WISE 2004 Extended Abstract

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1 WISE 2004 Extended Abstract Does the Internet Complement Other Marketng Channels? Evdence from a Large Scale Feld Experment Erc Anderson Kellogg School of Management, Northwestern Unversty Erk Brynjolfsson Sloan School of Management, MIT Yu (Jeffrey) Hu Sloan School of Management, MIT Duncan Smester Sloan School of Management, MIT Introducton Busness executves understand that the Internet cannot be treated as smply a stand-alone channel. They ncreasngly worry about channel conflct between the Internet and other marketng channels. How wll ther marketng efforts n other channels spllover to the Internet channel, and vce versa? Wll marketng plans that are proftable n the absence of the Internet channel stll make sense n the presence of the Internet channel? Do marketng efforts n one channel ncrease sales overall, or smply lead to substtuton from other channels? The answer to these questons has mportant economc consequences and equally mportant manageral mplcatons. However, emprcal studes on these topcs are scarce. We am to shed some lght on these questons through the analyses of the data of an experment at a retalng company that has both a catalog channel and an Internet channel. We demonstrate that marketng efforts n one channel can spll over and also ncrease sales n another channel. But ths spllover effect can vary for dfferent segments of a frm s base. Key parameters that can be used to segment a frm s base n ths context of channel nteracton nclude measures of s past purchasng hstory as well as ther famlarty wth the Internet channel. Interestngly, our results show that the Internet channel can also act as a substtute to the exstng channel for some types of s even as t smultaneously serves as a complement to the exstng channel for other types. Furthermore, when channel conflct s severe, we fnd that ncreased marketng efforts n one channel can actually reduce the frms overall revenues by dvertng sales from more effectve channels. Because of the desgn of the feld experment and the large number of partcpants, we are able to rule out the problems of endogenety that plague much emprcal research whle stll gettng farly precse estmates of each of the effects we study. Desgn of the Experment The experment was conducted n a nne-month perod from January 1, 2002 to September 31, 2002 by a retalng company that sells ts products through both a catalog channel and an Internet channel. The 20,000 s were selected wth above average hstorcal purchasng. These s were randomly assgned to one of two groups: a Control group that would receve twelve women s clothng catalogs durng ths nne-month perod, and a Test group that would receve fve more women s clothng catalogs, n addton to the same twelve catalogs receved by the Control group. The company then tracked the entre purchasng hstory of each of the 20,000 s, for the nne-month perod as well as the nne-month perod after the experment. 1 The company also provded us wth data of the 20,000 s entre purchasng hstory before the experment, up to January 1, Results 1 The Control group and the Test group would, on average, get the same catalog malng treatment n the nne months after the experment, because s were randomly assgned to each group. 2 The trackng of a s purchasng hstory s made possble by the use of unque account number for each. We have data of whether an order s placed through the Internet channel or the catalog channel. For an order made through the catalog channel, we can even pnpont whch catalog t s made from.

2 We frst test whether the assgnment of s to ether the Control group or the Test group s truly random. Usng the hstorcal purchasng data of these 20,000 s from January 1, 1996 to December 31, 2001, we run a seres of t-tests that confrm the random assgnment of s. 3 From dscussons wth ndustry experts, we know that t may take several months for the effect of a catalog malng to completely dsappear. Therefore, we use transacton data from the start of the experment to nne months after the last addtonal catalog was sent to s n the Test group to calculate the average number of unts ordered by each. Sendng addtonal catalogs can be thought of as ncreasng advertsng. However, there s a long-standng debate n the lterature about the effects of such advertsng on varous sales channels. 4 On one hand, sendng out catalogs should make s more aware of the vendors products and ncrease sales n all channels, ncludng the nternet channel. On the other hand, sendng the catalog could lead s to smply swtch ther purchases from the Internet channel to the catalog channel. The result of t-tests of the average number of unts ordered by s n Control group and Test group s presented n Table 1. We see that sendng addtonal catalogs to the Test group ncreases the average unts ordered per, but ths ncrease mostly comes from the catalog channel. Sendng addtonal catalogs does not sgnfcantly change the number of unts ordered per through the Internet channel. Ths seems to suggest that the Internet channel and the catalog are ndependent from each other. However, an mplcaton of the theory s that s who are already well-aware of the frm s products or who are relatvely satated wll respond to addtonal malngs prmarly by substtutng purchases among channels, rather than ncreasng overall purchases. Conversely, s who are less-aware of the frm s offerng or who are not satated are more lkely to ncrease ther total purchases. Accordngly, to explore ths mplcaton we segment s nto a Best group and a Good group by ther hstorcal purchasng from January 1, 1996 to just before the start of the experment. These two groups are sgnfcantly dfferent from each other n term of ther past purchasng. 5 Thus, we expect they respond dfferently to recevng addtonal catalogs. The result of t-tests of the average number of unts ordered by s for each of the two groups s presented n Table 2. Table 2 shows that sendng addtonal catalogs to Best s leads to an ncrease n unts ordered through the catalog channel and a small decrease n unts sold through the Internet channel. There exsts slght substtutablty between these two channels. On the other hand, sendng addtonal catalogs to Good s leads to an ncrease n unts ordered through both the catalog channel and the Internet channel. For ths segment, these two channels exhbt complementarty. One nterpretaton of these results s that sendng addtonal catalogs to Best s does not rase ther overall demand, because they are already satated by the products they have been purchasng. As a result, the domnatng effect for Best s s the channel swtchng effect, as we observe sendng addtonal catalogs swtches them from the catalog channel to the Internet channel. However, sendng catalogs to Good s does rase ther overall demand. Ths domnatng postve advertsng effect splls over to the Internet channel and more than cancels out the negatve channel swtchng effect, as we observe a net ncrease of unts ordered through the Internet channel. To further explore ths hypothess, we focus more narrowly on s who have ordered from the Internet channel before the experment. A summary of the average number of unts ordered by these so-called pror Internet users s presented n Table 3. Sendng addtonal catalogs to Best s who are pror Internet users causes a bg decrease n number of unts ordered through the Internet channel along wth a smaller ncrease n number of unts ordered through the catalog channel. The net effect s negatve. Sendng addtonal catalogs to Good s who are pror Internet users causes bg ncreases n unts ordered through both the Internet channel and the catalog channel. Customers who have ordered from the Internet channel before the start of the experment on average make 47.0% of ther orders from the Internet channel n the eghteen months after the start of the experment. Ths number drops to 3.8% for s who have not ordered from the Internet before the start of the experment. A lot of pror Internet users prefer orderng from the Internet channel to orderng from the catalog channel. For these s one order swtched from the Internet channel may not be made up by an order through the catalog channel. Compoundng ths 3 Ths result s shown n Column 1 of Table A1 n the Appendx. 4 Lterature that argues that advertsng has roles of ncreasng total demand and redstrbutng demand among sellers can be traced to Marshall (1919) and Chamberln (1933). 5 Ths result s shown n Column 2 and 3 of Table A1 n the Appendx. 2

3 wth the larger order sze through the Internet channel than through the catalog channel, we argue that one unt lost through the Internet channel may not be made up by one unt through the catalog channel. The Internet channel, because of ts user-frendly searchng and browsng capabltes, may lead to more mpulse purchasng compared wth the catalog channel. We regress the order sze on measures of a s hstorcal purchasng and a dummy varable that s one for orders through the Internet channel and zero otherwse: O j = β ln r + β ln f + β 3 ln m + γd 1 2, where O s the number of unts ordered by on order j, r, f, and m are respectvely measures of s recency, frequency, and monetary value, and I s a dummy varable that s one for orders through the Internet channel and zero otherwse. Ths result s presented n Table 4. Take the orders made by Best s as an example. The average order sze s The average sze of an order through the Internet channel s larger than the sze of an order through the catalog channel by 0.469, whch translates to 21.2% larger. Smlar results are found on orders from other groups of s. For Best s, sendng addtonal catalogs causes an effect that s domnated by channel swtchng effect. If these Best s are also pror Internet users, we observe a decrease n number of unts ordered through the Internet channel along wth a smaller ncrease n number of unts ordered through the catalog channel. The net effect on total number of unts ordered s negatve. For Good s who are also pror Internet users though, sendng addtonal catalogs causes an effect that s domnated by advertsng effect that ncreases the demand for products. Ths effect s reflected n ncrease n unts ordered through both the Internet channel and the catalog channel. In addton to runnng t-tests, we also ftted the level purchase data to the followng Posson regresson model for each group: q Pr( Y = q) = e λ λ / q!, q = 01,, 2,... ln λ = β1 ln r + β 2 ln f + β 3 ln m + γd, where Y s the number of unts ordered by, r, f, and m are respectvely measures of s recency, frequency, and monetary value, and D s a dummy varable that s one for s who have receved addtonal catalogs n the experment and zero for others. For each group, we estmate three Posson regresson models: the frst wth Y beng the number of unts ordered, the second wth Y beng the number of unts ordered through the catalog channel, and the thrd wth Y beng the number of unts ordered through the Internet channel. Table 5 reports the estmated γ, whch s the margnal effect of sendng addtonal catalogs to the log of expected number of unts ordered per ( γ = ln λ / D ), controllng for a s hstorcal purchasng. The complentarty and substtutablty results found by t-tests n Table 2 and Table 3 become even more sgnfcant after we control a s hstorcal purchasng usng a Posson regresson model. Concluson In ths research, we study the nteracton between the Internet channel and other marketng channels through the analyses of the data of an experment conducted by a retalng company that has both a catalog channel and an Internet channel. Our results show that the Internet channel can act as a substtute to the exstng channel for some s whle beng a complement to the exstng channel for others. Because of the nteracton between these channels and because of the unque propertes of the Internet channel, marketng efforts that would have been proftable n the absence of the Internet channel may actually reduce a frm s proftablty. We demonstrate that marketng efforts n one channel can spll over to another channel. But ths spllover effect can be dfferent for dfferent segments of a frm s base. Key parameters that can be used to segment a frm s base n ths context of channel nteracton are measures of s past purchasng hstory as well as ther famlarty wth the Internet channel. Bblography 3

4 Chamberln, E The Theory of Monopolstc Competton. Harvard Unversty Press. Cambrdge, MA. Marshall, A Industry and Trade. MacMllan and Co. London, U.K. Tables unts ordered per Sample Sze Control Test Dfference * * Table 1: Average Number of Unts Ordered by Customers n the Experment (Note: * sgnfcantly from zero n a two-taled t-test, p<0.05) Best Good unts ordered per Sample Sze Control Test Dfference Control Test Dfference * * * * Table 2: Average Number of Unts Ordered by Customers n the Experment (Note: * sgnfcantly from zero n a two-taled t-test, p<0.05) Best Good unts ordered per Sample Sze Control Test Dfference Control Test Dfference * * * Table 3: Average Number of Unts Ordered by Customers who Have Ordered from the Internet Channel before the Experment (Note: * sgnfcantly from zero n a two-taled t-test, p<0.05) 4

5 All Pror Internet Users ln(recency) ln(frequency) Best Good Best Good (0.017) (0.018) (0.062) (0.065) (0.021) (0.029) (0.098) (0.098) ln(monetary Value) (0.070) (0.064) (0.290) (0.258) Dummy for Internet channel R-squared Sample Sze (0.054) (0.059) (0.124) (0.146) Table 4: Lnear Regresson of Order Sze on Measures of Customers Hstorcal Purchasng and Whether the Order s through the Internet Channel (standard errors n parentheses) All Pror Internet Users unts ordered per Sample Sze Best Good Best Good 0.048* 0.150* * * 0.341* * 0.468* 0.063* 0.122* 0.215* 0.260* Table 5: Margnal Effect of Sendng Addtonal Catalogs to the Log of Expected Number of Unts Ordered (Notes: Posson regresson; * sgnfcantly from zero n a two-taled t-test, p<0.05) 5

6 Appendx All Best Good Control Test Control Test Control Test Recency: Days snce last order Frequency: Number of unts Monetary Value: Average prce per unt n dollars Sample Sze Table A1: Average of Hstorcal Purchasng Measures for Customers n the Experment 6