Understanding the Impact of Marketing Actions in Traditional Channels on the Internet: Evidence from a Large Scale Field Experiment

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1 A research and educaton ntatve at the MT Sloan School of Management Understandng the mpact of Marketng Actons n Tradtonal Channels on the nternet: Evdence from a Large Scale Feld Experment Paper 216 Erc Anderson Erk Brynolfsson Yu (Jeffrey Hu Duncan Smester January 2005 For more nformaton, please vst our webste at or contact the Center drectly at or

2 Understandng the mpact of Marketng Actons n Tradtonal Channels on the nternet: Evdence from a Large Scale Feld Experment Erc Anderson Kellogg School of Management, Northwestern Unversty Erk Brynolfsson Sloan School of Management, MT Yu (Jeffrey Hu Sloan School of Management, MT Duncan Smester Sloan School of Management, MT ABSTRACT Ths paper studes the conflcts and complementartes between tradtonal marketng channels, such as a catalog, and the nternet channel. Two effects are modeled: a potental conflct due to substtuton between channels, and a potental complementarty, due to ncreased overall awareness of the company s products. A large-scale feld experment was conducted wth a retaler of apparel and outdoor gear that has both a catalog channel and an nternet channel to formally test these hypotheses. A sample of 9,824 of the company s Best customers was selected together wth a separate sample of 9,683 of the company s Good customers. Customers from each of these samples were then randomly assgned to equal-szed Test and Control groups, yeldng a total of four groups. We fnd that marketng actons n the catalog channel do ndeed have a sgnfcant mpact on the demand va the nternet. nterestngly, sendng addtonal catalogs to Best customers tends to crowd out sales on the nternet, whle sendng catalogs to Good customers tends to ncrease nternet sales. Furthermore, sendng addtonal catalogs to Good customers who are also pror nternet users led to a bg ncrease n demand through the nternet and a smaller ncrease through the catalog channel. On the other hand, sendng addtonal catalogs to Best customers who are pror nternet users caused such a large shft n demand from the nternet to the catalog channel that the net effect on overall demand was actually negatve. Part of the explanaton for ths effect s that nternet orders are larger n sze and more frequently have tems from dfferent categores. We nterpret ths as evdence that the nternet channel, because of ts user-frendly searchng, browsng and recommendaton capabltes, may lead to more mpulse purchasng compared wth the catalog channel. Our results suggest that retalers who gnore cross channel conflcts and complements wll send too many catalogs to ther best customers, too few to ther good customers, and mss opportuntes to drve customers to the nternet channel. Acknowledgements: We thank partcpants at the 2004 Workshop on nformaton Systems and Economcs (WSE for valuable comments on ths paper. Generous fnancal support was provded by the Center for ebusness at MT under a grant from Amazon.com.

3 1. ntroducton Today most frms use the nternet channel, n addton to ther tradtonal marketng channels such as physcal stores and catalogs, to reach ther customers. 1 As the proporton of revenue brought n by the nternet channel grows, frms ncreasngly worry about the nteracton between the nternet channel and tradtonal marketng channels. How wll marketng actons n tradtonal channels spllover to the nternet channel, and vce versa? Wll marketng plans that are proftable n the absence of the nternet channel stll make sense n the presence of the nternet channel? Do marketng actons n one channel ncrease overall sales, or smply lead to substtutons from other channels? Answers to these questons have mportant economc consequences and equally mportant manageral mplcatons. Understandng the cross-channel mpact of marketng actons s especally mportant for the drect marketng ndustry. The drect marketng ndustry has a long-standng tradton of conductng repeated splt-sample tests n whch randomly selected samples of customers are exposed to dfferent marketng actons. n the past customers have responded ether by mal or telephone, whch has allowed the frm to track whch condtons a certan customer was exposed to. The nternet now offers a new channel through whch customers can place orders. n practce, frms have found t much harder to match customers wth marketng actons these customers are exposed to, n the presence of the nternet channel. Ths ssue has undermned the marketng plans of many frms. For example, the reducton n response rates over the tradtonal mal and telephone orderng channels led some frms to conclude that the return on ther nvestment n malng catalogs no longer ustfes the cost of the catalogs. However, when these frms reduced ther malng rates they sometmes saw drops n demand on ther nternet channel, a potental consequence that they had not antcpated when desgnng ther malng polces. n dscussons 1 Aberdeen Group surveyed more than 500 retalng and busness-to-busness frms and reported that 84.1% of these frms have an nternet channel n addton to tradtonal channels. (Aberdeen

4 wth a wde range of drect-marketng frms, managers at these frms have dentfed ths ssue of understandng cross-channel effects as one of the most mportant problems facng the retal ndustry. Surprsngly there has been very lttle work documentng how marketng actons n one channel affect demand n other channels. n ths paper we report the fndngs from a large-scale feld experment that nvestgates ths ssue. The experment was conducted wth a retaler of apparel and outdoor gear that has both a catalog channel and an nternet channel. A sample of 9,824 of the company s Best customers was selected together wth a separate sample of 9,683 of the company s Good customers. 2 Customers from each of these samples were then randomly assgned to equal-szed Test and Control groups, yeldng a total of four groups: Test and Control for both Best and Good customers. Customers n the two Test groups were maled 17 catalogs over a nne-month perod. Customers n the two Control groups receved ust 12 catalogs over ths perod. We then separately measured the demand from each group through the catalog (mal and telephone and nternet channels. Sendng addtonal catalogs can potentally have two effects on demand. Frst, t can be thought of as ncreasng advertsng and knowledge of the company s products, and thus causes an ncrease n overall demand. Second, t may smply shft demand from the nternet to the catalog channel. Whch effect wll be mportant n practce? Our fndngs confrm the exstence of both effects. When dfferent subsamples are studed, an nterestng dchotomy s revealed. Among the Good customers, malng addtonal catalogs yelded a sgnfcant ncrease n demand across both channels. Apparently, when these customers were remnded more frequently about company s products, they ordered more not only va the catalogs, but also va the nternet. The postve effect on the nternet channel was large and confrms that catalogs have an mpact beyond the 2 Customers wthout at least a good pror purchase hstory, based on recency, frequency and monetary value, were not ncluded n the frm s experment. 3

5 catalog channel. Ths means that f frms only measure how catalogs affect orders through the catalog channel they wll tend to under-nvest n malng to these Good customers. n contrast, amongst the Best customers, malng addtonal catalogs dd not ncrease demand over the nternet. n fact, there was a sgnfcant reducton n the number of tems ordered over the nternet amongst the Best customers n the Test group, compared to those n the Control group. We nterpreted ths fndng as evdence of substtuton: the Best customers had lttle scope for ncreasng ther demand and nstead ust shfted ther demand from the nternet to the catalog channel. f frms only measure how catalogs affect orders through the catalog channel they wll tend to over-nvest n malng to these Best customers. We could also examne nternet-ntensve customer more ntensvely. Customers who had ordered from the nternet before the experment made a large proporton of ther purchases through the nternet channel after the start of the experment. Cross-channel effects are more sgnfcant for these pror nternet users than for the general populaton of customers. Sendng addtonal catalogs to Good customers who are pror nternet users led to a bg ncrease n demand through the nternet and a smaller ncrease through the catalog channel. On the other hand, sendng addtonal catalogs to Best customers who are pror nternet users caused a shft n demand from the nternet to the catalog channel. Surprsngly, the net effect on overall demand was actually negatve. To explore whether a swtch of purchases from the nternet channel to the catalog channel ncreases a frm s revenue, we study the dfference between the nternet orders and catalog orders and fnd that nternet orders are larger n sze and more frequently have tems from dfferent categores. We nterpret ths as evdence that the nternet channel, because of ts userfrendly searchng, browsng and recommendaton capabltes, may lead to more mpulse 4

6 purchasng compared wth the catalog channel. Ths can help explan why t s so costly when a frm s customer shfts from the nternet channel to the catalog. The remander of the paper proceeds as follows. n Secton 2, we provde a bref revew of the lterature related wth cross-channel effects. n Secton 3, we provde a smple model of how marketng actons affect demand across channels and use ths model to formulate predctons on how cross-channel effects can be dfferent for dfferent segments of customer. n Secton 4, we descrbe the desgn of the feld experment. n Secton 5, we present our fndngs from the experment. The paper concludes n Secton 6 wth some broader mplcatons. 2. Lterature Revew There has been some research that uses theoretcal models to study how a mult-channel retaler that has one tradtonal channel and one nternet channel should set prces optmally. Lal and Sarvary (1999 compare the optmal prces under two scenaros: sellng through stores only and sellng through both stores and the nternet channel, and fnd that the ntroducton of an nternet channel may lead to a hgher prce. Cattan, Glland and Swamnathan (2003 study the prcng decsons of a retaler that has one tradtonal channel and one nternet channel, when the efforts a consumer has to make n order to purchase from these two channels are dfferent and the retaler s operatng costs n these two channels are dfferent. Ther computatonal results show that the strategy of holdng the prce n the tradtonal channel steady and settng the nternet prce to maxmze the total proft performs reasonably well. Huang and Swamnanthan (2003 have smlar fndngs n a settng where the tradtonal channel and the nternet channel have a certan degree of substtuton. n these papers the nternet channel and the tradtonal channel are ether assumed to be ndependent from each other, or assumed to be substtutes for each other and competng for customer demand. Our paper s not concerned wth the prcng strateges of a 5

7 mult-channel retaler. nstead, we focus how marketng actons n one channel can affect demand n another channel. t complements ths lterature by provdng emprcal evdence that these two channels can be substtutes for each other for one segment of customers whle complementng each other for another segment of customers. Ths paper uses sendng catalogs as an example of marketng actons and demonstrates how marketng actons n one channel can affect the demand through both channels. Because sendng catalogs can be thought of as advertsng, ths research s related wth the lterature on the effect of advertsng. There s a long debate n ths lterature on whether advertsng ncreases overall demand (nformatve advertsng or smply redstrbutes demand among sellers (combatve advertsng. These opposng vews of the effect of advertsng can be traced to Marshall (1919 and Chamberln (1933. Researchers have developed theoretcal models to analyze these types of advertsng, such as Stgler (1961, Mlgrom and Roberts (1986, and Becker and Murphy (1994. More recently ths debate has been renewed when researchers study ths queston emprcally usng data from tobacco advertsng. A revew can be found n Duffy (1996. nterestngly, our paper fnd evdence that supports the exstence of both effects: sendng catalogs has both an effect of ncreasng overall demand and an effect of redstrbutng demand among channels. For customers who are already satated, the man effect s the channelswtchng effect; but for customers who are not satated yet, the effect on overall demand can be sgnfcant. 3. A Model of How Marketng Actons Affect Demand n ths secton, we provde a smple model of how marketng actons affect demand across channels. Ths model helps us formulate the hypotheses that we wll test usng data from a largescale feld experment. 6

8 We assume a mult-channel frm that sells to ts customers through both a tradtonal channel (catalog and an nternet channel. The demand through a certan channel s gven as the overall demand tmes the demand share of that channel. We assume that marketng actons can affect both the overall demand and the demand share and that these effects can be dfferent for dfferent customers. More formally, we have: D = D( A, S ( A,, = 1, 2, 3,... (1 DC = D( A, (1 SC ( A,, = 1,2, 3,... (2 where D s customer s demand through the nternet channel, D s customer s demand through the catalog channel, D A, s customer s overall demand under marketng acton A, ( C S ( A, s customer s share of demand through the nternet channel, and s customer s characterstcs. The margnal effects of marketng acton A are smply the partal dervatves: D D( A, = S ( A, + D( A, S ( A,, = 1, 2, 3,... (3 D C D( A, = (1 S ( A, + D( A, S ( A,, = 1, 2, 3,... (4 where D( A, s the margnal effect of marketng acton A on the overall demand, S ( A, s the margnal effect on of marketng acton A on the share of demand through the nternet channel. We assume that marketng actons have decreasng returns,.e., t s harder to mprove the overall demand when the overall demand s hgh than when the overall demand s low. n other 7

9 D( A, words, s low for hgh D( A,. We also assume that marketng actons have larger effects on the share of demand through the nternet channel when the share of demand through the nternet channel s non-zero. Ths assumpton s based on the observaton that t s especally hard to mprove the share of nternet channel when a customer has currently a zero share of nternet channel ths customer ether has no nternet access or has a strong averson to the nternet channel. n other words, S ( A, s low for low S A,. ( n addton, we assume that sendng catalogs to customers wll have a postve effect on the D( A, overall demand,.e., > 0 and a negatve effect on the share of nternet channel,.e., S ( A, < 0. We are now ready to derve predctons on how sendng catalogs can have dfferent effects on demand for dfferent segments of customers. Customers who Have Hgh Overall Demand and Small Share of nternet Channel For customers who have already been purchasng a lot but very few of ther purchases are through the nternet channel, the margnal effect of sendng catalogs on the overall demand and the margnal effect on the share of nterne channel are both small,.e., D( A, and S ( A, are both low. By defnton, these customers have hgh D( A, and low S ( A,. Thus, the margnal effect of sendng catalogs on the demand through the nternet, as shown n equaton (3, s domnated by S ( A, D( A, whch s a small negatve effect. On the other hand, the margnal effect of sendng catalogs on the demand through the catalog 8

10 channel, as shown n equaton (4, s the sum of two small postve terms, resultng n a small postve effect. Customers who Have Low Overall Demand and Small Share of nternet Channel For customers who have not been purchasng a lot and very few of ther purchases are through the nternet channel, the margnal effect of sendng catalogs on the overall demand, D( A,, domnates the margnal effect on the share of nterne channel, S ( A,. By defnton, these customers have low D A, and low S A,. Thus, the margnal effect of sendng catalogs ( ( on the demand through the nternet, as shown n equaton (3, s domnated by D( A, S ( A, whch s a small postve effect. On the other hand, the margnal effect of sendng catalogs on the demand through the catalog channel, as shown n equaton (4, s the sum of a large postve term and a small postve term, resultng n a szable postve effect. Customers who Have Hgh Overall Demand and Large Share of nternet Channel For customers who have already been purchasng a lot and a lot of ther purchases are through the nternet channel, the margnal effect of sendng catalogs on the overall demand, D( A,, s small and the margnal effect on the share of nterne channel, S ( A,, s large. By defnton, these customers have hgh D A, and large S A,. Thus, the margnal effect ( ( of sendng catalogs on the demand through the nternet, as shown n equaton (3, s domnated by S ( A, D( A, whch s a szable negatve effect. On the other hand, the margnal 9

11 effect of sendng catalogs on the demand through the catalog channel, as shown n equaton (4, s the sum of a large postve term and a small postve term, resultng n a large postve effect. Customers who Have Low Overall Demand and Large Share of nternet Channel For customers who have not been purchasng a lot but a lot of ther purchases are through the nternet channel, the margnal effect on the share of nterne channel, S ( A, and the margnal effect of sendng catalogs on the overall demand, D( A,, are both large. By defnton, these customers have low D ( A, and large S ( A,. Thus, the margnal effect of sendng catalogs on the demand through the nternet, as shown n equaton (3, s domnated by D( A, S ( A, whch s a large postve effect. On the other hand, the margnal effect of sendng catalogs on the demand through the catalog channel, as shown n equaton (4, s the sum of two large postve terms, resultng n a large postve effect. Wth these theoretcal predctons n place, we are n poston to test them wth the large scale feld experment. 4. Desgn of the Experment n ths secton we wll descrbe the desgn of the experment and how data was collected. The experment was conducted over a nne-month perod from January 1, 2002 to September 31, 2002 wth a retaler of apparel and outdoor gear that has both a catalog channel and an nternet channel. A sample of 9,824 of the company s Best customers was selected together wth a separate sample of 9,683 of the company s Good customers. Customers from each of these samples were then randomly assgned to equal-szed Test and Control groups, yeldng a total of 10

12 four groups: Test and Control for both Best and Good customers. Customers n the two Control groups were maled 12 catalogs over ths nne-month perod. Customers n the two Test groups were maled 5 addtonal over ths perod, n addton to the same 12 catalogs that were maled to the Control groups. The company then tracked subsequent purchases made by these 19,507 customers that are n the experment, untl June 30, 2003 roughly nne months after the last test catalog was maled. From the company s past experence, t can take up to 15 weeks for customers to respond to catalog malng. Thus, n order to make sure we cover all the purchases that may have resulted from the malng of the 17 test catalogs, we wll use n our demand analyses consumers purchasng data n a eghteen-month perod from the start of malng the frst test catalog (January 1, 2002 untl nne months after the malng of the last test catalog (June 30, t s worth mentonng that consumers n the Control groups and the Test groups would, on average, get the same catalog malng treatment after the last test catalog was maled, because customers were randomly assgned to each group. The trackng of a customer s purchasng hstory s made possble by the use of a unque account number for each customer. n transacton data, there exsts a flag that ndcates whether an order s placed through the nternet channel or the catalog channel. For an order placed through the catalog channel, we can even pnpont whch catalog t s placed from. n addton, we have data of these 19,507 customers entre purchasng hstory before the experment, up to June 24, Results Random Assgnment and Comparson of Best vs. Good We frst test whether the assgnment of customers to ether the Control group or the Test group was truly random. Usng the hstorcal purchasng data of these 19,507 customers before the experment (from June 24, 1988 to December 31, 2001, we run a seres of t-tests that confrm the random assgnment of customers. n Table 1, we report the average number of days snce the 11

13 last order (Recency, the average number of prevous orders (Frequency, and the average dollar amount of prevous orders (Monetary Value for customers n each condton. These three measures are wdely accepted measures that are used n the retal ndustry to segment customers. T-tests on each of the three measures show that there are no sgnfcant dfferences between the Control group and the Test group, no matter we look at all the customers n the experment, or only the Best customers, or only the Good customers. However, the Best customers are very dfferent from the Good customers n all three measures of hstorcal purchasng, as one would expect. On average, the Best customers make almost four tmes as many purchases as the Good customers over the same tme perod; they make more frequent purchases than the Good customers; but they pay slghtly lower prces per tem than the Good customers. The company has a very complex algorthm that categorzes whether a customer s a Best customer or a Good customer based on the customer s hstorcal purchasng. But we fnd that the Recency, Frequecy, and Monetary Value measures capture most of the nformaton. Usng these three measures and a Logt model, we can predct correctly, for 87.8% of the 19,507 customers, whether a customer s a Best customer or a Good customer. Table 1: Average of Hstorcal Purchasng Measures for Customers n the Experment All Best Good Control Test Control Test Control Test Recency: Days snce last order Frequency: Number of tems Monetary Value: Average prce per tem Sample Sze Demand of All Customers 12

14 Havng confrmed the random assgnment of consumers to ether the Control group or the Test group, we next study the effect of malng addtonal catalogs on demand. Table 2 presents the average number of tems ordered by customers n the Control group and the Test group. We see that sendng addtonal catalogs to the Test group ncreases the total number of tems ordered per customer from to Ths ncrease mostly comes from the catalog channel. The effect on the number of tems ordered per customer through the nternet channel s postve, but very small and statstcally nsgnfcant. Ths result seems to suggest that marketng actons n the catalog channel have no sgnfcant mpact on the nternet channel. Table 2: Average Number of tems Ordered by Customers n the Experment Average number of tems ordered From nternet From All Control Test Dfference * catalogs Sample Sze (Note: * sgnfcantly from zero n a two-taled t-test, p<0.05, ** sgnfcantly from zero n a two-taled t-test, p<0.01 Comparng Best versus Good Customers However, ths cross-channel effect becomes sgnfcant once the total populaton s segmented nto the Best customers and the Good customers. Table 3 shows the effect of malng addtonal catalogs on the demand by the Good customers and on the demand by the Best customers respectvely. As we mentoned before, the Best customers are very dfferent from the Good customers n terms of hstorcal purchasng. Therefore, they may respond dfferently to the malng of addtonal catalogs. There ndeed exsts an nterestng dchotomy. Sendng addtonal catalogs to the Best customers leads to an ncrease n the number of tems ordered through the 13

15 catalog channel tems per customer n the Control group versus tems per customer n the Test group, and a decrease n the number of tems ordered on the nternet tems per customer n the Control group versus tems per customer n the Test group. On the other hand, sendng addtonal catalogs to the Good customers leads to an ncrease n the number of tems ordered through both the catalog channel tems n the Control group versus tems n the Test group, and the nternet channel tems n the Control group versus tems n the Test group. One nterpretaton of these results s that sendng addtonal catalogs to the Best customers does not rase ther overall demand much, because they are already satated by the products they have been purchasng. For the Best customers, the domnatng effect s the channel swtchng effect, as we observe sendng addtonal catalogs swtches these customers demand from the nternet channel to the catalog channel. However, sendng addtonal catalogs to the Good customers does rase ther overall demand. Ths domnatng postve advertsng effect splls over to the nternet channel and more than cancels out the negatve channel swtchng effect, as we observe a net ncrease of the number of tems ordered on the nternet. Average number of tems ordered From nternet From Table 3: Average Number of tems Ordered by Customers n the Experment Best Good Control Test Dfference Control Test Dfference * * catalogs Sample Sze (Note: * sgnfcantly from zero n a two-taled t-test, p<0.05, ** sgnfcantly from zero n a two-taled t-test, p<0.01 Posson Regresson 14

16 Although the dfferences n demand between the Control group and the Test group reported n Table 3 are szable, t-tests does not prove the dfferences are statstcally sgnfcant, except for the dfference n demand between the Good customers n the Control group and the Good customers n the Test group. We suspect that ths mght have resulted from the data s devaton from normalty. Among 19,507 customers n ths experment, only 58.8% of them purchased at least one tem from ether channel n the eghteen-month perod. Because there are a lot of consumers who made zero purchases, the number of tems ordered by each customer s heavly skewed toward zero and does not ft a normal dstrbuton well. t has a skewness of and a kurtoss of A skewness-kurtoss ont test reects the hypothess that the number of tems ordered by each customer s drawn from a normal dstrbuton. Ths large devaton from normalty can sgnfcantly lower the power of t-tests and lead to false statstcal nsgnfcance. n order to nvestgate whether the dfferences n demand between the Control group and the Test group are statstcally sgnfcant, we use a Posson regresson model. Ths model s a wdelyaccepted model for count data n economcs and marketng lterature. Ths model also allows us to ntroduce a customer s hstorcal purchasng measures Recency, Frequency and Monetary Value nto the regresson and control for them. More specfcally, we assume that the number of tems ordered by customer s drawn from a Posson dstrbuton wth parameter λ : λ e Pr( Y = q = λ, q = q! q 0,1, 2,... (5 ln λ = β1 ln r + β 2 ln f + β 3 ln m + γd, (6 where Y s the number of tems ordered by customer, r, f, and m are respectvely measures of customer s Recency, Frequency, and Monetary Value, and D s a dummy varable that s 15

17 one for customers who were n the Test group and receved addtonal catalogs and zero for others. Three Posson regresson models are estmated for the Best customers and for the Good customers respectvely: the frst model wth Y beng the total number of tems ordered, the second wth Y beng the number of tems ordered through the catalog channel, and the thrd wth Y beng the number of tems ordered through the nternet channel. Under ths specfcaton, the γ coeffcent measures the margnal effect of sendng addtonal catalogs on the log of expected number of tems ordered ( γ = ln λ / D, controllng for hstorcal purchasng. n other words, the estmated γ coeffcent s the percentage change n the expected number of tems ordered when addtonal catalogs were maled. Table 4 reports the estmates of γ coeffcent for Best customers and Good customers. Posson regresson results confrm the results derved by t-tests. Sendng addtonal catalogs to the Best customers leads to a 3.3% ncrease n ther demand through the catalog channel whle causng a 12.0% decrease n demand through the nternet channel. Snce the catalog channel brngs n more sales than the nternet channel, the net effect s an ncrease of 2.0% n total demand. Estmates of these coeffcents are hghly sgnfcant. Posson regresson results confrm t-test results that sendng addtonal catalogs does not rase the Best customers overall demand much, and the man effect s the channel swtchng effect. f the company only measures how catalogs affect orders made through the catalog channel, t wll gnore the negatve effect on the demand through the nternet channel and wll over-nvest n malng to these Best customers. On the contrary, sendng addtonal catalogs to the Good customers rases ther overall demand by 11.6%. Ths s exhbted n ncreases n demand through both channels: an 8.3% ncrease through the catalog channel and a 32.2% ncrease through the nternet channel. All of these 16

18 effects are hghly sgnfcant. f the company only measures how catalogs affect orders made through the catalog channel, t wll gnore the postve effect on the demand through the nternet channel and wll under-nvest n malng to these Good customers. Table 4: Margnal Effect of Sendng Addtonal Catalogs to the Log of Expected Number of tems Ordered % change n expected number of tems ordered From nternet From Best Good 0.020** 0.116** ** 0.322** 0.033** 0.083** catalogs Sample Sze (Notes: Posson regresson; * sgnfcantly from zero n a two-taled t-test, p<0.05, ** sgnfcantly from zero n a two-taled t-test, p<0.01 Number of Orders or Order Sze? t s mportant to explore further why sendng addtonal catalogs to the Best customers reduces ther demand on the nternet. Do they make fewer orders? Or do they order fewer tems per order? Sendng addtonal catalogs to the Good customers ncreases ther demand on the nternet, but one may wonder whether they make more orders or they order more tems per order. n ths secton, we try to separate the mpact of sendng addtonal catalogs on the number of tems ordered nto the effect on the number of orders and the effect on the order sze and study these two effects separately. The number of tems ordered by a customer can be wrtten as the number of orders tmes the average number of tems per order (or average order sze: Y = O * S, (7 17

19 where Y s the number of tems ordered by customer, O s the number of orders placed by customer, and s the average order sze by customer. S We frst use a Posson regresson model to study the effect on the number of orders. The model s the same as the Posson regresson model shown n equatons (5 and (6 that s used to model the number of tems ordered, except that varable Y s replaced by varable O. More specfcally, we assume that the number of orders placed by customer s drawn from a Posson dstrbuton wth parameter λ : λ e Pr( O = q = λ, q = q! q 0,1, 2,... (8 ln λ = β1 ln r + β 2 ln f + β 3 ln m + γd. (9 Table 5 reports the estmates of γ coeffcent that measures the percentage change n the expected number of orders made when addtonal catalogs were maled. Sendng addtonal catalogs to the Good customers causes ncreases n the number of orders through both channels: a 9.6% ncrease through the catalog channel and a 23.1% ncrease through the nternet channel. Sendng addtonal catalogs to the Best customers leads to a 4.1% ncrease n the number of orders through the catalog channel. However, although sendng addtonal catalogs leads to a decrease n the number of tems ordered through the nternet channel, the number of orders through the nternet channel does not decrease. Sendng addtonal catalogs leads to a 2.3% ncrease n the number of orders through the nternet channel. n order to reconcle ths ncrease n the number of orders wth the decrease n the number of tems ordered on the nternet, t must be that the average order sze on the nternet for the Best customers has decreased as a result of sendng addtonal catalogs. 18

20 Table 5: Margnal Effect of Sendng Addtonal Catalogs to the Log of Expected Number of Orders % change n expected number of orders From nternet From Best Good 0.040** 0.114** ** 0.041** 0.096** catalogs Sample Sze (Notes: Posson regresson; * sgnfcantly from zero n a two-taled t-test, p<0.05, ** sgnfcantly from zero n a two-taled t-test, p<0.01 We next study the effect of sendng addtonal catalogs on the average order sze. The average order sze s a contnuous measure, and we model t by the followng lnear regresson model: S = α + β ln + 1 ln r + β 2 ln f + β 3 m + γd ε, (10 where S s the average order sze by customer, r, f, and m are respectvely measures of customer s Recency, Frequency, and Monetary Value, D s a dummy varable that s one for customers who were n the Test group and receved addtonal catalogs and zero for others, and ε s a normally dstrbuted random error. We estmate ths model wth S beng the overall average order sze, the average order sze on the nternet, and the average order sze through the catalog channel, respectvely. Notce that for customers who dd not place any orders, we cannot calculate ther average order sze. For customers who dd not place any orders through a certan channel, we cannot calculate ther average order sze through that channel. Such customers are automatcally excluded from ths analyss. Thus the actual number of observatons for each regresson can be less than the number of customers. Table 6 reports the estmates of γ coeffcent that measures the change n the average order sze when addtonal catalogs were maled. We fnd that sendng addtonal catalogs lowers the 19

21 average order sze, both on the nternet and through the catalog channel. The effect of sendng addtonal catalogs on the average order sze s very small and statstcally sgnfcant for the Good customers. For the Best customers, the effect of sendng addtonal catalogs on the average order sze s much larger but stll statstcally nsgnfcant. Table 6: Margnal Effect of Sendng Addtonal Catalogs to the Average Order Sze Change n average order sze From nternet From Best Good catalogs Sample Sze (Notes: lnear regresson; * sgnfcantly from zero n a two-taled t-test, p<0.05, ** sgnfcantly from zero n a two-taled t-test, p<0.01 The fallng average order sze for the Best customers s evdence of ther sataton by the products they have been purchasng. After the Best customers are satated, t becomes very hard for them to fnd products that nterest them. As a result, the order sze decreases quckly as they make more orders. The Good customers have not been purchasng as many products as the Best customers have, and they are less lkely to be satated. As they make more orders, the order sze does not fall as quckly as t would for the Best customers. Order Sze on the nternet vs. through the Catalog Channel Table 6 also reveals an nterestng phenomenon: the change n the average order sze s much larger on the nternet than through the catalog channel. Sendng addtonal catalogs to the Best customers leads to a reducton of n the average order sze on the nternet, whle causng a reducton of only n the average order sze through the catalog channel. The same s true 3 The actual number of observatons for each regresson can be less than the number of customers. We only lst the number of customers here. 20

22 for the Good customers. One potental explanaton s that orders on the nternet are larger n sze, when compared wth orders through the catalog channel. Thus, the same percentage change would translate to a larger effect n order sze on the nternet than through the catalog channel. We next explore ths possblty and compare the order sze on the nternet wth the order sze through the catalog channel. t s worth mentonng that the comparson of order sze on the nternet and through the catalog channel can help us understand how a swtch of demand from the nternet channel to the catalog channel would affect a mult-channel retaler s revenue. f the order sze s larger on the nternet, then every order swtched from the nternet channel to the catalog channel causes a loss n the retaler s total revenue, and vce versa. We use data on all the orders placed by customers n the experment durng the eghteen-month perod. We run a lnear regresson of the sze of customer s -th order on measures of ths customer s hstorcal purchasng, a dummy varable for orders through the nternet channel, and a dummy varable for recevng addtonal catalogs: O = 1 r + β 2 ln f + β 3 ln m + β 4 + β 5 β ln D + ε, (11 where O s the sze of customer s -th order, r, f, and m are respectvely measures of customer s Recency, Frequency, and Monetary Value, s a dummy varable that s one f ths order s through the nternet channel and zero otherwse, D s a dummy varable that s one f customer receved addtonal catalogs and zero otherwse, and ε s a normally dstrbuted random error. An average order placed through the catalog channel by the Best customers has an order sze of 2.224, whle an average order placed on the nternet by the same customers has an order sze of n other words, an average order on the nternet s about 20.2% larger than an average 21

23 order through the catalog channel, for the Best customers. The same result holds true for the Good customers: an average order on the nternet s about 10.7% larger than an average order through the catalog channel (2.254 versus Regresson results n Table 7 confrm the trend shown by these descrptve statstcs. The coeffcent on the dummy for nternet orders s for the Best customers and for the Good customers, wth both beng hghly sgnfcant. Ths s after controllng for customers hstorcal purchasng and whether a customer receved addtonal catalogs. We nterpret the larger order sze on the nternet as mpulse purchasng. The nternet channel, because of ts userfrendly searchng, browsng, and recommendng capabltes, often leads to more mpulse purchasng compared wth the catalog channel. Ths fndng of larger order sze on the nternet than through the catalog channel has mportant mplcatons for a mult-channel retaler. t suggests that a mult-channel retaler should drve ts customers toward the nternet channel by engagng n efforts such as desgnng catalogs that promote ts nternet channel, provdng onlne tools for customers to easly locate tems that appear n catalogs, and offerng nternet promotons. Table 7: Comparson of Order Sze on the nternet and through the Catalog Channel All Best Good ln(recency 0.047** (0.015 (0.018 ln(frequency 0.530** 0.347** (0.020 (0.024 ln(monetary 0.434** 0.144* Value (0.067 (0.065 Dummy for 0.444** 0.170** nternet orders (0.051 (0.065 Dummy for Recevng Addtonal Catalogs

24 (0.027 (0.043 Constant ** * (0.318 (0.319 R-squared Sample Sze (Notes: lnear regresson; standard errors n parentheses; * sgnfcantly from zero n a two-taled t-test, p<0.05, ** sgnfcantly from zero n a two-taled t-test, p<0.01 To explore ths mpulse purchasng ssue further, we study whether nternet orders more frequently combne tems from dfferent categores, compared wth orders through the catalog channel. We regress the number of categores spanned by tems n customer s -th order (varable C on measures of ths customer s hstorcal purchasng and a dummy varable for nternet orders, and a dummy varable for recevng addtonal catalogs. The model s the same as the lnear regresson model shown n equaton (11, except that varable O s replaced by varable C. More specfcally, we have: C β 1 ln r + β 2 ln f + β 3 ln m + β 4 + β 5 = D. (12 An average order placed through the catalog channel by the Best customers spans categores, whle an average order placed on the nternet by the same customers spans categores. The same result holds true for the Good customers: an average order placed through the catalog channel spans categores, whle an average order on the nternet spans categores. Most orders have only tems n one product category. But orders on the nternet more frequently combne tems from multple categores, compared wth orders through the catalog channel. For the Best customers, the percentage of orders that have tems multple categores s 6.6% on the nternet and 4.9% through the catalog channel. For the Good customers, the percentage s 8.3% on the nternet versus 5.4% through the catalog channel. Regresson results n Table 8 confrm the trend shown n these descrptve statstcs. The coeffcent on the dummy for nternet orders s for the Best customers and for the 23

25 Good customers, wth both beng hghly sgnfcant. Ths s after controllng for customers hstorcal purchasng and whether addtonal catalogs were maled. Table 8: Comparson of Number of Categores Spanned by Orders on the nternet and through the Catalog Channel All Best Good ln(recency ** (0.002 (0.002 ln(frequency 0.033** 0.027** (0.002 (0.003 ln(monetary ** Value (0.007 (0.009 Dummy for 0.021** 0.024** nternet orders (0.006 (0.009 Dummy for Recevng Addtonal Catalogs (0.003 (0.006 Constant 1.181** 1.099** (0.036 (0.042 R-squared Sample Sze (Notes: lnear regresson; standard errors n parentheses; * sgnfcantly from zero n a two-taled t-test, p<0.05, ** sgnfcantly from zero n a two-taled t-test, p<0.01 Pror nternet Users Customers who had ordered through the nternet channel before the start of the experment on average made 47.4% of ther orders on the nternet n the eghteen months after the start of the experment. Ths number drops to 8.1% for customers who had not ordered on the nternet. Because a lot of these pror nternet users prefer orderng on the nternet to orderng through the catalog channel, cross-channel effects may be dfferent for these customers than for the general populaton of customers. 24

26 Table 9 shows the effect of malng addtonal catalogs on the demand by these pror nternet users. Sendng addtonal catalogs to the Best customers who are also pror nternet users leads to an ncrease n the number of tems ordered through the catalog channel tems per customer n the Control group versus tems per customer n the Test group, and a decrease n the number of tems ordered on the nternet tems per customer n the Control group versus tems per customer n the Test group. The decrease n the demand on the nternet s much bgger than the ncrease n the demand through the catalog channel, and the net effect s negatve. On the other hand, sendng addtonal catalogs to the Good customers who are also pror nternet users causes an ncrease n the number of tems ordered through both the catalog channel tems n the Control group versus tems n the Test group, and the nternet channel tems n the Control group versus tems n the Test group. Table 9: Average Number of tems Ordered by Customers n the Experment who Are Pror nternet Users Total number of tems ordered per customer From nternet From Best Good Control Test Dfference Control Test Dfference * * * catalogs Sample Sze (Note: * sgnfcantly from zero n a two-taled t-test, p<0.05, ** sgnfcantly from zero n a two-taled t-test, p<0.01 We re-estmate the Posson regresson model descrbed n equaton (5 and (6, usng data on pror nternet users only. Results are shown n Table 10. These results confrm the results derved by t-tests. Sendng addtonal catalogs to the Best customers who are also pror nternet users leads to a 5.7% ncrease n ther demand through the catalog channel whle causng a 29.3% decrease n demand on the nternet. The net effect s a decrease of 11.5% n total demand. f the 25

27 company only measures how catalogs affect orders made through the catalog channel, t wll gnore the large negatve effect on the demand through the nternet channel and wll make the wrong decson on whether to send addtonal catalogs to these customers. On the contrary, sendng addtonal catalogs to the Good customers who are also pror nternet uses rases ther overall demand by 32.8%. Ths s exhbted n ncreases n demand through both channels: a 13.8% ncrease through the catalog channel and a 49.0% ncrease through the nternet channel. f the company only measures how catalogs affect orders made through the catalog channel, t wll gnore the bgger postve effect on the demand through the nternet channel and wll heavly under-nvest n malng to these customers. Table 10: Margnal Effect of Sendng Addtonal Catalogs to the Log of Expected Number of tems Ordered for Pror nternet Users Total number of tems ordered per customer From nternet From Best Good ** 0.328** ** 0.490** * catalogs Sample Sze (Notes: Posson regresson; * sgnfcantly from zero n a two-taled t-test, p<0.05, ** sgnfcantly from zero n a two-taled t-test, p< Concluson n ths paper we study the mpact of marketng actons n tradtonal channels on the nternet. Usng data collected from a large-scale feld experment that s conducted by a retaler that has both an nternet channel and a catalog channel, we demonstrate that marketng actons n tradtonal channels can have a sgnfcant mpact on the nternet. Our results also show that the overall cross-channel effect can be dfferent n magntude as well as sgns for dfferent segments of customers. Key parameters that can be used to segment a customer base n ths context nclude 26

28 measures of customers past purchasng hstory as well as ther famlarty wth the nternet channel. Because of ths cross-channel effect, marketng plans that are proftable n the absence of the nternet channel may not make sense n the presence of the nternet channel. Usng sendng addtonal catalogs as an example of marketng actons, we fnd that sendng addtonal catalogs can potentally have two effects on demand. t can cause an ncrease n overall demand, and t may also shft demand from the nternet to the catalog channel. Our fndngs confrm the exstence of both effects and reveal an nterestng dchotomy. Amongst the Good customers malng addtonal catalogs yelded a sgnfcant ncrease n demand across both channels. The effect on the nternet channel was large and confrms that catalogs have an mpact beyond the catalog channel. f frms only measure how catalogs affect orders through the catalog channel they wll tend to under-nvest n malng to these Good customers. n contrast, amongst the Best customers, malng addtonal catalogs dd not ncrease demand over the nternet. Rather, the man effect s a shft of demand from the nternet to the catalog channel. f frms only measure how catalogs affect orders through the catalog channel they wll tend to over-nvest n malng to these Best customers. n order to make the rght marketng decson, frms cannot solate channels from one another and assume the non-exstence of cross-channel effect. nstead, the rght marketng plan requres sharng of data across marketng channels and ntegratng customer data that are scattered across organzatons. We also fnd that the nternet channel, because of ts user-frendly searchng, browsng, and recommendng capabltes, often leads to more mpulse purchasng compared wth tradtonal channels. We provde evdence supportng the theory of ncreased mpulse purchasng on the nternet, ncludng the larger order sze on the nternet compared wth through the catalog channel, and the hgher frequency of nternet order combnng tems from multple categores 27

29 compared wth orders through the catalog channel. These results suggest that a mult-channel retaler should drve ts customers toward the nternet channel by engagng n efforts such as desgnng catalogs that promote ts nternet channel, provdng onlne tools for customers to easly locate tems that appear n catalogs, and offerng nternet promotons. 28

30 Bblography Ackerberg, D Emprcally dstngushng nformatve and prestge effects of advertsng. RAND Journal of Economcs, 32(2: Becker, G. S. and K. M. Murphy A smple theory of advertsng as a good or bad. Quarterly Journal of Economcs, 108 (4: Brynolfsson, E., Y. Hu, and M. D. Smth Consumer surplus n the dgtal economy: Estmatng the value of ncreased product varety at onlne booksellers. Management Scence, 49(11: Cattan, K. D., W. G. Glland, and J. M. Swamnathan Addng a drect channel? How autonomy of the drect channel affects prces and profts. Workng Paper. The Kenan- Flagler Busness School, Unversty of North Carolna, Chapel Hll, NC. Chamberln, E The Theory of Monopolstc Competton. Harvard Unversty Press. Cambrdge, MA. Duffy, M Econometrc studes of advertsng, advertsng restrctons and cgarette demand: A survey. nternatonal Journal of Advertsng, 15: Huang, W. and J. M. Swamnathan Prcng on tradtonal and nternet channels under monopoly and duopoly: Analyss and bounds. Workng Paper. The Kenan-Flagler Busness School, Unversty of North Carolna, Chapel Hll, NC. Lal, R. and M. Sarvary When and how s the nternet lkely to decrease prce competton. Marketng Scence, 18(4: Marshall, A ndustry and Trade. MacMllan and Co. London, U.K. Mlgrom, P. and J. Roberts Prce and advertsng sgnals of product qualty. Journal of Poltcal Economy, 94(4: Stgler, G. L The economcs of nformaton. Journal of Poltcal Economy, 71(:

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