whle valuable, are not desgned to delneate nformatonal and fulfllment dfferences between onlne and offlne channels, and solate ther effects separately. For example, offlne channels typcally offer mmedate fulfllment whereas onlne channels offer delayed fulfllment due to shppng tmes of one or more days. Crtcally, n our study, the ntroducton of showrooms s a shock to the product nformaton avalable to some of the customers (but not others), and yet has no mpact on fulfllment. The effect of ths shock on demand not yet documented by academcs (but hnted at n the Bonobos.com example above where showrooms are thought to lead to a reducton n deterrent to purchase for certan customers), s our frst focus. In addton, artcles at the marketng-operatons nterface on the key drvers of returns (Petersen and Kumar 2009), the effect of return optons on consumer utlty (Anderson et al. 2009), reverse supply chan desgn (e.g., Gude et al. 2006, Blackburn et al. 2004) and contractual provsons ncludng fees or money-back guarantees (e.g., Davs et al. 1995, 1998, Shulman et al. 2009, Su 2009) mply that an nformatonal shock mght also affect returns. Accordngly, the potental for the nformaton shock to affect returns and operatonal effcency s our second focus. We examne ths drectly by showng how return patterns and samplng channel effcency evolve subsequent to a showroom ntroducton Brcks to Clcks and Haus to Browse When a retaler adds a new channel, exstng channels are naturally affected. Keepng wth the themes developed thus far, the exstng channel may realze more (or less) demand and / or become more (or less) operatonally effcent. Hence, t helps to thnk of exstng lterature n terms of nsghts offered nto demand effects and operatonal effects, and to utlze the framework n Fgure 1 to llumnate what channel comparsons are made n pror studes. Avery et al. (2012), for example, study whether, and f so why, addng new offlne stores n a market helps or hurts onlne sales there. In ndustry jargon (and by ttle) ths research focuses on brcks to clcks,.e., addng quadrant 1 (offlne fulfllment, offlne nformaton delvery) to quadrant 4 (onlne fulfllment, onlne nformaton delvery). The authors fnd synerges onlne sales ncrease when offlne stores enter the market ntally from new customers n partcular, and then n general as tme progresses. In addton, catalog sales are adversely affected, confrmng earler fndngs (e.g., Pauwels et al. 2011). 12 Our work s substantvely dstnct from these and related studes n three mportant ways. Frst and foremost, we focus on what happens when Quadrant 2 (onlne fulfllment, offlne nformaton 12 A number of other studes focus on the reverse problem,.e., what happens when quadrant 4 s added to quadrant 1. For an excellent revew of key fndngs and conceptual summary see Avery et al. (2012).

10 10 Bell, Gallno and Moreno: Offlne Showrooms and Customer Mgraton n Omn-Channel Retal delvery) s added to Quadrant 4 (onlne fulfllment, onlne nformaton delvery). Showrooms, the nstrument of choce used by leadng onlne-frst retalers lke WarbyParker.com and Bonobos.com, among others, are not stores as they hold no nventory. In showrooms, consumers can touch, feel, and nspect products, pror to any purchase that s fulflled onlne. We are therefore able to focus drectly on the connecton between customers need for tactle product nformaton pror to purchase and channel preferences, because the mpact of Quadrant 2 on Quadrant 4 occurs only va nformaton delvery. 13 Second, there are sound theoretcal reasons for showrooms, yet, to our knowledge, no supportng emprcal studes. Showrooms overcome lmtatons of the onlne channel n sellng products wth uncertan ft, yet mantan the poolng benefts of centralzed fulfllment (Eppen 1979). As noted earler, showrooms, whch requre nether replenshment nor demand forecastng, are far less operatonally complex than stores. If showrooms unlock ncremental sales and operatonal benefts then practtoners may want to gve serous consderaton to ths type of cost-effcent offlne channel. Consequently, manageral mplcatons arsng from our work are dfferent from, yet complementary to, those from the exstng lterature. In the sprt of Gallno and Moreno 2014 who show how Buy Onlne, Pck up In-Store (or BOPS) ntatves help offlne-frst retalers, we show another nuance to the nformatonal functon of channels, by demonstratng how showrooms help onlne-frst retalers. Thrd, and smlar to pror omn-channel studes n marketng, we report fndngs on the demand mpact of channel nnovaton. In addton, however, we add new results relatng to operatonal effcency an area largely overlooked n the extant lterature. If, for example, nformaton shocks va showrooms nduce customer mgraton and alter channel algnment (we elaborate on these terms subsequently) ths may mpact operatonal effcency. Specfcally, whether customers become more or less lkely to return products or convert to purchase from samplng. Recent research n operatons management shows that customers wth specfc characterstcs are more lkely to defect n response to shocks n servce levels and mgrate to the compettor that best serves them (e.g., Buell et al. 2010; Buell et al. 2011). We nvestgate the conceptually parallel phenomenon of whether customers wth a specfc characterstc hgh senstvty to ft are more lkely to leave pre-exstng onlne and samplng channels and move to showrooms. And f so, whether ths s operatonally effcent for the retaler. In summary, we focus drectly and precsely on the core channel functon of nformaton, and how and why the nformaton delvery mechansm affects operatonal effcency as well as realzed demand. As such, the pont of comparson s very much whether customers obtan nformaton va 13 Studes that measure the mpact of Quadrant 1 on 4 (and vce versa) do not seek to solate nformatonal or fulfllment effects separately as nformaton delvery and logstcs methods are both dfferent n the two channels.

11 Bell, Gallno and Moreno: Offlne Showrooms and Customer Mgraton n Omn-Channel Retal 11 browse (onlne) or va haus (offlne), and what ths means not only for dgtal-frst retalers but also any retaler consderng the zero nventory store, aka showroom, as part of ther omnchannel strategy. 3. Insttutonal Settng, Data, and Econometrc Approach 3.1. Insttutonal Settng WarbyParker.com, the conc and leadng onlne eyewear brand, suppled sales and returns data whch we augmented wth addtonal data derved from the US Census and other sources. Founded n February 2010, the frm has sold over 1,000,000 frames through onlne, samplng, and offlne channels. 14 The company sells glasses wth prescrpton lenses and sunglasses and all frames offered by Warby Parker are exclusvely desgned by them and unavalable through tradtonal offlne retal channels (Lens Crafters, Sunglass Hut, etc.). Durng the perod of our data all eyewear sold for 95 dollars (ncludng prescrpton lenses). These data have three features that make them deal for our research. Frst, and most crtcal, eyewear s an exemplar product category for the mportance of non-dgtal product attrbutes n the purchase process. When WarbyParker.com launched n 2010, only 1-2 percent of the $22 bllon US eyewear market was transacted onlne. Ths s not surprsng as eyewear s a category n whch many customers would lke to touch, feel, and try on the product before purchasng t. Second, whle WarbyParker.com was founded onlne, the company offered a samplng program (dscussed below) mmedately upon launch, leadng ndustry observers to refer to them as the Netflx of Eyewear. 15 In terms of vsceral product nformaton, the samplng channel s ntermedate between full exposure to the entre product lne n a showroom, and vrtual exposure onlne, whch allows us to examne the nformaton functon n detal. Thrd, the showrooms dffer from the other two channels (onlne and samplng) only n regard to the amount of nformaton avalable to customers pre-purchase. Snce all three channels use dentcal onlne fulfllment we can solate the mpact of nformaton alone, free from potental confounds related to dfferences n fulfllment. The three channels, wth addtonal descrpton are:. Web Channel (wth Vrtual Try-On Opton) Customers can browse the entre product lne at the webste pror to purchasng. In addton, they can upload ther own pctures and examne, vrtually, the ft, feel and style of dfferent frames. Ths vrtual try on tool s based on state-of-the-art technology and presents realstc mages, yet t s not hard to magne the lmtatons t has n closng the experental gap,.e., the gap 14 The channel s the medum through whch customers access to product nformaton before placng an order, as orders are always placed through the webste and centrally fulflled n the same way b html.

12 12 Bell, Gallno and Moreno: Offlne Showrooms and Customer Mgraton n Omn-Channel Retal between what customers mght learn about products from the vrtual process and what they mght experence when the product s delvered.. Samplng Channel ( Home Try-On Program) Customers who partcpate n the Home Try-On (HTO) program receve fve frames free of charge, for fve days. 16 Specfcally, customers go onlne and select fve frames from the complete selecton offered and have them delvered. When the fve days are up, customers return the frames and decde to buy or not. Purchases are made on the web and fulfllment s dentcal to that for the web channel. We follow protocols establshed by management, where an HTO order s sad to convert f the customer recevng the HTO makes a purchase wthn the subsequent two months. In our data the converson rate (fracton of HTO orders that lead to a purchase) s lower than 50 percent. Customers who partcpate n the HTO, relatve to those who go to the ste only, have better product nformaton as they have been able to physcally touch, try, and test the product pror to any purchase.. Showroom Channel For customers who want to touch, feel, try, and experence the entre product lne physcally pror to makng a purchase decson, the precedng two channels are nadequate. Recognzng ths, Warby Parker developed a thrd channel, the showroom, to help overcome ths problem. Durng the perod of our data, the frm opened a total of 20 showrooms n 15 dfferent locatons across the US (13 were n contnuous operaton, 7 closed). Showrooms are establshed n partnershp wth dfferent local retalers who devote a porton of ther retal space to dsplayng the Warby Parker product lne. In cases where customers purchased, transactons were placed exactly as they were for the other two channels,.e., through the webste and were fulflled onlne Detaled Data Descrpton We utlze data on: (1) sales through the three channels, (2) HTO orders (requests made by customers to receve samples usng the aforementoned program, whch may or may not result n a purchase) and, (3) product returns. The data cover a 37 month (158 week) perod from the openng of the busness n February 2010 through March 2013, and nclude all transactons. Snce sales data are collected from ncepton they are not left censored. To mtgate rght censorng we consdered returns and conversons that cover a perod that expands untl May 2013,.e., a further two months (as noted earler the frm uses a two-month wndow to determne conversons ). Most of our analyses are at the week-zip code level and focus on the channel source that orgnated the 16 Although HTO s free to the customers, and helpful for closng the experental gap, t s not free for Warby Parker and has a sgnfcant mpact on gross margns.

13 Bell, Gallno and Moreno: Offlne Showrooms and Customer Mgraton n Omn-Channel Retal 13 sale. WEB sales are onlne purchases, HTO sales are purchases made wthn two months of when the customer receved the HTO order (sample), and SHOWROOM sales occurred offlne. 17 For each sale, through each channel, we know the ZIP code to whch t was shpped. In addton, we obtaned ZIP-code level data on household demographcs and spendng at offlne eyewear retalers from the 2010 ESRI Demographcs and Busness Database. 18 These data contan ZIP code level varables typcally avalable from the US Census (e.g., populaton sze, area, household ncome, etc.) as well as others that are germane to the offlne retal envronment. We use these data to construct ZIP code level control varables and also develop the propensty score matchng approach n whch we develop samples of locatons wth and wthout showrooms. Some ndvdual-level data were also made avalable to us. Specfcally, we know: (1) whether the order was a frst order for a partcular customer or a repeat order, (2) f the transacton ncluded a prescrpton, (3) the detals of the lenses n the order (prescrpton strength), (4) whether the product was eventually returned, and (5) when an ndvdual placed an HTO order and f t resulted n a sale Econometrc Approach We use a Dfference-n-Dfferences (DD) approach wth propensty score adjustment. Conversely, a naïve approach would smply look at the dfference n dependent varables of nterest, e.g., demand and returns, between the perod before and after the showroom opened at a partcular locaton. Unfortunately, factors completely unrelated to the openng of a showroom can dffer between the two (pre-showroom and post-showroom) perods, hence the need to utlze a DD approach and hold all else constant Standard Dfference-n-Dfferences (DD) Angrst and Pschke (2008) provde an exhaustve dscusson of socal scence applcatons of DD; moreover, there are several recent applcatons n marketng and operatons management (e.g., Caro and Gallen 2010, Parker et al. 2014), ncludng multchannel retal as well (e.g., Gallno and Moreno 2014, Avery et al. 2012). To mplement the DD method n our research, we must dentfy a porton of the populaton unaffected by the nterventon,.e., the openng of a showroom. To do ths, we select a control group that shares characterstcs wth the group that was exposed to the treatment and the effect of the treatment s ascertaned by comparng the dfferences between the control group and the treatment group, before and after the treatment s appled. 17 To track SHOWROOM sales the company mplemented the followng system. After a customer decded to buy whle n the showroom, they were gven a 5 dollar coupon code whch the salesperson entered at the end of the checkout process, makng t possble to lnk the transacton wth the showroom vst. 18 These are avalable for purchase at for detals.

14 14 Bell, Gallno and Moreno: Offlne Showrooms and Customer Mgraton n Omn-Channel Retal We delneate the two groups by consderng the dstance between a potental customer and a showroom locaton. Only those customers contaned wthn a reasonable tradng area around the showroom are potentally nfluenced by ts presence; hence, those wthn the tradng area n the treatment group and those wthout are n the control group. Followng crtera set forth by the frm, we defned the area of nfluence of a showroom as a 30 mle radus from the locaton of the showroom. 19 ZIP codes wthn the 30-mle radus are n the treated group and those outsde are n the control group. The emprcal dstrbuton of showroom sales shows that 82 percent come from ZIP codes wthn a 15 mle radus of a showroom and 87 percent come from wthn a 30 mle radus. A total of 20 showrooms were open at some pont durng our perod of analyss. 13 were n contnuous operaton and 7 opened and closed. Ths helps dentfcaton snce t adds varaton to the control and treatment groups over tme. For the results that follow, we focus on those ZIP codes that saw orders of at least 40 frames durng the perod of analyss. 20 Our fnal panel conssts of 1,972 ZIP codes, 823 of whch were n the nfluence area of a showroom at some tme durng the perod of analyss. For ease of exposton, Table 1 presents summary statstcs for llustratve varables used n the analyss. 21 A potental concern wth our dentfcaton strategy s that the locatons where the company opened the showrooms are endogenous wth demand. Indeed, one would not expect showrooms to be opened n random locatons. Management nformed us that whle they had a clear dea about ctes of nterest, ther decson to open a showroom n a specfc ZIP code was drven by a combnaton of market potental and partnershp opportunty. These decsons by management resulted n a showroom map that, whle not random, s not completely endogenous ether. As dscussed next, we therefore utlze a propensty score model to equalze treated and non-treated locatons Quas-Expermental Desgn: Propensty Scores Weghtng DD s an effectve approach when the treatment and control groups follow equal trends n the pre-treatment perod. In our case, ths assumpton may not hold as ZIP codes near showrooms and ZIP codes far from showrooms are potentally very dfferent n ther characterstcs. The medan household ncome of the treatment group, for example, s $75,798/year whereas the correspondng number for the control group s much less ($68,693/year). 19 We vared ths dstance and conducted extensve robustness checks, all of whch are documented later n the paper and avalable from the authors upon request. 20 As we dd wth the 30-mle radus showroom tradng area, we agan check the robustness of the results to ths decson. We fnd that all results hold allowng the sales value per ZIP code to range from 1 to 150 and report the results throughout the text. Complete results are also avalable from the authors, upon request. 21 Summary statstcs for all varables n the master lst are avalable from the authors, upon request.

15 Bell, Gallno and Moreno: Offlne Showrooms and Customer Mgraton n Omn-Channel Retal 15 Table 1 Summary Statstcs - Mean (Standard Devaton) by ZIP Code Varables Unt Treated Control Total Total Sales (Unts) Frames (410) (96) (281) HTO Orders (Unts) Orders (278) (190) (232) Populaton Count. 33,973 31,673 32,633 (20,746) (16,115) (18,227) Households Count. 13,083 12,531 12,762 (7,559) (5,604) (6,498) Dversty Index Index (21) (19) (20) Medan Household Income Dollars 75,798 68,693 71,658 (33,346) (29,382) (31,294) Per Capta Income Dollars 40,979 36,738 38,508 (19,709) (14,331) (16,916) Eyeglasses & Contact Mkt. Index (50) (42) (46) ZIP Codes Count ,149 1,972 A propensty score adjustment s one effectve way to counter mbalances n values of the characterstcs for treatment and control locatons. Propensty score-based methods frst ntroduced by Rosenbaum and Rubn (1983) are among the most popular and wdely used socal scence models for dealng wth endogenety (see Imbens and Wooldrdge 2009 for a comprehensve revew and dscusson). Snce potental bases arse when covarates are correlated wth the treatment ndcator, the so-called propensty score s the probablty that an ndvdual observatonal unt receves the treatment, condtonal on ts observed covarates. Therefore, for subpopulatons of observatons wth the same propensty score, covarates wll be ndependent of the treatment. Ths elmnates the bases n the comparsons between treated and control unts. Propensty scores can be used by consderng the scores as samplng weghts (see Rosenbaum 1987, Hrano and Imbens 2001, Hrano et al and Gensler et al. 2012), as propensty score weghtng re-weghts treatment and control observatons to make the two populatons comparable n terms of ther observable covarates. Followng Hrano and Imbens (2001) we defned the weghts as follows: ω(w, x) = W ê(x) + 1 W 1 ê(x) where W = 1 ndcates a treated ZIP code and ê(x) s the estmated probablty of beng treated. After obtanng these weghts, we estmate our DD model, ncludng those weghts n the estmaton. Among all methods consdered n the comprehensve revew by Imbens and Wooldrdge (2009), the approach we take s deemed especally attractve for practcal applcatons (e.g., Bang and Robns 2005, Hernn and Robns 2006). We do our matchng at the most granular geographc

16 16 Bell, Gallno and Moreno: Offlne Showrooms and Customer Mgraton n Omn-Channel Retal level avalable to us: the ZIP code. To compute the weghts, we utlze almost 50 dfferent ZIPcode-level varables from 2010 ESRI Demographcs and Busness Database ncludng demographc, socoeconomc, and busness-related varables pertnent to our nsttutonal context Valdaton of Propensty Score Weghtng Approach Propensty score weghtng can be mplemented n a varety of ways, accordng to the choce of lnk functon, number of varables used n estmaton, and so on. Frst, n order to ensure that our approach works as t should, we consder logt and probt lnk functons estmated on both large (47) and small (22) sets of varables and confrm that our substantve fndngs are robust to these alternatve computatons of the weghts. Second, we check to make sure that our weghts actually balance the treatment and control groups properly. To do ths, we follow Guo and Fraser (2009) and smply compare estmates from a set of weghted and unweghted regressons. Specfcally, n these regressons, the dependent varable s a partcular covarate, e.g., Total ZIP code populaton, and the ndependent varable s the treatment ndcator,.e., whether a showroom opened or not n a ZIP code. When we use the logt lnk functon and all 47 varables, the estmate for ths covarate from the unweghted regresson s hghly statstcally sgnfcant (β = 10,911, p <.001), whereas the estmate from the weghted regresson s not (β = 6,036, n.s.). That s, showrooms are more lkely to be placed n ZIP codes wth more people, and any relable analyss wll need to account for ths fact. Smlarly, we fnd that the unweghted regressons mply that showrooms are, for example, more lkely n ZIP codes wth hgher per capta ncome, larger eyewear market sze and retal market potental, and younger populatons. The weghted regressons elmnate all these sgnfcant dfferences, agan provdng evdence that our propensty score method properly balances the data The Overall Impact of Showrooms on Demand and Mgraton The openng of a showroom can affect customers who lve wthn the tradng area, whch we assumed to be 30 mles (see Secton 3). In ZIP codes where customers cannot access a showroom (control ZIP codes) there are two channels through whch customers can obtan product nformaton: the WEB channel and the HTO channel. For ZIP codes n the treatment group, the SHOWROOM channel s added as a thrd opton. Total sales n ZIP codes n the treatment group,.e., those 22 Specfc measures nclude ZIP-code level populaton and ncome metrcs, the proporton of households n the target age demographc (25 45 years-old), market sze metrcs such as the number of offlne eyewear stores n the 3-dgt zp code area, and so on. A full lst of these varables along wth descrptve statstcs s avalable from the authors, upon request. 23 All regresson results for all four permutatons: logt and probt lnk functons, large and small varable sets, are avalable from the authors, upon request.

17 Bell, Gallno and Moreno: Offlne Showrooms and Customer Mgraton n Omn-Channel Retal 17 wthn the tradng area of a showroom, are expected to ncrease once t opens smply because sales through the showroom channel were zero before t opened. In addton to ths postve demand effect n treatment ZIP codes through the showroom tself, we also measure the mpact on demand va the other two channels as well. Specfcally, whether customers mgrate from the two pre-exstng channels to the showroom, and f so, how that affects sales and operatonal effcency. Table 2 summarzes the varables used n these analyses. Table 2 Varable Defntons Evaluatng the Impact of Showrooms Varable Name Defnton T OT ALSALES t Total frames sold (n unts) at ZIP Code on week t. W EBSALES t Total frames sold (n unts) through the WEB at ZIP Code on week t. HT OSALES t Total frames sold (n unts) through the HTO program at ZIP Code on week t. OP EN t Dummy varable that s 1 f on week t there was a Showroom open n ZIP Code. Converson of Home Try-On Program Varable Name Defnton HT OORDERS t Total HTO orders through the HTO program at ZIP Code on week t. OP EN t Dummy varable that s 1 f on week t there was a Showroom open n ZIP Code Impact on Total Sales The followng regresson equaton captures the effect of the showroom openng on total demand: log(t OT ALSALES) t = μ + α 1 OP EN t + W t + ɛ t (1) where OP EN t = 1 ndcates that ZIP code s n the vcnty of a showroom that s open on week t, and 0 otherwse. Showrooms open at dfferent ponts n tme so OP EN t captures varaton not only across dfferent ZIP codes but also wthn a ZIP code over tme. Fxed effects μ capture any tme-nvarant factors, and W t (one dummy for each tme perod) capture the trend n overall sales over tme. The coeffcent on OP EN t, α 1, s nterpreted relatve to baselne sales for a gven ZIP code and the seasonalty patterns for a gven week. If α 1 s postve and sgnfcant, then openng a showroom delvers an ncrease n overall sales for ZIP codes n the vcnty of the showroom, compared to matched ZIP codes where a showroom s not accessble. Showroom sales are by defnton zero before a showroom opens, so we expect α 1 > 0. Table 3 shows the results wth (Column 2) and wthout (Column 1) the propensty score adjustment (our preferred specfcaton). 24 The effect of a showroom openng on total demand s substantal and 24 For ease of exposton we report only the propensty-score adjusted results for the remander of the paper. The unadjusted regresson results delver the same substantve concluson and all are avalable from the authors, upon request. Moreover, ths frst fndng and all those reported subsequently are robust to functonal form changes as well (lnear and Posson regresson models produce the same substantve concluson).

18 18 Bell, Gallno and Moreno: Offlne Showrooms and Customer Mgraton n Omn-Channel Retal economcally meanngful, at about 7.5 percent (α 1 = 0.074, p < 0.001). 25 An effect of ths magntude s probably not solely due to new sales through the showroom only, but to a combnaton of effects through all three channels. Moreover, the effect(s) could arse from ncreases n brand awareness rather than provson of vsceral nformaton per se, and there could be heterogenety n the effect accordng to latent market potental and so on. As such, we explore these nuances shortly. Table 3 VARIABLES Impact on Total Sales. (1) (2) log(sales) log(sales) OPEN 0.105*** 0.074*** (0.009) (0.021) Fxed Effects YES YES Tme Controls Week-year Week-year Prop. Score Weghtng NO YES Observatons 313, ,570 R-squared Number of ZIP Codes 1,972 1,972 Robust standard errors n parentheses p < 0.05, p < 0.01, p < Impact on the Web Channel The regresson specfcaton s unchanged except that the dependent varable s now log(w EBSALES) t : log(w EBSALES) t = μ + β 1 OP EN t + W t + ɛ t (2) If β 1 s postve and sgnfcant, then openng a showroom delvers an ncrease n web sales for ZIP codes n the vcnty of the showroom, compared to matched ZIP codes where a showroom s not accessble. Table 4 shows the results. Openng a showroom s assocated wth an ncrease n web sales of 2.9 percent (β 1 = 0.029, p < 0.001), whch confrms the total ncrease n sales observed n Table 3 was not solely attrbutable to sales n the showrooms alone. The web sales ncrease could be due to customers browsng n the showroom and buyng onlne. Alternatvely, a customer vstng WarbyParker.com could attrbute more legtmacy to the brand smply because t has a local physcal presence, and therefore be more nclned to buy onlne. 25 The percentage ncrease can be calculated as (e α 1 1) 100.

19 Bell, Gallno and Moreno: Offlne Showrooms and Customer Mgraton n Omn-Channel Retal 19 Table 4 VARIABLES Impact on Web Sales. (1) log(websales) OPEN 0.029*** (0.007) Fxed Effects YES Tme Controls Week-year Prop. Score Weghtng YES Observatons 313,570 R-squared Number of ZIP Codes 1,972 Robust standard errors n parentheses p < 0.05, p < 0.01, p < Impact on the Samplng Channel (Home Try-On) The regresson specfcaton s unchanged except that the dependent varable s now log(ht OSALES) t : log(ht OSALES) t = μ + γ 1 OP EN t + W t + ɛ t (3) If γ 1 s postve and sgnfcant, then openng a showroom delvers an ncrease n home try on sales for ZIP codes n the vcnty of the showroom, compared to matched ZIP codes where a showroom s not accessble. Table 5 (Column 1) shows the results. Openng a showroom, however, s assocated wth a decrease n home try on sales of 4.5 percent (γ 1 = 0.045, p < 0.001). As we show later n the robustness secton, the drop n sales through ths channel s even more pronounced n hgh actvty ZIP codes,.e., those where the base level of total sales s hgh. Of course the effectveness of the samplng program depends not only on total sales through ths channel but also on the number of orders that went out n the frst place, and therefore the mplct rate of converson from samplers to customers. Hence, we estmate: log(ht OORDERS) t = μ + δ 1 OP EN t + W t + ɛ t (4) Table 5 (Column 2) shows the results. Openng a showroom s also assocated wth a decrease n home try on orders (δ 1 = 0.100, p < 0.001). Notce, however, that the decrease n orders exceeds that of sales, whch mples that the converson rate of the samplng program mproves n locatons where showrooms are opened. Taken together, these results suggest that a statstcally sgnfcant number of customers mgrate from the samplng program to the showroom, after the latter s opened, and that those who reman n the samplng program after a showroom opens are more lkely to generate successful conversons.