Battle of the Retail Channels: How Internet Selection and Local Retailer Proximity Drive Cross-Channel Competition

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1 Assocaton for Informaton Systems AIS Electronc Lbrary (AISeL) ICIS 2007 Proceedngs Internatonal Conference on Informaton Systems (ICIS) December 2007 Battle of the Retal Channels: How Internet Selecton and Local Retaler Proxmty Drve Cross-Channel Competton yu hu Purdue Unversty Erk Brynjolfsson MIT Mohammad Rahman Purdue Unversty Follow ths and addtonal works at: Recommended Ctaton hu, yu; Brynjolfsson, Erk; and Rahman, Mohammad, "Battle of the Retal Channels: How Internet Selecton and Local Retaler Proxmty Drve Cross-Channel Competton" (2007). ICIS 2007 Proceedngs Ths materal s brought to you by the Internatonal Conference on Informaton Systems (ICIS) at AIS Electronc Lbrary (AISeL). It has been accepted for ncluson n ICIS 2007 Proceedngs by an authorzed admnstrator of AIS Electronc Lbrary (AISeL). For more nformaton, please contact elbrary@asnet.org.

2 BATTLE OF THE RETAIL CHANNELS: HOW INTERNET SELECTION AND LOCAL RETAILER PROXIMITY DRIVE CROSS-CHANNEL COMPETITION Erk Brynjolfsson* Yu (Jeffrey) Hu MIT Sloan School of Management Krannert School of Management Cambrdge, Massachusetts Purdue Unversty West Lafayette, Indana Mohammad S. Rahman Krannert School of Management Purdue Unversty West Lafayette, Indana Abstract A key queston for Internet commerce s the nature of competton wth tradtonal brck-andmortar retalers. Although tradtonal retalers vastly outsell Internet retalers n most product categores, research on Internet retalng has almost entrely neglected ths fundamental dmenson of competton. How and where can Internet retalers wn ths battle? To answer ths queston, we collect a unque dataset on the local market structure and match t to a dataset on consumer demand through both Internet and catalog channels. We fnd that local market structure can sgnfcantly explan varatons n demand through these two drect channels. Interestngly, we fnd that Internet sales of nche products, often unavalable n physcal stores, are largely mmune from competton by tradtonal retalers. Snce the Internet channel sells proportonately more nche products than the catalog channel does, the competton between Internet and local store channels s less ntense than that between the catalog and local store channels. Keywords: Internet, competton, retalng, channel, nche products, geography. *The authors are lsted n alphabetcal order. Twenty Eghth Internatonal Conference on Informaton Systems, Montreal

3 Economcs and Busness Value of Informaton Systems 1. Introducton Our prmary compettors are brck-and-mortar, so we have to be really responsve from a fulfllment standpont. More and more, we're gong to be competng wth the guy down the street where a customer can drve and pck up an order the same day. Kurt Goodwn, VP of Operatons of Crutchfeld, quoted n Dubbs (2002) A key queston for Internet commerce s the nature of competton wth tradtonal brck-and-mortar retalers. In almost every product category sold on the Internet, consumers have the opton of buyng from a tradtonal retaler nstead. Although tradtonal retalers vastly outsell Internet retalers n most of these categores, research on Internet retalng has almost entrely neglected ths fundamental dmenson of competton. How and where can Internet retalers wn ths battle? In order to compete wth brck-and-mortar stores, Internet retalers have made large nvestments n delvery nfrastructures, Web technologes, marketng promotons, and customer servces. 1 For nstance, Jeff Wlke, SVP of Amazon, dentfed the nstant gratfcaton avalable n tradtonal stores as the key challenge to Amazon s growth. 2 As a result, n order to compete wth brck-and-mortar retalers, many Internet frms have bult delvery centers all across the U.S., speedng up the delvery of ther products to consumers. In order to attract consumers, who usually do not pay shppng and handlng charges when purchasng from local stores, Internet retalers frequently offer free-shppng dscounts. Fnally, Internet retalers have nvested heavly n technologes that allow consumers to carefully nspect and sample products before makng purchases, and they offer customer servces that are as good as those offered by brck-and-mortar retalers, allevatng consumers concern about returns and refunds. Despte the wdely-accepted noton that Internet commerce competes wth brck-and-mortar commerce, and despte the mllons of dollars that have been nvested n the competton between Internet retalers and brck-and-mortar retalers, our understandng of ths type of competton s remarkably lmted. In partcular, there s lttle academc research that studes whether the level of ths competton vares n dfferent local markets and for dfferent products. Ths paper attempts to answer these questons usng a unque combnaton of data sets. There exsts strong theoretcal support for that the competton between Internet retalng and brck-and-mortar retalng vares n dfferent local markets (Balasubramanan 1998). In markets where consumers have access to many brck-and-mortar stores, the competton between Internet retalng and brck-and-mortar retalng s ntensfed; whle n markets where consumers are under-served by local stores, Internet retalng faces lttle or no competton. Thus, t s lkely that the local market structure,.e., the number of local stores wthn the dstance customers are wllng to travel for purchasng consumer products, wll affect consumer demand through the Internet channel. 3 The scarcty of academc research on ths topc s at least partly due to the dffculty n collectng data on local market structure. Only recent technologcal advances have made ths data avalable. In ths paper, we collect a unque data set on the local market structure. We then match ths data set on local market structure to a data set on consumer demand through drect channels that nclude the Internet and catalog channels. Our analyses provde strong evdence that the local market structure can sgnfcantly explan the varaton n demand through drect channels, even after controllng for the relevant demographc varables n each local market. The competton between Internet retalng and brck-and-mortar retalng should also vary for dfferent products. Economc theory shows that product dfferentaton lowers the competton among frms (e.g., Perloff and Salop 1985). Thus, when an Internet retaler sells popular products that are lkely to be wdely avalable n local stores, the competton between such an Internet retaler and local stores s ntensfed. However, Internet retalers may face lttle or no competton f they sell nche products that are unlkely to be avalable n local stores. Our emprcal results are consstent wth ths theoretcal predcton. We fnd that the mpact of local market structures on consumer demand through drect channels s smaller n sze for nche products than that for popular products. 1 ComScore, an onlne market research company, estmates that n 2006 non-travel Internet retal has accounted for approxmately 7 percent of U.S. consumers retal spendng excludng gas, autos and food (Rubn 2006). 2 Author s ntervew at Amazon Headquarters, Seattle, Washngton, February, In economcs, the term market structure s used to descrbe the state of the market wth respect to competton. 2 Twenty Eghth Internatonal Conference on Informaton Systems, Montreal 2007

4 Brynjolfsson et al. / Battle of the Retal Channels Although, n many respects, the Internet channel s smlar to the catalog channel, prevous research has shown that nche products can make up a larger percentage of a company s total sales through the Internet channel than through the catalog channel (Anderson 2004; Brynjolfsson et al. 2006). The current study brngs to lght an nterestng mplcaton of that fndng on the relatve competton among the Internet, catalog, and local channels. Specfcally, snce the Internet channel sells proportonately more nche products than the catalog channel, t s lkely that the level of competton between the Internet channel and local stores s lower than the level of competton between the catalog channel and local stores. We fnd emprcal results that are consstent wth ths predcton. Understandng how the level of the competton between drect channels and local stores vares n dfferent local markets and for dfferent products has both mportant manageral mplcatons and economc consequences. Answers to ths queston would help economsts and publc-polcy makers more accurately estmate the new value created by Internet commerce, gudng the government n ts polces toward the Internet commerce. In addton, our study shows that the local market structure varable can have a sgnfcant mpact on consumer demand and ths mpact dffers for dfferent products. Hstorcally, frms n the drect retalng ndustry have long used varous measures to segment consumers and treated dfferent consumer segments wth dfferent marketng strateges. Thus, our results suggest that ncludng the local market structure varable n these frms marketng decsons can make ther marketng strateges more effectve. The remander of the paper s organzed as follows. In the next secton, we brefly dscuss the relevant lterature on ths topc. In Secton 3, we dscuss our data collecton methodology and data sources. We present our emprcal analyses and results n Secton 4. Fnally, we conclude the paper n Secton Lterature Revew Theoretcal research on the competton between drect retalng and tradtonal brck-and-mortar retalng can be traced to Balasubramanan (1998). By analyzng a game-theoretc model that has a drect retaler and multple brck-and-mortar stores, he suggests that the drect retaler strongly competes wth local stores. As the number of brck-and-mortar stores ncreases, the competton becomes more ntense and the proft of both the drect retaler and brck-and-mortar stores decreases. In a landmark paper, Bresnahan and Ress (1991) fnd that the number of frms n a local market s correlated wth market sze varables. They then show that as the number of competng frms n a local market ncreases, the competton becomes more ntensfed and frms proft margns fall. More recently, Campbell and Hopenhayn (2005) fnd that the competton s tougher n larger markets. These fndngs support the hypothess that a drect retaler would be n an advantageous poston n local markets where there are no or few brck-and-mortar stores, compared wth n local markets where there exst many local stores. Holdng the drect retaler s prce constant, ts demand wll fall as the number of local stores ncreases. Prevous research suggests that greater selectons, lower prces, and convenence are prmary drvers n entcng customers to the catalog channel (Btran and Mondschen 1996). Internet markets can mprove consumer welfare through wder product selecton (Brynjolfsson et al. 2003). Consumer demand through the Internet wll be hgher n local markets where local prces and sales tax rates are hgh (Chou 2005; Ellson and Ellson 2006; Goolsbee 2000; Goolsbee 2001; Prnce 2007). However, the mpact of the local market structure,.e., the number of local stores, on consumer demand through the drect channels that nclude the Internet and catalog channels has not been explored by prevous lterature. In ths paper we am to brdge ths gap. Our paper s closely related to two nterestng papers that study how geographcal varables can have an mpact on consumer behavor onlne-- Sna and Waldfogel (2004), Forman et al. (2006), although t also dffers from them n many aspects. Sna and Waldfogel (2004) fnd that an ndvdual s more lkely to connect to the Internet f there are more content that s of nterest to her on the Internet and there are less content that s of nterest to her locally. Ther results suggest that the Internet competes wth offlne meda, but they do not study the competton between Internet retalng and brck-and-mortar retalng, whch s the focus of our paper. Forman et al. (2006) fnd evdence that the exstence of a dscount store or a large bookstore n a geographcal locaton decreases the lkelhood that a popular book wll appear n Amazon s lst of top 10 bestsellng books for that geographcal locaton. Our paper dffers sgnfcantly from ther paper by studyng the effect of the number of local stores on ndvdual-level demand and dscussng how ths effect vares for dfferent channels and dfferent products rather than studyng the effect of store exstence on aggregate-level demand. More specfcally, we drectly measure the ndvdual-level demand, whle they use aggregate sales rankng for top products at the geographcal locaton-level to make nferences about Twenty Eghth Internatonal Conference on Informaton Systems, Montreal

5 Economcs and Busness Value of Informaton Systems demand. Furthermore, we drectly measure the number of physcal stores at the zp code-level, whle they use the exstence of stores at the level of less specfc geographcal locatons whch nclude large ctes and small towns. Recently there are a few papers n marketng that have leveraged spatal data to understand consumers behavor (e.g., Bradlow et al for a revew of ths emergng lterature). For example, Jank and Kannan (2005) show that ncludng spatal dependence can help predct whether a consumer purchases an electronc copy or a prnt copy of the same book sold through a publsher s webste. We contrbute to ths nascent lterature by hghlghtng how the local market structure, whch vares wth geographc locaton, can affect a consumer s purchasng behavor at drect channels. We also advance the lterature by studyng how the mpact of local market structure on demand vares across the Internet and catalog channels as well as by underscorng ensung strategc mplcatons. 3. Data For ths study, we have collected data from dfferent sources. Our customer demand data comes from a large retaler of women s clothng products. 4 The retaler prmarly operates n two channels: the catalog channel and the Internet channel, wth both channels contrbutng almost equally to the frm s revenue. 5 We have nformaton regardng all the transactons made startng from May 19, 2003 to June 15, 2006 through the prmary channels: catalog channel (mal and telephone orders) and the Internet channel (webste). For each tem purchased from the retaler, we have nformaton regardng the prce pad, date of transacton, customer s unque d, whether or not the tem was returned, channel (e.g., mal, telephone, or the web) used to purchase the tem, and transacton d. Overall we have records of about 7 mllon transactons that were made by about 1 mllon unque customers. We also have nformaton regardng the catalogs and emals each customer receved between January 2005 and June For each customer, we know the customer s home zp code. Ths unque dataset enables us to determne overall demand of each consumer at the drect retaler, and also channel-specfc demand. In addton, the home zp code allows us to determne the local market structure for each customer. An mportant feature of the retaler s that t offers exactly the same product selecton (and prces) through ts Internet and catalog channels. Ths eases the frm s logstc and orderng processes. Also, the frm uses the same order fulfllment methods and facltes for the two channels. These decsons greatly facltate our research desgn by automatcally controllng for dfferences n sales tax polces, shppng costs, and the possblty of stock outs, elmnatng these alternatve explanatons for potental dfferences n the demand across the two channels. Our local market structure data comes from the two leadng provders of store-drectory servces: Yahoo Local ( a leadng onlne portal that provdes nformaton of local busnesses, and Superpages.com ( a spn-off from Verzon that s a promnent provder of yellow pages and nformaton servces. We have wrtten a set of web-crawlng programs n Python to collect data from each webste. We have frst obtaned a comprehensve lst of 41,513 zp codes served by the US Postal Servce as of November 2006 n the 50 U.S. states and the Dstrct of Columba. For each zp code, we have collected the number of women s clothng stores lsted on Superpages.com that are wthn 5 mles, 10 mles, 15 mles, 20 mles, 25 mles, and 50 mles from the center of that zp code. Smlarly, for Yahoo Local, we have collected the number of women s clothng store lsted wthn 5 mles, 10 mles, 15 mles, 25 mles, and 50 mles from the center of that zp code. In order to ensure consstency of the data collected, we have collected ths data three tmes from each webste between the last week of October 2006 and the end of November We have notced mnor and almost neglgble changes n the number of stores lsted for a few zp codes among these three snapshots, and we have not found any systematc or sgnfcant dfference among data collected n three dfferent snapshots for each zp code. For ths study, we use the data obtaned from the last collecton. In order to allevate any concern regardng accuracy of the local market structure data and any ste-specfc effect, we smultaneously collect ths data from two ndependent stes Superpages.com and Yahoo Local, and check whether the data collected from these two stes are smlar for the 5-mle radus and 10-mles radus that customers are wllng to travel for ther purchases. We are able to fnd nformaton for about 41,219 zp codes on both stes. 4 The retaler requests to reman anonymous. 5 The retaler also has a physcal store. We do not have any nformaton regardng the transactons made n the physcal store. Note that the physcal store accounts for a neglgble amount of overall sales. 4 Twenty Eghth Internatonal Conference on Informaton Systems, Montreal 2007

6 Brynjolfsson et al. / Battle of the Retal Channels For each common radus, we calculate the Pearson correlaton coeffcent between the number of stores lsted on Superpages.com and the number of stores lsted on Yahoo Local. We fnd these two sets of numbers are extremely hghly correlated. The correlaton coeffcents are presented n Table 1. Ths gves us confdence n our data on local market structure. Snce Superpages.com s the largest provder of drectory servces, we present our results usng the data on local market structure collected from ths ste. As expected, the qualtatve nature of the results does not change even f we use the data collected from Yahoo Local. Table 1: Pearson Correlaton of the Number of Stores Lsted Radus Correlaton 5 mles 0.911** 10 mles 0.951** 15 mles 0.965** 25 mles 0.973** 50 mles 0.974** ** Sgnfcantly dfferent from zero, p<0.01 A customer s demographc and socoeconomc varables may also nfluence the customer s demand. Snce we observe each customer s home zp code, we are able to collect the demographc and socoeconomc varables for each customer at the zp-code level from the U.S. Census Emprcal Analyses Table 2: Descrptve Statstcs on Customers Demand Average Number of Items Purchased Overall 0.94 Internet Channel 0.44 Catalog Channel 0.50 Sample Sze 163,933 In our analyses we focus on the latest year January 1, 2006 tll June 15, 2006 for whch we have data. An mportant consderaton n selectng the sample for our study s to control for the advertsng effect on the customer s demand. The retaler promotes ts products by sendng catalogs and emals. Although every customer wth vald emal address receves all emals, every customer does not receve all catalogs wthn a tme perod. It s lkely that customers demand wthn the tme perod from January 1, 2006 to June 15, 2006 s nfluenced by the catalogs receved durng ths tme perod as well as the catalogs receved before January 1, We have observed from the data that the mpact of a catalog lasts for about 30 days. Ths s consstent wth the retaler s past experence whch suggests that the effect of a catalog lasts for days. To be more conservatve, we only nclude customers who have receved all the catalogs that were sent out between November 1, 2005, whch s 60 days pror to January 1, 2006, and June 15, Snce our analyses focus on transactons that occurred between January 1, 2006 and June 15, 2006, we wll later use the earler data to control for hstorcal purchases. We have found 183,023 customers each of whom has receved all the catalogs sent out durng the perod between November 1, 2005 and June 15, There are a number of customers who are from outsde the 50 U.S. states and the Dstrct of Columba; we have excluded these customers from our fnal sample. Also, snce the retaler has a physcal store n Florda, t needs to collect sales tax from Florda customers. Although our results reman 6 The demographc and socoeconomc varables used n ths paper are contaned n the Summary Fle 1 and Summary Fle 3 of U.S. Census Twenty Eghth Internatonal Conference on Informaton Systems, Montreal

7 Economcs and Busness Value of Informaton Systems practcally unchanged even f we nclude Florda customers n our analyses, for the sake of consstency n sales tax polcy we have excluded the customers who are from Florda. Correspondngly, we have retaned 143,874 customers for our analyses. Table 2 presents the descrptve statstcs for our sample. On average customers n our sample bought about 1 tem from the retaler, wth approxmately 47% of ther purchases occurrng through the Internet channel Intal Results Frst, we study whether the local market structure can have an mpact on the probablty of purchasng from the drect retaler, through ether the Internet channel or the catalog channel. Assumng y s an ndcator of whether customer had postve demand between January 1, 2006 and June 15, 2006, we can estmate the effect of the local market structure on y usng bnary response models. We estmate the followng Probt model: Py= ( 1 X ) = (XQ), (1) where X s a vector of explanatory varables. We let varable NumStores be the natural log of the number of local stores lsted on Superpages.com wthn 5 mles of each customer s home zp code. 7 In column 2 of Table 3, we present the estmaton results of such a Probt model wth the explanatory varables beng NumStores and an ntercept. Reassurngly, ths result and all the results that follow reman qualtatvely unchanged, even f we use the data collected from Yahoo Local nstead of the data colleted from Superpages.com, or f we change the dstance from 5 mles to 10 mles, or to other dstances. In addton, although we only report the results usng a Probt model, our fndngs are robust to usng a Logt model as an alternatve. Our coeffcent of nterest, the coeffcent on varable NumStores, s negatve and sgnfcantly dfferent from zero. Ths ndcates that customers wth more tradtonal stores nearby are less lkely to purchase anythng from the drect retaler, and ths effect s economcally and statstcally sgnfcant. Drect retalers that operate the Internet and catalog channels drectly compete wth tradtonal brck-and-mortar retalers. Next, we nvestgate the mpact of the local market structure on the overall demand at the drect retaler. Note that the number of tems purchased s a count measure, whch can be modeled by a Posson dstrbuton. However, the Posson dstrbuton contans a strong assumpton that the mean and the varance are equal. In our data, the varance exceeds the mean whch causes over-dsperson. In ths scenaro, a Posson regresson would provde consstent parameter estmates, but the standard errors would be underestmated. Consequently, we estmate the effect of the local market structure on the overall demand usng a negatve bnomal regresson model. Negatve bnomal regresson s a generalzaton of the Posson regresson model, allowng for over-dsperson by ncorporatng an ndvdual unobserved effect nto the condtonal mean (Hausman et al. 1984). 8 Correspondngly, we estmate the followng negatve bnomal regresson model: y e µ µ f( y X ) =, y = 1,2,.. (2) y! where: y s the number of tems purchased by customer between January 1, 2006 and June 15, 2006; s a vector of explanatory varables; E( y X ) = µ = exp(x Q + ) s the condtonal mean; accounts X 7 The natural log of zero s not defned. Thus we add one to the number of local stores before takng the natural log. 8 A standard regresson model attrbutes the randomness n demand to unobservable dosyncratc random factors that affect a consumer s demand. A Posson model for count data attrbutes randomness n a consumer s demand to the fact that the realzaton of a Posson random varable wth a gven mean wll be dfferent each tme. The negatve bnomal regresson model allows for both of these sources of varaton. 6 Twenty Eghth Internatonal Conference on Informaton Systems, Montreal 2007

8 Brynjolfsson et al. / Battle of the Retal Channels for the unobserved heterogenety wth ~ (, ), whch means s assumed to follow a log-gamma dstrbuton (Cameron and Trved 1998; Greene 2002). 9 The estmates of the negatve bnomal regresson model are presented n column 3 of Table 3. Once agan, the coeffcent assocated wth the varable NumStores s negatve and sgnfcantly dfferent from zero. Ths suggests that as the number of nearby local stores ncreases, a consumer purchases fewer tems from the drect retaler. We nterpret ths result as evdence that the local market structure can have an mpact on consumer demand at the drect retaler. Table 3: The Effect of Local Market Structure on Demand at the Drect Retaler NumStores Intercept Probt Model Negatve Bnomal ** ** (0.002) (0.003) ** ** (0.006) (0.010) Log Lkelhood -84, , Sample Sze 163, ,933 Standard errors are lsted n parentheses. ** Sgnfcantly dfferent from zero, p<0.01 * Sgnfcantly dfferent from zero, p< Controllng for Demographc and Socoeconomc Varables, Urban versus Rural Effect, and Hstorcal Purchasng A customer s demographc and socoeconomc varables such as her ncome, age, educaton, and gender can nfluence that customer s demand. A concern regardng the results n Table 3 may be that the varable of the number of local stores embodes dfferences n these demographc and socoeconomc varables, and n turn, these varables effect on the customer s demand. We wll address ths ssue by dong the followng. We wll collect these demographc and socoeconomc varables from U.S. Census 2000 at the zp code-level, and use these varables as control varables when we estmate the Probt model and negatve bnomal regresson model. Accordngly, we nclude n our analyss the medan household ncome n natural log, percentage of female populaton, percentage of populaton wth at least a Bachelor s degree, and medan age of the female populaton, all at the level of the customer s home zp code. In addton, whether a customer lves n an urban area or a rural area may have an nfluence on that customer s demand (Glaeser et al. 2001). In order to deal wth the ssue of whether the varable of the number of local stores represents ths urban customer versus rural customer effect, we wll add the populaton densty (populaton per square mle/10,000), at the level of the customer s home zp code, as a control varable. 10 Column 2 and column 3 of Table 4 present the results obtaned from a Probt model and a negatve bnomal regresson model, after controllng for demographc and socoeconomc varables, urban versus rural effect. The results n column 2 and column 3 of Table 4 ndcate that ncome and age do nfluence consumer demand, separately from the number of local stores. But other control varables are not sgnfcant. More mportantly, the effect of the number of local stores s stll negatve and sgnfcant. We nterpret ths as evdence that customers wth more brckand-mortar stores nearby have a lower demand at the drect retaler, and ths competton effect of local stores on 9 The Posson regresson model s a specal nstance of the negatve bnomal model. A specfcaton test for rejectng the Posson regresson model can be carred out by testng the hypothess = Snce U.S. Census demographc and socal-economc varables are not avalable for every zp code, controllng for demographc and socal-economc varables slghtly reduces our sample sze to 143,835. Twenty Eghth Internatonal Conference on Informaton Systems, Montreal

9 Economcs and Busness Value of Informaton Systems drect retalng perssts even after controllng for demographc and socoeconomc varables and urban versus rural effect. If anythng, ths competton effect of local stores on drect retalng becomes larger n sze after addng n these control varables, as evdenced by the fact that the coeffcent on varable NumStores becomes more negatve. Ths result s hardly surprsng wthout ncludng some of the control varables such as ncome that are postvely correlated wth demand, the varable of the number of local stores can embody these postve effects on demand and become less negatve as a result of not ncludng those control varables. Once those varables are controlled for, the coeffcent on the varable of the number of local stores becomes more negatve. Table 4: The Effect of Local Market Structure on Demand, Controllng for Socoeconomc Factors and Hstorcal Purchase NumStores Recency Frequency Monetary Value Medan Income Probt Model Negatve Bnomal Probt Model Negatve Bnomal Probt Model Negatve Bnomal ** ** ** ** ** ** (0.003) (0.004) (0.002) (0.003) (0.003) (0.004) ** ** ** ** (0.004) (0.005) (0.004) (0.005) 0.169** 0.320** 0.169** 0.320** (0.005) (0.007) (0.005) (0.007) ** ** ** ** (0.005) (0.007) (0.005) (0.007) 0.095** 0.164** 0.087** 0.148** (0.015) (0.024) (0.016) (0.024) Populaton Densty (0.006) (0.010) (0.006) (0.010) Medan Age of 0.003** 0.005** 0.004** 0.006** Female (0.001) (0.001) (0.001) (0.001) Percentage wth Bachelor s (0.038) (0.057) Degree (0.037) (0.057) Percentage Female (0.192) (0.287) Populaton (0.189) (0.288) Intercept ** ** 0.174** 1.106** ** * (0.197) (0.304) (0.030) (0.042) (0.202) (0.305) Log Lkelhood Sample Sze 163, , , , , ,891 Standard errors are lsted n parentheses. ** Sgnfcantly dfferent from zero, p<0.01 * Sgnfcantly dfferent from zero, p<0.05 As expected, the ft s better for both models after ncludng all these control varables. Ths s shown by the hgher log lkelhood numbers n column 2 and column 3 of Table 4 than n Table 3. The lkelhood rato test does reject the basc specfcaton n Table 3, and we need to nclude these demographc and socoeconomc varables and urban versus rural effect. Hstorcal purchasng measures are wdely used n the drect retalng ndustry to segment customers nto loyal and non-loyal customers and to control for customer heterogenety. In the ndustry as well as n the academc lterature, Recency, Frequency, and Monetary Value measures, also known as the RFM measures, have been wdely 8 Twenty Eghth Internatonal Conference on Informaton Systems, Montreal 2007

10 Brynjolfsson et al. / Battle of the Retal Channels used to measure customers hstorcal purchasng behavor and segment them nto dfferent segments (e.g., Anderson and Smester 2004). Recency s commonly defned as the number of perods snce the last purchase; Frequency s defned as the total number of orders placed over a perod of tme; and Monetary Value s defned as the average per-tem prce a customer pad for the tems purchased over a perod of tme. We wll also use these RFM measures, n natural log, of customers hstorcal purchasng behavor n the tme perod pror to January 1, 2006 as control varables for customer heterogenety. Column 4 and column 5 of Table 4 present the results obtaned from a Probt model and a negatve bnomal regresson model, after controllng for hstorcal purchasng measures. The results n column 4 and column 5 of Table 4 ndcate that hstorcal purchasng measures can explan a lot of the varaton n demand n the current perod, and the coeffcents on RFM measures are hghly sgnfcant wth large t-statstcs. Ths s consstent wth what prevous research has found. But more mportantly for our research, the coeffcent on varable NumStores s negatve and hghly sgnfcant, even after controllng for the RFM measures whch have been wdely used by drect retalers to segment consumers and to treat dfferent consumer segments wth dfferent marketng strateges. Thus, our results suggest that ncludng the local market structure varable n these frms marketng decsons can make ther marketng strateges more effectve. Column 6 and column 7 of Table 4 present the results obtaned from a Probt model and a negatve bnomal regresson model, after controllng for demographc and socoeconomc varables, urban versus rural effect, as well as hstorcal purchasng measures. To further nterpret the effect of the local market structure on consumer demand at the drect retaler, we calculate how customers demand wll change when the number of local stores ncreases from the 25% percentle of ts sample dstrbuton to the 50% percentle, and then to the 75% percentle. The dstrbuton of the number of local stores reaches ts 25% percentle at 0, 50% percentle at 7, and 75% percentle at 30. We wll use the coeffcents reported n column 5 of Table 4 n our calculaton. Holdng all the control varables n our analyss constant, ncreasng the number of local stores from 0 store to 7 stores reduces customers demand at the drect retaler by 4.2%. Holdng everythng else equal, ncreasng the number of local stores from 7 stores to 30 stores reduces customers demand at the drect retaler by 2.7% Robustness Check Table 5: Negatve Bnomal Regresson of Demand onto Local Market Structure (coeffcent estmates) Varable StoresAbove0 StoresAboveMedan StoresAbove30 Coeffcent * (0.013) ** (0.012) ** (0.014) Standard errors are lsted n parentheses. ** Sgnfcantly dfferent from zero, p<0.01 * Sgnfcantly dfferent from zero, p<0.05 An even stronger approach to test the robustness of our results s to estmate the effect of local market structure on consumer demand usng dummy varables nstead of a contnuous measure of the number of stores. Ths approach also allows us to detect the exstence of non-monotoncty f ths effect s ndeed non-monotonc. We have created a dummy varable StoreAbove0 ndcatng whether there s at least one store wthn 5 mles of the customer s home zp code, and we have replaced varable NumStores wth ths dummy varable StoreAbove0 when we estmate our negatve bnomal regresson model. The coeffcent on dummy varable StoreAbove0 s reported n row 2 of Table 5. We have omtted the coeffcents on control varables for the sake of brevty these coeffcents are smlar to the coeffcents reported n column 5 of Table 4. We then create the second dummy varable StoreAboveMedan ndcatng whether there are more than 7 (whch s the medan of the dstrbuton of the number of stores) wthn 5 mles of customer s home zp code, and the thrd dummy varable StoreAbove30 ndcatng whether there are more Twenty Eghth Internatonal Conference on Informaton Systems, Montreal

11 Economcs and Busness Value of Informaton Systems than 30 stores wthn 5 mles of customer s home zp code. The results usng these two dummy varables are reported n row 3 and row 4 of Table 5 respectvely. Once agan, the coeffcents on these dummy varables whch capture varatons n the local market structure are all negatve and hghly sgnfcant. Controllng for demographc and socoeconomc varables, urban versus rural effect, and hstorcal purchasng measure, we fnd that the demand at the drect retaler s 3.2% lower for customers who have access to at least one store wthn 5 mles than for customers who do not have access to any stores wthn 5 mles. The coeffcents on dummy varables StoresAboveMedan and StoresAbove30 can be nterpreted n smlar ways Testng for Endogenety NumStores Recency Frequency Monetary Value Medan Income Populaton Densty Medan Age of Female Percentage wth Bachelor s Degree Percentage Female Populaton Resduals Intercept Table 6: Test for Endogenety Endogenety Test Orgnal Probt Model ** ** (0.005) (0.003) ** ** (0.004) (0.004) 0.169** 0.169** (0.005) (0.005) ** ** (0.005) (0.005) 0.088** 0.087** (0.016) (0.016) (0.008) (0.006) 0.004** 0.004** (0.001) (0.001) (0.042) (0.038) (0.201) (0.192) (0.006) ** ** (0.203) (0.202) Log Lkelhood Sample Sze 163, ,891 Standard errors are lsted n parentheses. ** Sgnfcantly dfferent from zero, p<0.01 * Sgnfcantly dfferent from zero, p<0.05 Next we wll test for endogenety (or non-orthorgonalty), although prevous related research has not expressed any concerns regardng endogenety. Ths endogenety (or non-orthorgonalty) concern arses when there may exst unobservable factors that affect both the number of local stores and the customer s demand. In such a case, the 10 Twenty Eghth Internatonal Conference on Informaton Systems, Montreal 2007

12 Brynjolfsson et al. / Battle of the Retal Channels varable of the number of local stores would be correlated (non-orthogonal to) wth the error n the regresson model, whch may lead to neffcent estmates for the parameters. To address ths concern, we test for endogenety n our model by usng the total populaton n a zp code and the percentage of non-whte populaton n a zp code as the nstruments for the number of local stores. The total populaton of an area s a sgnfcant factor n determnng the number of local stores, and also the race dstrbuton s lkely to play an mportant role n predctng the local market structure (Goolsbee 2001; Sna and Waldfogel 2004). Here, total populaton and the percentage of non-whte populaton are assumed to be ndependent of the unobservable factors that may nfluence both the ndvdual customer demand as well as the number of local stores that are avalable n her area. Followng the endogenety-testng procedure outlned n Wooldrdge (2001), we frst regress NumStores onto other control varables and the two nstrumental varables the total populaton n a zp code n natural log and percentage of non-whte populaton. Subsequently, we nclude the estmated resduals from ths regresson as an explanatory varable n our orgnal Probt model. A statstcally sgnfcant coeffcent on the resduals would sgnal that NumStores s endogeneous. The estmates are presented n the second column of Table 6. The thrd column ncludes the estmates of the orgnal model to ad comparson. The estmate for the Resduals s not sgnfcantly dfferent than zero. Thus, we fal to reject the null hypothess that NumStores s exogenous Effect of Local Market Structure Vares across Dfferent Products In theory, the competton between drect retalng and brck-and-mortar retalng should also vary for dfferent products. In the clothng ndustry, the runway shows and varous fashon magaznes play a key role n settng the trend for popular desgns (Agns 1999). Subsequently, clothng retalers offer ther verson of the product that s consstent wth the fashon trend (Rants 2002). Presumably, physcal stores, wth ther more lmted shelf-space and fewer SKUs, wll focus on these popular products. As a result of popular products beng wdely avalable n local stores, the competton between a drect retaler and local stores would be ntense for popular products. On the other hand, a drect retaler s lkely to face lttle or no competton f t sells nche products that are unlkely to be avalable n local stores. Correspondngly, we emprcally analyze ths theoretcal predcton. In our sample, customers have bought 1,831 unque products through the Internet channel, and 1,310 products through the catalog channel. The unon of these two sample yelds 1,866 unque products purchased through both channels, wth an ntersecton of 1,275 products. 11 Followng the wdely used Pareto Prncple (also known as the 80/20 rule), we defne the top 20% of products that generate 80% sales as the popular products. 12 The rest of the products are defned as the nche products. Subsequently, we estmate the effect of the number of local stores on the demand for popular and nche products separately. The results are shown n Table 7. For popular products, the coeffcent assocated wth NumStores s negatve and sgnfcantly dfferent than zero. In contrast, for nche products, the coeffcent assocated wth NumStores s postve and not statstcally dfferent from zero. Thus, the mpact of the local market structure on consumer demand through drect channels s almost entrely va popular products. Meanwhle, nche products stocked by the drect retaler are vrtually mmune from cross-channel competton Effect of Local Market Structure Vares across Dfferent Channels So far we have analyzed the affect of local stores on the total customer demand realzed through both channels. In ths secton, we focus on analyzng the mpact of local market structure on each drect retalng channel ndvdually. The economc theory of competton predcts that entry n a market as a monopolst s much easer than enterng as a duopolst or an olgopolst (Trole 1988). Therefore, olgopoly theory wll predct that the results from the prevous secton wll apply to each channel ndvdually. However, as mentoned earler, one of the major dfferences between the catalog and the Internet channel s the search costs ncurred by a customer. Lower search cost on the 11 Here we do not consder dfferent colors as varetes. Ths s the most conservatve defnton of product varety, where dfferent colors and dfferent szes of an tem are consdered one unque product. If we consder an tem and a color consttutng a product, then we have 4588 unque products. Our results are robust to consderng tem-color combnaton as a varety. 12 We rank sales of all 1,866 products to dentfy the top products. The fndngs that follow are robust to usng the top 50% of products as popular products. Twenty Eghth Internatonal Conference on Informaton Systems, Montreal

13 Economcs and Busness Value of Informaton Systems Internet channel allows customers to purchase nche tems that ft ther tastes better; as a result, the product sales dstrbuton s sgnfcantly flatter on the Internet than on the catalog. As such, the avalablty of popular products at local stores, and the demand for nche tems on the Internet s lkely to moderate the mpact of local market structure on the Internet channel. We hypothesze that the Internet demand wll be less affected by the local market structure n comparson to the catalog demand. Table 7: Negatve Bnomal Regresson of Demand onto Local Market Structure NumStores Recency Frequency Monetary Value Medan Income Populaton Densty Medan Age of Female Percentage wth Bachelor s Degree Percentage Female Populaton Intercept Popular Nche ** (0.004) (0.006) ** ** (0.005) (0.007) 0.324** 0.509** (0.008) (0.011) ** ** (0.008) (0.012) 0.178** (0.025) (0.037) * (0.011) (0.013) 0.007** (0.001) (0.002) (0.060) (0.088) (0.301) (0.438) ** (0.320) (0.467) Log Lkelhood Sample Sze 163, ,891 Standard errors are lsted n parentheses. ** Sgnfcantly dfferent from zero, p<0.01 * Sgnfcantly dfferent from zero, p<0.05 To examne ths hypothess we analyze the demand through the catalog and the Internet channel separately. In partcular, we estmate the negatve bnomal regresson model for the number of tems sold through the catalog and nternet respectvely. The second and thrd columns n Table 8 present the results for catalog channel and the Internet channel respectvely. The results show that local market coeffcent s sgnfcantly more negatve for the catalog channel compared to that for the Internet channel. To ad nterpretaton, ceters parbus, consder ncreasng the number of local stores for a customer from 0 to 7. Such an ncrease n local market wll reduce demand for the customer about 5.0% on the catalog channel whereas the demand would be reduced about 2.5% on the Internet channel. Ths fndng ndcates that the demand on the catalog channel decreases wth an ncrease n local stores, whereas the Internet demand s not as affected by the ncrease n local stores. 12 Twenty Eghth Internatonal Conference on Informaton Systems, Montreal 2007

14 Brynjolfsson et al. / Battle of the Retal Channels Table 8: Negatve Bnomal Regresson of Demand onto Local Market Structure for Each Channel NumStores Recency Frequency Monetary Value Medan Income Populaton Densty Medan Age of Female Percentage wth Bachelor s Degree Percentage Female Populaton Intercept Catalog Internet ** * (0.005) (0.006) ** ** (0.007) (0.007) 0.225** 0.375** (0.009) (0.011) ** ** (0.009) (0.011) 0.074* 0.253** (0.031) (0.034) * (0.012) (0.016) 0.013** ** (0.002) (0.002) ** 0.296** (0.075) (0.081) (0.374) (0.414) * ** (0.397) (0.440) Log Lkelhood Sample Sze 163, ,891 Standard errors are lsted n parentheses. ** Sgnfcantly dfferent from zero, p<0.01 * Sgnfcantly dfferent from zero, p<0.05 Although ths confrms our hypothess that the Internet channel s less affected by the local market, we cannot necessarly attrbute ths fndng to the domnance of nche product sales on the Internet. Hence, we next focus on the relatonshp between the local market structure and the demand for dfferent products. As mentoned earler, customers have bought 1,831 dfferent products through the Internet channel, and 1,310 products through the Catalog channel. The unon of these two sample yelds 1,866 unque products purchased through both channels, wth an ntersecton of 1275 products. Clearly, the Internet channel sells more varety compare to the varety sold on the catalog channel. 13 Not surprsngly, we fnd that the top 20% of products generate 84.2% of sales for the catalog channel. In contrast, the top 20% of products generate just 75.6% of sales for the Internet channel. Once agan, we use negatve bnomal regresson to analyze the effect on the demand for each type of products. The estmates of the mpact of local market structure on the demand for popular tems and demand for nche tems on the 13 In studyng the dfference n sales dstrbutons, we fnd that the slope for the Internet channel s sgnfcantly lower than that of the catalog channel, ndcatng that the sales dstrbuton for the Internet channel s sgnfcantly flatter than that for the catalog channel. See Brynjolfsson et al for further detals on usng ths approach to compare two sales dstrbutons. Twenty Eghth Internatonal Conference on Informaton Systems, Montreal

15 Economcs and Busness Value of Informaton Systems catalog channel are reported n column 2 and column 3 of Table 9 respectvely. In column 4 and column 5 of Table 9, we report the fndngs for the Internet channel, replcatng the same analyss. Table 9: Negatve Bnomal Regresson of Demand onto Local Market Structure for Dfferent Channels NumStores Recency Frequency Monetary Value Medan Income Populaton Densty Medan Age of Female Percentage wth Bachelor s Degree Percentage Female Populaton Intercept Catalog Channel Internet Channel Popular Nche Popular Nche ** ** (0.006) (0.009) (0.006) (0.009) ** ** ** ** (0.007) (0.011) (0.008) (0.010) 0.232** 0.399** 0.387** 0.566** (0.010) (0.016) (0.011) (0.015) ** ** ** ** (0.010) (0.017) (0.012) (0.017) 0.104** ** 0.237** (0.032) (0.051) (0.036) (0.051) ** * (0.013) (0.018) (0.017) (0.019) 0.014** * ** (0.002) (0.003) (0.002) (0.003) ** * 0.346** (0.079) (0.126) (0.085) (0.120) * (0.394) (0.626) (0.434) (0.588) ** ** ** (0.419) (0.659) (0.463) (0.640) Log Lkelhood Sample Sze 163, , , ,891 Standard errors are lsted n parentheses. ** Sgnfcantly dfferent from zero, p<0.01 * Sgnfcantly dfferent from zero, p<0.05 When consderng the demand for popular products, the coeffcent for the local market structure s negatve and hghly sgnfcant for both the catalog channel and the Internet channel. Ths ndcates that the demand for popular products suffer from an ncrease n local stores. As expected, ths s consstent wth the fact that local retalers stock mostly popular products, and therefore mpacts the demand for such tems. Interestngly, when consderng the mpact on demand for nche products, the coeffcent on NumStores s statstcally nsgnfcant for both the catalog channel and the Internet channel. Ths suggests that customers use drect retaler for tems that are hard to fnd or unavalable at local stores (nche products), a fndng consstent wth the prevous studes. As noted earler, nche products account for proportonally more sales on the Internet channel (24%) than t does on the catalog channel (16%), a dfference stemmng from the search cost dfference between these two channels. Also, the demand for nche products s postvely mpacted by the local market structure, ndcatng customers wth more stores are more lkely to use the drect retaler for nche products. Therefore, although customers use the 14 Twenty Eghth Internatonal Conference on Informaton Systems, Montreal 2007

16 Brynjolfsson et al. / Battle of the Retal Channels Internet channel for popular products, snce they use t relatvely more for nche products, the mpact of local market structure s neglgble on the overall demand for the Internet channel. 5. Concluson In ths study, we develop nsghts from analyzng a unque combnaton of datasets on consumer purchases and ther local market structures. Our focus s on the competton between local stores and drect retalng channels. In partcular, after controllng for consumer dfferences, we examne whether consumers wth few local stores shop more from a drect retaler than do consumers wth multple local retalng optons. In addton, we dentfy the role of demand for popular products and demand for nche products n shapng the mpact of local stores on the catalog channel and the Internet channel Internet sales nche products, whch are often unavalable n physcal stores, are largely mmune from competton by tradtonal retalers. We present sgnfcant evdence that as the number of local stores ncreases a consumer s demand at the drect retaler decreases, a fndng consstent wth the predctons of basc economc theory. We also show that the competton between local stores and the drect retaler s prmarly geared toward popular products. In case of nche products, we fnd strong evdence that a customer prefers the drect retaler, supportng the conventonal noton that local retalers prmarly stock popular tems. More mportantly, we demonstrate that customers buy relatvely more nche products through the Internet channel than through the catalog channel, an outcome ensung from the low search costs on the Internet. Ths relatvely hgher demand for nche products n turn mtgates the competton of the Internet wth local stores. Ths provdes substantve ndcaton of wde product selecton effect n onlne and offlne substtuton. In general, our fndngs suggest that busnesses must consder geography n strategcally targetng customers. For nstance, onlne retalers should emphasze greater product selectons to consumers wth many tradtonal stores nearby. Thus, marketng communcatons must accommodate customers geographc heterogenety. Twenty Eghth Internatonal Conference on Informaton Systems, Montreal