The Personalization Services Firm: What to Sell, Whom to Sell to and For How Much? *

Transcription

1 The Personalzaton Servces Frm: What to Sell, Whom to Sell to and For How Much? * oseph Pancras Unversty of Connectcut School of Busness Marketng Department 00 Hllsde Road, Unt 04 Storrs, CT joseph.pancras@busness.uconn.edu Phone: Fax: K. Sudhr Yale School of Management 35 Prospect St, PO Box 0800 New Haven, CT Emal: k.sudhr@yale.edu Phone: Fax: uly, 005 * Ths paper s based on an essay from the frst author s dssertaton at New York Unversty. The authors thank oel Steckel, Yuxn Chen, Pars Cleanthous, Russell Wner, Vck Morwtz and woong Shn for feedback and helpful dscussons and the workshop partcpants at Carnege Mellon Unversty, Koc Unversty, London Busness School, New York Unversty, SUNY Buffalo, Unversty of Central Florda, Unversty of Connectcut, Unversty of Georga, Unversty of Southern Calforna, Unversty of Texas at Austn, Washngton Unversty at St. Lous and Yale Unversty for ther comments.

2 The Personalzaton Servces Frm: What to Sell, Whom to Sell to and For How Much? Abstract Personalzaton servces such as ndvdual-specfc advertsng and couponng are a growth market. Some personalzaton servce frms offer ther servces on an exclusve bass to manufacturers n a product category whle others offer t on a non-exclusve bass. Some restrct the length of purchase hstory data used for personalzaton, whle others use very long purchase hstores. Despte these dfferences, there s lttle emprcal gudance on what s the optmal busness strategy for a partcular frm. Ths paper flls ths vod by offerng an emprcal framework to help a personalzaton servces frm choose the rght strategy. It also enables the frm to dentfy new types of future compettors. We llustrate the approach n the context of a personalzed coupon vendor n grocery retalng. We fnd that personalzaton usng the maxmum avalable purchase hstory data on a non-exclusve bass s the most proftable strategy for the vendor. We also evaluate the possblty of a grocery retaler usng consumer nformaton from ts loyalty card programs to offer these personalzed coupon servces. We fnd that snce personalzaton mproves the retaler's profts due to the sale of groceres, the retaler can use ths proft ncrease to subsdze the sale of personalzed coupon servces. Therefore retalers may be the most potent compettve threat to personalzed coupon vendors n grocery retalng. Keywords: Personalzaton Servce, One-to-One Marketng, Targeted Coupons, Competton, Marketng Channels, Informaton Suppler.

3 . Introducton. The Personalzaton Servces Industry Personalzed marketng targeted at ndvdual consumers (a.k.a. one-to-one marketng) has been on the rse over the last two decades (Peppers and Rogers 997). A number of vendors now specalze n offerng personalzed communcaton and promoton servces to consumer marketers to help these frms mprove the effcency of ther advertsng and promoton dollars. Table lsts some of the major players n the personalzaton servces busness. For each of these players, we provde a bref descrpton of ther busness and report ther revenues, market captalzaton and growth rates. As can be seen from Table, the ndustry s ganng n mportance as reflected n ts market valuatons as well as revenues and growth rates. Several companes n ths ndustry have revenues n the hundreds of mllons of dollars and valuatons over a bllon dollars. **** Insert Table here**** The use of scanners n offlne retalng and the ntrnsc dgtal nature of onlne retalng have enabled the easy collecton of purchase hstory data. The fallng costs of dgtal storage and computaton have made the recordng and analyss of vast amounts of purchase hstory data for personalzaton purposes feasble. In the grocery and drugstore markets, Catalna Marketng obtans purchase hstory data through cooperatng retalers and provdes targeted coupons on behalf of both grocery manufacturers and retalers to households purchasng at that partcular retaler. Catalna Marketng has penetrated about,000 of the roughly 34,000 supermarkets n the Unted States and records about 50 mllon transactons per week, whch s then used to ad manufacturers for targetng. Such targeted marketng consderably enhance response rates and therefore enhances the effcency of the marketng programs. For example Catalna s response rates are estmated to be around 6-9% compared to the -% response rates for coupons n massmaled free standng nserts (FSI). On the Internet, companes such as DoubleClck collect past vst data from cooperatng webstes and use these to delver targeted advertsng for ts advertsng clents. In the catalog and specalty retalng ndustry, frms such as Abacus BC Allance and I- Behavor pool transactonal data from over a thousand catalog ttles/retalers to offer mproved

4 targeted drect marketng servces to ts members. Whle Abacus collects data only at the catalog level, frms such as I-Behavor collect data at the SKU level. The Abacus BC Allance has 550 catalogs/retalers who have pooled together data on over 4.4 bllon transactons from over 90 mllon households (Mller 003). I-Behavor has data on over 000 md-szed catalog companes on over 03 mllon consumers from 89 mllon households. Advances n data collecton and storage technologes wll contnue to fuel the growth and scale of personalzaton servces frms. Further, advances n promoton delvery technologes to ndvduals (n-store at the pont of purchase; at home through drect mal; onlne through emal; and even by wreless through cell-phones when on the move) ncreases the effectveness and tmelness of personalzed marketng strateges. Not surprsngly, personalzed advertsng and promotons are pervasve n a wde range of ndustres ncludng servces such as bankng, telephony, nsurance, durable goods such as autos, and the vast range of products sold n supermarkets and drugs stores. But despte ther growng economc mportance, there s very lttle emprcal research addressng ssues of concern to ths ndustry.. The Research Problem Much of the extant research on ths ndustry to-date has been of the engneerng type. The engneerng type research focuses on how frms should use data about households to better personalze the advertsng or prce promoton. Ths research has occurred n marketng, nformaton systems and computer scence. (Ansar and Mela 003, Lu and Shh 005, Adomavcus et al. 005). Researchers often poston these as approaches by whch a frm can take advantage of ts nternal databases to mprove ts marketng effectveness. Equvalently, from the pont of vew of the personalzaton servces ndustry, ths research leads to technologes that facltate creaton of the products they offer to ther markets. In contrast to such engneerng research, our goal n ths paper s to help personalzaton servces frms answer questons of a marketng nature,.e., once the technology s avalable, what features the product should have, who t should be sold to and at what prce... Dversty of Strateges n Practce: Are Current Strateges Optmal? In practce, personalzaton servces frms offer targetng servces to ther clent on both an exclusve bass as well as a non-exclusve bass. For example, Catalna dvdes a year nto four thrteen-week perods and dvdes the Unted States nto several regons n defnng the product. Wthn any partcular tme perod and regon, t offers the targetng servce on an

5 exclusve bass to manufacturers wthn a partcular product category. Catalna dvdes the market nto hundreds of fnely defned categores (currently over 500 categores). In contrast, targetng servce provders n the catalog and specalty retalng arena such as Abacus and - Behavor offer ther targetng servces on a non-exclusve bass. They sell to any catalog marketer or specalty retaler who requests ther servces. These provders also dffer n ther outlook toward ncreasng the accuracy of ther targetng servces. Catalna s offers two types of targetng servces: () Checkout Coupon, based on last purchase data and () Checkout Drect based on 65 weeks of purchase hstory data. It voluntarly does not use data beyond 65 weeks. Catalna orgnally decded on the 65 week lmt nearly two decades ago, when storage was consderably more expensve. In such an envronment, t makes sense to destroy older data, f more recent data are better predctors of consumer behavor. However, n many nfrequently purchased categores, one would expect that usng data beyond the last 65 weeks can help mprove the accuracy of targetng consderably. Further as data storage costs have fallen, t may make sense to revst the lmt on data used for targetng. The 65 week lmt s also theoretcally puzzlng because Catalna uses an exclusve clent strategy, where ncreasng accuracy should always mprove proftablty for the clent and therefore the prce that Catalna can charge for the servce. Absent the threat of downstream competton, ncreasng accuracy should be proftable unless the cost of storage relatve to the gans s prohbtvely expensve. In contrast to Catalna, a company such as Abacus contnues to expand the accuracy of ts database. Abacus pools data from over 550 catalog marketers/specalty retalers on over 90 mllon households and contnue to expand the depth of purchase nformaton about households n ts database. Abacus uses data for up to 5 years on each household n ther database. When DoubleClck purchased Abacus n 999, t sought to combne the offlne data from Abacus wth onlne transacton behavor captured by DoubleClck. DoubleClck however dd not combne ther offlne and onlne data because prvacy advocates vehemently opposed the dea and t created a publc relatons problem for Catalna. Despte the dversty n the practces of frms about Whom to sell (Should we sell exclusvely or non-exclusvely?) and What to sell ( Should we lmt the depth of the data used for targetng? ), there s lttle research to gude personalzaton servce frms on what the optmal strategy should be. Are the exstng strateges used by frms optmal? Or could they mprove by 3

6 shftng to a dfferent marketng strategy? As storage costs fall, the economcs of usng longer hstores can change. Can personalzaton servces frms beneft from ncreasng the extent of purchase hstory t uses for targetng? Should they reevaluate ther polces of offerng exclusve/non-exclusve contracts to frms n a category and allow multple frms? The tmelness of ths research s hghlghted n a recent stock analyss report about Catalna by Deutsche Bank (Gnoccho et al 005) whch states: Categores are sold on four thrteen-week cycles wth exclusvty (only one manufacturer can promote that category durng that perod). As Catalna beleves that only approxmately 0-5% of ts customers want exclusvty, they are lookng at ways to potentally sell more than one manufacturer n a category. Usng our analytcal approach, Catalna wll have an emprcal bass to answer ths crtcal busness ssue that they currently face... The What to Sell, Whom to Sell to and For How Much Questons To fx deas and to facltate emprcal work, we llustrate the research problem that we address n the context of Catalna, a frm whch sells personalzed coupon servces to grocery manufacturers usng purchase hstory data of households from cooperatng retalers. Consder the followng smple queston facng Catalna s management: What prce should Catalna charge for ts servce from a grocery manufacturer such as Henz for ssung targeted coupons on ts behalf n the ketchup category to households? Naturally, the prce should depend on the economc value (.e., the ncremental profts), that Henz would obtan from usng the targetng servce. What would that economc value be? For most standard products and servces, the economc value of a product to a customer s ndependent of who else uses t. But for targetng servces, the economc value of the servce to Henz would depend on whether Henz alone uses the servce or whether ts compettor Hunt s also uses the servce at the same tme, because the effectveness of targetng s a functon of whether one s compettor also targets. If the economc value to Henz (and therefore prces) depends on who else Catalna sells the servce to, the prcng queston s lnked to the Whom to Sell to queston. Ths s partcularly nterestng because the economc value of the servce for Henz may be hgher or lower f Hunt s also uses the servce;.e., ths servce can have postve or negatve externaltes. If the servce has postve externaltes, t makes obvous sense for the frm to sell ts servce to both Henz and Hunt s. If t has negatve externaltes, then Catalna would have to evaluate 4

7 whether the negatve externaltes for Henz and Hunt s s suffcently low to stll sell to both Henz and Hunt s; f not, t would have to sell the servce on an exclusve bass to ether Henz or Hunt s dependng on who would have the hgher wllngness to pay (hgher economc value). Thus the decson about whether to sell on an exclusve bass to one manufacturer or on a non-exclusve bass to multple manufacturers s an emprcal queston for Catalna. Further, the whom to sell to queston s ntertwned wth the What prce to charge queston. Thus far n ths scenaro, we have treated the qualty of the targetng servce that Catalna offers as fxed. We treat the qualty of the targetng servce as the accuracy wth whch t can help a frm such as Henz to dentfy the segment that Henz seeks to target. Catalna can ncrease the accuracy of ts targetng servce n a number of ways: () use demographc nformaton; () ncrease the length of purchase hstory of households wthn a category at a cooperatng retaler; (3) use nformaton about purchasng behavor n other categores at the cooperatng retaler; and (4) combne nformaton about purchasng behavor of households from other retalers. Demographc nformaton has been shown to be of lmted use n predctng consumer preferences for grocery products (e.g., Ross, McCulloch and Allenby 996). Increasng length of purchase hstory should work n most categores where there are stable preferences. However ncreasng purchase hstory length may become less useful f consumer preferences change over tme. As an obvous example, lengthenng purchase hstores to mprove accuracy can be counter-productve n categores lke dapers where purchases n the category tend to be for a lmted duraton. Recently there has been nterest n cross-sellng products and a number of mult-category studes have shown that certan characterstcs such as prce and feature senstvty may be correlated across categores (Ansle and Ross 998). Clearly, household purchases across retalers can be useful n mprovng accuracy, but Catalna does not have ths opton because t s contractually oblged not to pool nformaton across retalers that cooperate wth t n provdng purchase hstory data. Thus the most promsng means by whch Catalna can mprove ts accuracy n most categores s by lengthenng the purchase hstory whch t uses to target. For the purposes of analyss ths paper, we wll restrct ourselves to usng purchase hstory data wthn the targeted category of nterest at the focal retaler. Households are dentfed only by a retaler s nternal dentfcaton number (say from a loyalty program) and therefore t s mpossble for Catalna to pool nformaton across multple retalers. 5

8 If we relax the assumpton that targetng accuracy s fxed, Catalna needs to decde on the qualty of ts servce,.e., how accurate ts targetng servce should be. Ths we call the What to sell queston. For most products/servces, frms would lke to maxmze the qualty of ther products/servces f ncreasng qualty were relatvely costless. However, targetng servces are dfferent n that ncreasng the qualty of the servce may reduce the economc value of the servce for the downstream clents. The dea s smple: f the targetng servce s sold on an exclusve bass to only Henz, the economc value of the targetng servce for Henz wll defntely ncrease because Henz can more effectvely prce dscrmnate ts customers. But f the targetng servce s sold to both Henz and Hunt s, the prce dscrmnaton effect of targetng can be overwhelmed by the more ntense competton created by targetng (e.g., Shaffer and Zhang 995). Whether the prce dscrmnaton effect or competton effect domnates s moderated by the level of targetng accuracy (Chen, Narasmhan and Zhang 00). At low levels of accuracy, prce dscrmnaton effects domnate competton effects. But at hgh levels of accuracy the competton effect domnate prce dscrmnaton effects. Thus Catalna could potentally destroy economc value to downstream clents by ncreasng accuracy f t sold the product on a non-exclusve bass to both Henz and Hunt s. Catalna may fnd t worthwhle to ncrease accuracy and sell on an exclusve bass to Henz or Hunt s to reduce the effects of competton. Alternatvely, t may reduce accuracy and sell to both Henz and Hunt s and extract greater total revenues from both. It s also mportant to note that many theoretcal papers have restrcted themselves to allowng for household heterogenety only on horzontal attrbutes. In realty, households are not only heterogeneous on horzontal attrbutes, but also on vertcal attrbutes. Hence t s possble that some of the theoretcal nsghts may not carry over n real markets. An emprcal analyss that uses flexble models of consumer behavor that are approprate for a partcular context s mportant to address the strategy questons of a partcular frm. Theoretcally, therefore the What to sell queston s ntertwned wth the Whom to sell queston and the For how much queston for targetng servces. The goal of ths paper s to offer an emprcal approach to help a personalzaton servces frm such as Catalna arrve at an optmal answer to these questons. Whle the detals of the emprcal modelng n ths paper wll be talored to the envronment n whch Catalna operates, the general approach we develop to address the research 6

9 questons can be appled n other emprcal contexts wth approprate modfcatons for the specfc characterstcs of that context. For example, the framework can be used to help whether DoubleClck should sell ts targeted advertsng servces on an exclusve bass or a non-exclusve bass. Here we wll need to calbrate the mpact of advertsng (as opposed to couponng) on the downstream frms proftablty, but the rest of the analyss would be smlar...3 The Retaler as a Compettor to Catalna Catalna uses purchase hstory data of retalers n offerng targeted couponng servces. A natural queston that arses s: What f the retaler decdes to offer targeted couponng servces to manufacturers? Retalers have an advantage over Catalna n that targetng can also help mprove retal proftablty. Hence unlke Catalna, a retaler can potentally trade off mproved retal proftablty through targeted couponng aganst potental revenues from manufacturers such as Henz and Hunt s through the sale of personalzaton servces. Ths could mply that retalers may subsdze personalzaton servces n order to get hgher profts from the sale of goods. Large retalers wth the approprate nfrastructure could easly mplement such a targetng soluton. In fact, Tesco n the U.K. has been successfully collaboratng wth dunnhumby, a U.K. based frm n the development of personalzed marketng servces that ncludes targeted couponng over the last decade (Humby 004, Humby et al 003). In the U.S., dunnhumbyusa s a jont venture between Kroger and dunnhumby that seeks to replcate dunnhumby s success n the U.K. wth Tesco. We therefore also address the trple questons of Whom to Sell to, What to Sell and For how much? from a retaler s pont of vew. We measure the potental mprovement n profts from the sales of goods for the retaler, relatve to the mprovement n profts from targetng for the manufacturers to assess the level of potental subsdy that retalers may be able to provde manufacturers n offerng targetng servces..3 Related Research Ths paper s related to both theoretcal and emprcal research streams on personalzaton. In terms of theoretcal research, Shaffer and Zhang (995) frst questoned the proftablty of targeted promotons n a compettve envronment. They demonstrated that targeted prcng n a compettve envronment leads to lower profts relatve to unform prcng. They assumed symmetrc frms. Relaxng the symmetry assumpton, Shaffer and Zhang (00) show that n the presence of asymmetry, hgher qualty frms wth larger market shares can 7

10 mprove profts due to gans n market share even though they may lose proft margns due to ncreased competton. Thus, all the above papers show that prce margns suffer due to ncreased competton from targetng, though Shaffer and Zhang (00) show that wth asymmetry the larger frm may stll make greater profts due to hgher volumes. As we dscussed earler, Chen et al. (00) demonstrated that the level of targetng accuracy s a moderatng varable n assessng the proftablty of personalzed promotons. There s an nverted-u shaped relatonshp between proftablty and accuracy of targetng (personalzaton). There s also a growng lterature on behavor based prcng, whch dscusses whether a frm should use a consumer s past purchases behavor to offer promotons to one s own customers or those of ts compettors (e.g., Vllas-Boas 999; Fudenberg and Trole 000; Shaffer and Zhang 000). Essentally these papers also fnd that behavor based targeted prcng also leads to a prsoner s dlemma. In terms of emprcal research on personalzed prcng, Ross et al. (996) and Besanko et al. (003) evaluate the proftablty of targeted coupons. In a semnal paper, Ross et al. (996) nvestgate how manufacturers can mprove ther profts wth dfferent levels of consumer purchase hstory and demographc nformaton. Unlke ths paper, they do not model the retaler or competton between manufacturers. Besanko et al. (003) only study the proftablty of targetng usng only last vst data, but models both competton and the retaler. However, unlke ths paper, nether of the above papers nvestgates the personalzaton servce provder s strategc decsons. Our analyss also fnds that these two papers over-estmate the proftablty mpact of personalzaton. Ths s because the models of consumer behavor used n computng profts wth and wthout targetng are dfferent. We dscuss ths ssue n detal n Secton 4... In terms of personalzed advertsng/communcaton, Ansar and Mela (003) develop algorthms for how a frm should use consumer hstory to customze emal communcatons. The rest of ths paper s structured as follows: Secton develops the model and the soluton strategy. Secton 3 descrbes the data and the estmaton results. Secton 4 answers the questons about the personalzaton vendor s strategy. Secton 5 nvestgates the mpact of personalzed promotons from the perspectve of the retaler. Secton 6 concludes.. The Model of the Personalzaton Servces Market 8

11 Fgure represents a schematc of the grocery markets n whch Catalna operates. There are four sets of agents nvolved n ths market: () The personalzed coupon servce provder (Catalna) () the manufacturers (3) a retaler and (4) consumers. *** Insert Fgure *** The model of manufacturers sellng through a retaler to the consumer has been studed n prevous research (e.g., Sudhr 00, Berto Vllas-Boas 004). In these models the prcng decsons of manufacturers and retalers are modeled as endogenous. The model n ths paper expands on ths lterature by endogenously modelng the decsons faced by a personalzaton coupon provder who facltates targeted couponng to consumers n the market. Snce Catalna s contractually oblged not to pool purchase hstory data across multple retalers, the assumpton that Catalna uses only data from one retaler for ts targetng servce s consstent wth nsttutonal realty. As n most prevous research (e.g, Besanko et al. 998, 003; Sudhr 00), we assume that the retaler s a local monopolst. Berto Vllas-Boas (004) ndeed fnds very lttle evdence for cross-retaler competton at the sngle category level. Fgure represents the schematc of the decson alternatves faced by a personalzaton servces provder (PSP) such as Catalna regardng the sales of ts personalzaton servces. We model the tmng of the game nto two phases: Phase whch nvolves the sale of personalzaton servces and Phase whch nvolves the sale of consumer goods. Below we descrbe the dfferent stages of the Phase decson related to the sale of targetng servces. *** Insert Fgure *** Phase : Sale of Personalzaton Servces Stage : Catalna s What to Sell Decson: At ths stage, Catalna decdes on the length of purchase hstory t should optmally use for targetng. Here we consder three alternatves: () Last Vst, along the lnes of targetng used n Besanko et al. (003), () Last Purchase, as used by Catalna n ts Catalna Coupon program and (3) Full Purchase Hstory, along the lnes of what Catalna uses n ts Catalna Drect program. Stage : Catalna s Intal Whom to Offer to and At What Prce Decson: For ease of exposton, we wll consder a market wth two natonal brand manufacturers. Catalna has three alternatves to make ntal offers at ths stage: () Offer the targetng servce to Frm and set ts Catalna restrcts the full purchase hstory to only 65 weeks, but we wll evaluate dfferent lengths of purchase hstory. 9

12 f f prce ( p ); () Offer the targetng servce to Frm and set the prce ( p ); and (3) Offer the targetng servce to both frms and set the prces to both frms ( b b p, p ). The subscrpts and on prces refer to the prce charged to frms and. The superscrpt f refers to the fact that frm or s frst offered the servce exclusvely. The superscrpt b refers to the stuaton when both frms are ntally offered the servce on a nonexclusve bass. Stage 3: Intal Offer Acceptance/Rejecton by Manufacturers: Manufacturers decde whether to accept or reject the offer of targetng servces at the offered prces. In the case where one frm s exclusvely offered and accepts the offer, the manufacturers and retalers then move to the second sales of goods phase wth one of the frms havng the capablty to target. If both frms were offered ntally, then there are four possble outcomes: where one of the frms accepts, both accept and nether accept. Gven these outcomes, the manufacturers and retalers then move to the sales of goods phase wth the frms that have accepted the targetng offers havng the capablty to target. Stage 4: Catalna offers Servce to Other Manufacturer at Second Offer Prce: If one frm s exclusvely offered the servce frst and rejects t, then Catalna wll offer the servce second to the other frm on an exclusve bass. For example, f Frm receves the offer after Frm rejects s the ntal offer of exclusve servce, ths prce to frm wll be denoted as ( p ), where the superscrpt s ndcates the frm was offered the servce second after frm refused. Stage 5: Second Offer Acceptance/Rejecton by Manufacturers: Manufacturers who receved the second offer can ether accept or reject the offer for the targetng servce. Gven ths decson, the manufacturers and retalers then move to the second phases (sales of goods) wth the frms that have accepted the targetng offers havng the capablty to target. The payoffs realzed after the second phase are shown n three rows n Fgure. We denote the profts from the sale of goods to manufacturer f by Π xy f, where x and y refers to the personalzaton servce purchase decsons of frms and respectvely. A value of (0) refers to whether the frm uses (does not use) the personalzaton servces. The frst row ndcates the payoff to the personalzaton provder (.e., prce charged for personalzaton servces), the second and thrd rows ndcate the payoffs to Frms and respectvely whch shows the net profts from the sale of goods and the fees pad (f any) to the personalzaton servce provder. 0

13 It s mportant to note that n ths game of complete nformaton, Stage 4 and Stage 5 are n the off-equlbrum path, because Catalna wll offer the rght prce n the ntal offer so that whoever s offered ntally wll accept. We have marked the equlbrum paths n bold. Hence, even though there are 0 payoff matrces shown, the only relevant payoffs n equlbrum are the three payoff matrces where the frms that are ntally offered the targetng servce by Catalna accept the product. Nevertheless, the payoffs from the off-equlbrum paths are crtcally mportant for Catalna to fgure out what prce t should charge the frms n Stage. Ths s because Catalna s offer prce to the frms should take nto account the ncremental profts a frm wll make relatve to the outcome where the compettor obtans exclusve use of personalzaton servces. It should be noted that the prce charged s not wth respect to the stuaton where there s no targetng at all. Ths s because the scenaro where nether frm purchases personalzed coupons wll not be on the sub-game perfect equlbrum path and therefore s not a credble alternatve threat to ether frm or frm. Ths lmts the amount of value that can be extracted f 0 0 from ether frm by the personalzaton servce provder. Hence b 0 0 P =Π Π ; P =Π Π. b 0 f 0 0 P = Π Π ; P =Π Π and Phase : Sales of Goods Stage : Manufacturer: Manufacturers set wholesale prces and the coupon face values for ndvdual households. If they have not purchased the personalzaton servces, all households are assumed to have a coupon face value of zero. 3 Stage : Retaler takes the nformaton about wholesale prces and coupons ssued n settng retal prces. Snce the coupons are ssued by the retaler, t s reasonable to assume that the retalers take nto account the coupons ssued n settng retal prces. 4 Stage 3: Gven the retal prces and coupons ssued, the household makes buyng decsons n order to maxmze utlty. We now develop a detaled model of these three stages of Phase II. We descrbe the decsons faced by each of the players below. We begn wth the consumer model, then descrbe the retaler and manufacturer models respectvely. 3 Techncally manufacturers set the wholesale prces and Catalna decdes whether to offer the coupon and what s face value wll be, but ths dstncton s unmportant for the results after the manufacturer has made the decson to purchase the targetng servce. 4 Ths model where the manufacturer moves frst s the Manufacturer Stackelberg model. Consstent wth the prevous lterature (Sudhr 00; Besanko et al. 003), we dd not fnd support for the Vertcal Nash Interacton where the manufacturer and retaler moves smultaneously. Therefore we omt detals of the Vertcal Nash model for brevty.

14 Consumer A household ( =,,...,H) chooses one of avalable brands (denoted by j =...) n the category or decdes not to purchase n the category (j = 0, the no-purchase alternatve or outsde good ) on each household shoppng occason t =,,...,n. Let the vector X denote all varables for brand j experenced by household at shoppng occason t. Ths vector ncludes brandspecfc ndcators, marketng mx varables such as features, dsplays, and household-specfc varables whch depend on the prevous purchase/s such as state dependence and household stock on occason t. Consumers choose the brand that offers the maxmum utlty. We specfy the ndrect utlty of household for brand j (j =...) on shoppng occason t as follows: u = X β r α + ξ + ε () where X ncludes all varables that affect household s evaluaton of brand j on occason t as well as tme nvarant brand ntercepts, r s the prce of brand j at t, ξ s the brand j- specfc effect on utlty at shoppng occason t that affects all households but whch s unobserved by the econometrcan, and ε s the unobserved utlty of brands that vary over shoppng occasons across households. Snce the ndrect utlty for any tem n the choce set s dentfed only n terms of dfferences wth respect to a base choce n the logt model, we treat the outsde good as the base choce and normalze ts utlty as follows: u = ε 0t 0t The elements of the vector ε ( ε, ε,. ε ) = L each are assumed to follow an ndependent t 0t t t Gumbel dstrbuton wth mean zero and scale parameter. We model heterogenety usng a latent class framework (Kamakura and Russell 989) 5. Consumers are probablstcally allocated to one of K segments, where each segment k has ts 5 The latent class model wth dscrete segments has consderable emprcal valdty and manageral relevance (Wedel and Kamakura 000). A competng model s one whch characterzes consumer heterogenety usng a contnuous heterogenety dstrbuton (Gonul and Srnvasan, 993). Andrews et al. (00) fnd that both the dscrete and contnuous heterogenety dstrbutons ft the data farly well, though some papers have argued that contnuous heterogenety coupled wth dscrete heterogenety can ft the data better (Allenby et al. 998). In ths paper, we apply the latent class approach because of ts computatonal tractablty when solvng for the equlbrum targetng prces wth compettve and retaler reactons.

15 own parameter vector ( k k, ) α β. The sze of segment k s denoted as f k, whch can be nterpreted as the lkelhood of fndng a consumer n segment k, or the relatve sze of the segment n the populaton of consumers. The probablty that household that belongs to segment k chooses a brand j s gven by: k k exp( X β rα + ξ ) k S = () k k exp( X + l lt β rltα ξlt ) Note that ξ are the common demand shocks that affect all consumers. These are observable by the prce-settng frms and consumers n the market but unobservable by the researchers. Vllas-Boas and Wner (999) show that proft-maxmzng frms wll take ξ nto account when settng prces, therefore prce s correlated wthξ. Ths causes a prce endogenety problem. Wthout correctng for endogenety, the prce coeffcent wll be based towards zero. We wll dscuss how we address ths ssue n the estmaton secton. Because k f represents the lkelhood of fndng a consumer n segment k, the uncondtonal probablty of choce for brand j by consumer n tme perod t can be computed as: K K k k exp( X β rα + ξ ) k k k S = = f S f (3) k k k = k = exp( X + l lt β rltα ξlt ) Followng earler lterature (e.g., Besanko et al. 998), we assume the potental market sze for the category n any store-week to be the number of households that make shoppng trps (N t ) to that store n that week. On any gven week on whch a store vst s made, the consumer can choose to make the purchase ncdence decson, or the brand choce decson wthn the category. Retaler The retaler s goal s to maxmze category profts n tme perod t, gven the decsons to buy personalzaton servces by manufacturers. Let x = (0) denote whether manufacturer has purchased (not purchased) the personalzaton servce. Smlarly, let y = (0) denote whether manufacturer has purchased (not purchased) the personalzaton servce. Therefore the retaler xy chooses retal prces r, K r t xy t servce to solve the followng problem:, condtonal on whch frms have purchased the personalzaton 3

16 r,, r Nt xy xy xy xy xy Rt r w S r D j= = max Π = ( ) ( ) xy xy (4) t t where xy D s a matrx of ndvdual specfc coupon values as descrbed earler under the alternatve scenaros where the dfferent manufacturers purchase the targetng servce. The shares n the above equaton are the weghted average of the segment-specfc shares across the k segments. Takng the frst order condtons of equaton (4) wth respect to retal prces, we obtan the retaler s prcng equaton for each product n the category n terms of wholesale prces. The detals of the dervaton are provded n Appendx A. The retaler prce equaton s shown n equaton A5 of the appendx. Manufacturer A manufacturer m offerng a subset ℵ m of brands n the market sets the wholesale prce xy w (where j ℵ m ) and the coupon face values to ndvdual households ( D xy ) so as to maxmze the manufacturer s profts. A manufacturer who has not been sold the personalzaton servce wll have coupon face values set to zero. The manufacturer takes nto account the knowledge that retaler prces ( r xy ) wll be set takng nto account the wholesale prces and the coupon face values that have been ssued to ndvdual households. The proft of manufacturer m at tme t from the sales of goods s gven by: N t xy xy xy xy xy xy xy mt w D c S r w D D j ℵ = Π = ( ) ( (, ) ) (5) m where c s the margnal cost of the manufacturer for brand j n perod t, and xy xy xy xy xy S ( r ( w, D ) D ) s the probablty of household, buyng brand j n perod t gven the decsons of manufacturers (denoted by x) and (denoted by y) to purchase the purchase hstory data. Note that the retaler sets the retal prce takng nto account both the wholesale xy prce ( w ) and the vector of dscounts offered to all households,.e., D xy { xy } H = D =. We can wrte the manufacturer proft equatons at the ndvdual level as follows: xy xy xy xy xy xy xy Π = ( w D c ) S ( r ( w, D ) D ) mt j ℵ m 4

17 Takng the frst order condtons of (5), wth respect to w = w D, we are able to solve xy xy xy for the effectve margn from each household. Then the wholesale prce wll be w xy = max w xy and D = w w. The dervaton s detaled n the Appendx A. xy xy xy We specfy manufacturer margnal cost as a functon of factor prces, whch assumes a fxed proportons producton technology. c = λ + θ* Β + υ (6) j t where Β t are the factor prces, λ j are brand specfc ntercepts and υ s the cost shock. Estmaton and Soluton Strategy The soluton strategy conssts of the followng fve steps, where the frst two steps nvolve estmaton to characterze the market and the remanng three steps nvolve polcy smulatons to nfer the optmal strategy for the personalzaton servce frm. Step : Estmate the demand and supply model dscussed above. The demand model s a latent class model of household preferences and responsveness to marketng mx wth alternatve levels of purchase hstory lengths used to proxy for personalzaton qualty from consumer nformaton. 6 To account for potental prce endogenety concerns, we use the control functon approach developed by Petrn and Tran (004). Essentally, we obtan resduals from a regresson of prces of the dfferent brands aganst ts cost factors and nclude these resduals n the utlty equaton () n estmatng the demand model. More detals of the control functon approach are explaned n appendx B. Step : Apply Bayes rule on the aggregate latent class estmates usng each household s purchase hstory (the length of hstory vares dependng on the scenaro beng consdered and the number of vsts of the household durng the estmaton perod) to obtan household level probabltes of membershp n each of the latent classes. When purchase hstores are short, the ndvdual level probabltes dffer very lttle from the aggregate probabltes and as the purchase hstores lengthen, the ndvdual probabltes tend to become more dfferent from the aggregate probabltes reflectng more closely the dosyncratc preferences of the household. 6 Other aspects of consumer nformaton, such as consumer demographcs could potentally mprove the qualty of the personalzaton servce, but the ncremental mpact of demographcs over purchase hstory was mnscule n our analyss. So we focus on purchase hstory length as a measure of accuracy and omt demographcs n further analyss. Ths s consstent wth the fndngs n Ross et al. (996). 5

18 The manufacturers may use varyng levels of nformaton about consumer purchase hstory n targetng them. To ncorporate ths nformaton, consumers are classfed to demand segments by usng the result that the posteror probablty that a consumer belongs to a segment k condtonal on observed choce hstory H s obtaned by revsng the pror probablty of membershp k f n a Bayesan fashon (Kamakura and Russell 989): k ' k ( ) k' ( ') L H k f Pr( k H ) = (7) L H k f Step 3: Havng thus characterzed the household level preferences usng dfferent lengths of purchase hstory data, solve for the optmal prces and dscounts under alternatve targetng scenaros (exclusve, non-exclusve). To obtan steady state proft estmates, solve for prces and dscounts over a large number of weeks trackng both consumer past purchases (to account for state dependence effects) and nventores (to account for nventory effects on category purchases) over ths perod. In solvng for the equlbrum prces and dscounts, take nto account not only the prcng behavor of the manufacturers, but also the equlbrum passthrough behavor of retalers. The same marketng mx varables for features and dsplays as n the estmaton data are used n ths smulaton. Step 4: Gven the optmal prces and dscounts computed based on Step 3, evaluate manufacturer profts based on consumer choces, at the optmal prces and dscounts. Note that optmal prces and dscounts wll vary dependng on the avalable purchase hstory and whch frms do targetng. However consumer behavor should be based on the same true preferences rrespectve of what data frms have. Hence n predctng consumer choce, gven the chosen prces and dscounts, t s crtcal to always use the household level estmates obtaned usng the full purchase hstory data, because these are our best estmates of the true household behavor. One should not use the estmates obtaned wth shorter purchase hstores at ths stage as ths wll grossly overstate the proftablty of targetng. On frst glance, ths ssue may appear a mere detal, but we fnd that the mprovements n profts n earler papers (Ross and Allenby, 996; Besanko et al., 003) can be overstated f we do not assume a true stable consumer behavor based on the full purchase hstory. Step 5: Gven the profts obtaned under alternatve targetng scenaros of hstory length (full purchase hstory, only last purchase, only last vst, no targetng) and clent choce 6

19 (exclusve, non-exclusve), solve for the optmal strategy for the personalzaton servce provder, that answers the three questons (what to sell, to whom to sell and for how much) we seek to answer. 3. Emprcal Illustraton Data We use the AC Nelsen scanner panel data on the ketchup category from the largest retaler n the Sprngfeld, MO market for the emprcal llustraton. We restrct attenton to the four largest brand-szes whch collectvely account for 64% of the sales n ths category: Henz 3 oz, Hunt s 3 oz, Henz 8 oz, and the Store Brand 3 oz and use 00 weeks of purchase hstory data durng 986 to 988. We use a sample of 43 households based on whether they made at least fve purchases of the chosen brand-szes durng the 00 weeks of analyss. The 43 households bought ketchup n 073 vsts out of the total 660 store vsts. The summary of brand shares (condtonal on purchase) and prces are gven n Table. *** Insert Table *** Estmaton Results Based on the Bayesan Informaton Crteron (BIC), we found that a three segment latent class model s the best model. As dscussed earler, we correct for prce endogenety usng the approach n Petrn and Tran (003). The results are presented n Table 3 below. Segment s the least prce senstve, but also purchases least n the category based on the negatve coeffcents assocated wth the ntercept. It s 4% of the market. Segments and 3 are more prce senstve than segment and together consttute 76% of the market. However Segment s relatvely more loyal to Henz 3 oz. Segment 3 s preferences are more dffused across all brands and s the most prce senstve segment n the market, suggestng the least amount of loyalty. They were also relatvely nsenstve to nventory levels. Ths suggests that ths segment does not purchase ketchup at regular ntervals, but opportunstcally buy any brand when t s on sale. *** Insert Table 3*** The prce elastctes for the three segment latent class demand model as descrbed by Kamakura and Russell (989) and reported n Table 4. The own and cross prce effects are as expected. Hunt s 3 and the Store Brand 3 have hgher own elastctes than the two Henz brand-szes. Henz 8, the most expensve brand, has the lowest own elastcty. Hunt s 3 and Store 3 have hgher cross-elastctes, whch ndcate that swtchng would be hgher between 7

20 these brand-szes. Increase n the prce of the largest brand-sze Henz 3, wll result n more substantal substtuton to Hunt s 3 and Store 3 rather than Henz 8. *** Insert Table 4*** The cost estmates n Table 5 obtaned through the estmaton of Equaton 6 suggest that Henz and the store brand have lower margnal costs than Hunt s (though the dfferences are not sgnfcant). The prce of tomatoes 7 (the man ngredent of ketchup) s used as the factor cost n the cost equaton. Not surprsngly, tomato prces have a sgnfcant effect on margnal cost of ketchup. *** Insert Table 5*** 4. Analyss of Personalzaton Servce Provder Decsons Based on the estmates obtaned n Secton 3, we can now evaluate the proftablty of the alternatve decson scenaros from the personalzaton servce provder s perspectve usng smulatons. We smulate the market for 00 weeks, whch s a suffcently long perod to obtan stable estmates of profts under alternatve decson scenaros. 8 We frst demonstrate how length of purchase hstory affects the ablty to personalze promotons n Secton 4.. In Secton 4., we evaluate the profts of manufacturers (Henz and Hunt s) from the sale of goods as a functon of whether they use personalzed coupons ether on an exclusve or syndcated bass,.e., we compute ( Π, Π, Π, Π, Π, Π ) for dfferent lengths of purchase hstory. Based on these profts, we nfer what prce the personalzaton servce provder can charge under dfferent scenaros and thus arrve at the optmal decsons of the personalzaton servces vendor n Secton How Length of Consumer Purchase Hstory affects Personalzaton It s natural that personalzaton can be mproved by ncreasng the length of consumer purchase hstory nformaton used n targetng. Ths s the ratonale used by Catalna Marketng, n offerng two dfferent targetng products to packaged goods manufacturers, one whch uses 7 The prce data for tomatoes were obtaned from the Bureau of Labor Statstcs. Part of the data was obtaned from the webste and the rest through emal from BLS offcals. 8 Average profts per week were very stable wth consumer choces smulated over one hundred weeks. Increasng the perod of smulaton further had no effect on the results, but smply ncreased computaton tme. 8

21 only the last purchase by a customer, and a second whch uses the last 65 weeks of consumer purchase hstory. We now nvestgate how the length of purchase hstory affects the extent to whch personalzaton can be mproved. Frst, to compare aganst the results of Besanko et al. (003), we nvestgate the scenaro where only last vst nformaton s used for targetng. Second, to be consstent wth Catalna s couponng strategy and to compare the scenaros n Ross et al. (996), we nvestgate the scenaros where only last purchase nformaton s used and where the fully avalable purchase hstory s used. Fgures 3a-3c shows how the posteror probabltes (of belongng to segment ) of consumers change as a functon of the nformaton used. Fgure 3a shows the dstrbuton of posteror probabltes usng only the last week s nformaton of consumer purchases, Fgure 3b shows the dstrbuton of posteror probabltes usng only the last consumer purchases (whch could be an earler week f no purchase was made n the category n the last week) and Fgure 3c shows the dstrbuton of posteror probabltes usng 00 weeks of consumer purchase hstory. Fgure 3a clearly shows that the marketer can acheve very lttle dscrmnaton across consumers by usng only nformaton about the last vst, as the vast majorty of consumers are classfed n the same quntle as the aggregate probablty ( f Equaton 7),.e., 0.47 for Segment. The last purchase nformaton enables more dscrmnaton to be acheved between consumers, as seen n Fgure 3b. We can acheve much better dscrmnaton among consumers by usng 00 weeks of consumer purchase nformaton, as shown n the polarzed probabltes n Fgure 3c. By usng 00 weeks of nformaton, almost 40% of consumers are assgned wth a hgh degree of probablty (posteror probablty n the hghest quntle) to segment, whle more than 40% of consumers are not assgned to segment wth a hgh degree of probablty (posteror probablty n the lowest quntle). *** Insert Fgures 3a, 3b and 3c*** k n 4. The Effect of Personalzed Coupons on Manufacturer Clent Profts We now assess how the profts of manufacturer clents (Henz and Hunt s) change as a functon of personalzed coupons. We consder stuatons () when targeted couponng s done exclusvely by Henz or Hunt s and when both frms do targetng and () when targetng s based merely on last vst data or on the last purchase data, or based on the full purchase hstory data. In performng ths analyss, we control for retaler behavor by assumng that the retaler does not 9

22 target for the store brands usng the purchase hstory data avalable to t. 9 The proftablty results are reported n Table 6. Several nsghts emerge. *** Insert Table 6*** Frst, we see that personalzed promotons by both frms ncrease profts under the last vst scenaro, the last purchase scenaro and the full purchase hstory scenaros, relatve to the no-targetng scenaro. Further, the profts are greater for the full purchase hstory scenaro compared to both the last purchase and last vst scenaro. Ths shows that n ths market, the postve prce dscrmnaton effect of targetng domnates the negatve compettve effect of targetng. Even wth the full purchase hstory beng used, the prce dscrmnaton effect s ncreasng (we checked ntermedate lengths of purchase hstory data and fnd profts ncrease as the number of weeks of data used for targetng ncreases). Essentally, ths suggests that even wth the full purchase hstory of our dataset we have not reached the peak of the nverted U relatonshp between targetng accuracy and proftablty n a compettve targetng scenaro that was derved theoretcally n Chen et al (00). Second, we compare the case where only one frm exclusvely targets versus the case where both frms target. Under targetng usng full purchase hstory, Henz makes more profts when both frms target than when Henz alone targets. Ths shows that there s a postve externalty from the use of targetng for Henz n ths market. For Hunt s there s a small decrease n profts when both frms target as compared to when Hunt s only targets, showng that there s a negatve externalty for Hunt s. Fnally, we examne the magntudes of the mprovements n profts from the use of targetng. The maxmum proft gan that any frm obtans by usng targeted prcng n the ketchup category s about %. An mprovement of gross margns by % can be a substantve ncrease n net profts. For example, Henz had a gross margn of 40% and a net margn of 0% n 003 (Hoover Onlne). A % ncrease n gross margn can then translate to an ncrease of about 8% n net margns. 4.. Improvng the Accuracy of Estmated Targetng Profts 9 We also consdered the cases of (a) ntermedate lengths of purchase hstory data, and (b) where the retaler sends targeted coupons for the store brands. Snce these alternatve scenaros have no mpact on the ntuton and the qualtatve results we do not dscuss these n the paper. As dscussed earler, ncluson of demographc varables have very lttle mpact on the personalzaton. 0