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1 Yupng Lu The Long-Term Impact of Loyalty Programs on Consumer Purchase Behavor and Loyalty Despte the prevalent use of loyalty programs, there s lmted evdence on the long-term effects of such programs, and ther effectveness s not well establshed. The current research examnes the long-term mpact of a loyalty program on consumers usage levels and ther exclusve loyalty to the frm. Usng longtudnal data from a convenence store franchse, the study shows that consumers who were heavy buyers at the begnnng of a loyalty program were most lkely to clam ther qualfed rewards, but the program dd not prompt them to change ther purchase behavor. In contrast, consumers whose ntal patronage levels were low or moderate gradually purchased more and became more loyal to the frm. For lght buyers, the loyalty program broadened ther relatonshp wth the frm nto other busness areas. The fndngs suggest a need to consder consumer dosyncrases when studyng loyalty programs and llustrate consumers cocreaton of value n the marketng process. As an mportant component of frms customer relatonshp management (CRM) strategy, loyalty programs am to ncrease customer loyalty by rewardng customers for dong busness wth the frm. Through these programs, frms can potentally gan more repeat busness and, at the same tme, obtan rch consumer data that ad future CRM efforts. Snce Amercan Arlnes launched the frst contemporary loyalty program n 1981, loyalty programs have blossomed and now span varous ndustres, ncludng retal, travel, and fnancal ndustres. It s estmated that more than half of U.S. adults are enrolled n at least one loyalty program (Kvetz and Smonson 2003). Despte the prevalent use of loyalty programs, ther effectveness s not well understood (Bolton, Kannan, and Bramlett 2000). Some researchers queston the value of loyalty programs. For example, Dowlng (2002) suggests that loyalty programs do not necessarly foster loyalty and are not cost effectve and that the prolferaton of loyalty programs s a hype or a me-too scheme. Conversely, some recent studes show that loyalty programs have a postve mpact on consumers repatronage decsons and ther share of wallet (e.g., Lews 2004; Verhoef 2003). Wth lmted emprcal valdatons, the debate on whether loyalty programs are truly effectve contnues. The dvergent vews suggest a need to understand these programs better. Ths s also of strategc mportance because such programs are Yupng Lu s Assstant Professor of Marketng, College of Busness and Publc Admnstraton, Old Domnon Unversty (e-mal: edu). The author thanks Ruth Bolton, Wllam Bouldng, Rchard Staeln, and the four anonymous JM revewers for ther helpful suggestons on pror drafts of ths artcle, as well as Old Domnon Unversty for fundng the research. To read and contrbute to reader and author dalogue on JM, vst 2007, Amercan Marketng Assocaton ISSN: (prnt), (electronc) 19 costly nvestments and requre a frm s long-term commtment. It s vtal for managers to know whether and how these programs work before they take the plunge. Ths research contrbutes to a better understandng of loyalty programs n three ways. Frst, although evdence about the effectveness of loyalty programs has begun to accumulate recently, the feld s stll underdeveloped, and a clear pcture has yet to emerge. Addressng ths ssue, Bolton, Kannan, and Bramlett (2000, p. 28) suggest that to determne the long-term effcacy of a loyalty rewards program, a company must quantfy the program s nfluence on future purchase behavor (e.g., usage levels). The current research responds to the suggeston by quantfyng on a large scale the effectveness of a loyalty program n the convenence store ndustry. The key research queston s whether loyalty programs change consumers patronage levels and exclusve loyalty to the frm. These outcomes are mportant to study because they are drectly related to consumer proftablty and the fnancal success of a loyalty program. Second, ths research examnes consumers longtudnal behavor change after they jon a loyalty program. Gven the long-term orentaton of loyalty programs and ther transformaton of sngle purchases nto multperod decsons (Kopalle and Nesln 2003), t s natural that ther effectveness should be examned longtudnally. Methodologcally, studyng loyalty programs over tme allevates self-selecton bas. Because loyalty program members may already be frequent customers who are more lkely to fnd the program attractve, smply comparng the behavor of loyalty program members wth that of nonmembers cannot establsh a conclusve causal relatonshp (Leenheer et al. 2003). Thus, examnng dynamc behavor change s more powerful than cross-sectonal studes of behavor at a certan pont n tme (Verhoef 2003). However, relatvely few publshed emprcal studes have examned longtudnal loyalty program effects, especally from the perspectve of Journal of Marketng Vol. 71 (October 2007), 19 35

2 contnuous loyalty programs. Ths leaves a gap n the understandng of the true effects of such programs. A recent study by Lews (2004) has advanced ths area by examnng dynamc postreward effects on consumer behavor n the context of a contnuous loyalty program. The current research extends ths work by studyng the general effects of such a loyalty program on long-term purchase behavor change. Thrd, ths research studes how the dosyncrases of ndvdual consumers nfluence behavor changes that occur after they jon a loyalty program. Prevous research has shown that the dosyncratc ft of an ndvdual wth a loyalty program can nfluence hs or her lkelhood of jonng the program (Kvetz and Smonson 2002). However, t s not clear whether such effects would carry over to how these consumers change ther purchase patterns after program enrollment. Addng to the lmted set of ndvdual dosyncrases that have been examned n the lterature, the current research explores how consumers wth dfferent ntal usage levels change ther behavor to maxmze the benefts they receve from a loyalty program. Are Loyalty Programs Effectve? Defnton Loyalty program. In ths artcle, a loyalty program s defned as a program that allows consumers to accumulate free rewards when they make repeated purchases wth a frm. Such a program rarely benefts consumers n one purchase but s ntended to foster customer loyalty over tme. Thus, promotons that work as one-shot deals, such as nstant scratch cards, are not consdered loyalty programs here. Ths excluson s approprate because these one-tme promotons do not create the same customer lock-n as true loyalty programs (Sharp and Sharp 1997). Consumer loyalty. Ths research adopts Olver s (1999, p. 34) defnton of consumer loyalty as a deeply held commtment to rebuy or repatronze a preferred product/servce consstently n the future. Accordng to Olver, consumer loyalty can occur at four dfferent levels: cogntve, affectve, conatve, and behavoral. Although all four facets of consumer loyalty are mportant, the current research focuses on behavoral loyalty. Ths aspect of consumer loyalty has not been thoroughly examned n prevous research, even though t has a drect mpact on a frm s bottom lne and facltates the assessment of loyalty program proftablty (and, relatedly, the decson to nvest n such a program or to expand or termnate an exstng program). Loyalty Programs and Value Enhancement Loyalty programs are often consdered value-sharng nstruments and can enhance consumers perceptons of what a frm has to offer (Bolton, Kannan, and Bramlett 2000; Y and Jeon 2003). Ths value enhancement functon s mportant because the ablty to provde superor value s nstrumental to customer relatonshp ntaton and retenton (Srdeshmukh, Sngh, and Sabol 2002; Woodruff 1997). Indeed, enhanced value percepton s consdered a necessary condton to a loyalty program s success (O Bren and Jones 1995). Loyalty programs provde value to consumers n two stages. In the frst stage, program ponts are ssued to consumers at the tme of purchase. Although these ponts have no practcal value untl they are redeemed, recent studes show that they have mportant psychologcal meanng to consumers (Hsee et al. 2003; Van Osselaer, Alba, and Manchanda 2004). The psychologcal beneft ncreases the transacton utlty of a purchase (Thaler 1985) and, subsequently, the overall value percepton of dong busness wth the frm. Because consumers can later redeem ponts for free rewards, pont accumulaton creates an antcpaton of postve future events, whch ncreases consumers lkelhood of stayng n the relatonshp (Lemon, Whte, and Wner 2002). In the redempton stage, consumers receve both psychologcal and economc benefts from a loyalty program. The free reward functons as a postve renforcement of consumers purchase behavor and condtons them to contnue dong busness wth the frm (Sheth and Parvatyar 1995). Psychologcally, gvng free rewards to customers shows the frm s apprecaton and personal recognton of ts customers. Ths sense of beng mportant can enhance consumers overall sense of well-beng and deepen ther relatonshp wth the frm (Btner 1995; Gwnner, Gremler, and Btner 1998). Some researchers suggest that there are other psychologcal benefts as well, such as the opportunty to ndulge n gult-free luxures (Kvetz and Smonson 2002) and a sense of partcpaton (Dowlng and Uncles 1997), whch may be especally approprate for brands that do not carry ths belongngness (Olver 1999). All these psychologcal and economc benefts translate nto an attractve value proposton from the frm. Loyalty Programs and Relatonshp Commtment Beyond the need for superor value, a necessary condton for any relatonshp to develop s the commtment of both partes n the relatonshp (Morgan and Hunt 1994). Gven a wde varety of choces and a low swtchng barrer, t s easy for today s consumers to swtch among dfferent frms. Ths poses sgnfcant threats to customer relatonshps because consumers are not lkely to commt to a sngle brand or frm. Loyalty programs can allevate ths lack of commtment and reduce customer defecton by rasng swtchng costs. Because loyalty programs reward customers for ther repeated patronage, consumers tend to focus ther purchases n one program to maxmze the benefts they receve (Sharp and Sharp 1997). Such vested nterests n a program make t dffcult for compettors to entce customers away from a frm. Usng game-theoretc models, Km, Sh, and Srnvasan (2001) demonstrate that such a compettve barrer benefts the frm and results n hgher prces n the marketplace. Ths s especally true for hghvarety-seekng products and servces (Zhang, Krshna, and Dhar 2000). Loyalty programs not only help buld customer commtment but also demonstrate a frm s commtment. It s often costly for frms to ntate and mantan a loyalty program. It requres extensve efforts to manage pont records and reward ssuance. After such a program s n place, t s usually dffcult to termnate t wthout rskng the loss of con- 20 / Journal of Marketng, October 2007

3 sumers goodwll. Although a loyalty program brngs real cost to the busness, t also shows the frm s commtment to establshng a long-term relatonshp wth ts customers. Such a commtment and demonstraton of goodwll can further deepen the relatonshp between the frm and ts customers. Emprcal Evdence of the Effectveness of Loyalty Programs Lab and feld studes have examned whether loyalty programs ndeed postvely affect consumers. Although both types of research are mportant, ths secton focuses on emprcal examnatons of real loyalty programs based on actual consumer behavor because they are most closely related to the current research. Exstng studes n ths area can be classfed nto three categores dependng on the comparson base on whch conclusons are drawn. Comparson across compettors. The frst group of studes quantfes the mpact of loyalty programs by comparng them across multple frms. The focal varables are usually market share or share of wallet. Usng consumer panel data of grocery purchases, both Mäg (2003) and Leenheer and colleagues (2003) fnd mxed support for the postve effects of loyalty programs on share of wallet. Leenheer and colleagues study reveals ncreased share of wallet for four of seven programs and offers support for the use of accumulated rewards (as opposed to prce dscounts) n loyalty programs. Mäg fnds that loyalty program membershp ncreases a consumer s share of wallet and store vst and decreases shares for compettors. However, ths s supported only at the chan level, not at the store level. Focusng on the arlne ndustry, both Kopalle and Nesln (2003) and Nako (1992) conclude that frequent-fler programs enhance the value of an arlne s products and ncrease consumer demand for arlnes that offer such programs. Wthn ths same category of research but focusng on a dfferent behavoral varable, Sharp and Sharp (1997) nvestgate the mpact of Australa s Fly Buys program by comparng observed purchase frequences wth the Drchlet baselne and fnd only a weak mprovement n repeatpurchase behavor for most stores. Usng a smlar approach, Meyer-Waarden and Benavent (2006) fnd only mxed effects of the loyalty programs offered by several French grocery retalers. Because members of the Fly Buys program can earn loyalty ponts across stores, t can be argued that such a multstore loyalty program fosters loyalty toward the program rather than toward any partcular store and may even encourage consumers to dvde ther loyalty among multple frms (Dowlng and Uncles 1997). Ths lmts the generalzablty of the fndngs. Moreover, rewards offered by the Fly Buys program (free ar travel or lodgng) are unrelated to actons consumers need to take (patronzng retal stores) to accumulate ponts. Recent research suggests that ths type of program may elct reactance from consumers and reduce ther ntrnsc motvaton to engage n the orgnal purchase actvtes (Kvetz 2005). Comparson across consumers. The second type of exstng research compares the behavor of loyalty program members wth that of nonmembers to dentfy the mpact of loyalty programs. Both Verhoef (2003) and Bolton, Kannan, and Bramlett (2000) examne the effectveness of loyalty programs n the fnancal ndustry. Verhoef fnds that partcpaton n an nsurance frm s loyalty program makes consumers more lkely to stay wth the frm and encourages them to expand ther busness wth the frm. Bolton, Kannan, and Bramlett offer a more n-depth examnaton of ths ssue by studyng the moderatng effect of a credt card frm s loyalty program on the relatonshp between consumers servce experences and ther subsequent behavor. They fnd that program members wegh negatve experences less n ther repatronage decsons than nonmembers. Ths s consstent wth the proposton that loyalty programs form compettve barrers between frms and allow frms to enjoy ther customers more exclusvely. Bolton, Kannan, and Bramlett do not fnd a sgnfcant man effect of loyalty program membershp on customer retenton, but ther results show that loyalty program members used ther credt cards more than nonmembers. As mentoned prevously, studes comparng loyalty program member and nonmember behavor are subject to self-selecton bas. That s, the dfferences between members and nonmembers may exst before the program rather than beng a result of the program. Ths makes t dffcult to establsh the drecton of the causal relatonshp, promptng researchers to suggest the approprateness of studyng dynamc behavor change nstead (Lews 2004; Verhoef 2003). Comparson across tme. The thrd category of studes remedes self-selecton bas by studyng the same consumers behavor across tme. A majorty of these studes focus on short-term loyalty programs. A typcal settng s an N-week turkey/ham supermarket reward program n whch consumers need to spend over a set amount each week for N weeks to receve a free turkey or ham (e.g., Lal and Bell 2003; Taylor and Nesln 2005). Studes fnd general support for such programs n terms of ncreased spendng levels. Drèze and Hoch (1998) further conclude that a loyalty program targetng a specfc product category not only ncreases spendng n the focal product category but also ncreases store traffc and overall spendng n all categores. When studyng behavor changes over tme, researchers suggest two types of effects: a short-term pont pressure effect and a long-term rewarded behavor effect (Taylor and Nesln 2005). The pont pressure effect represents a temporary shock n spendng as consumers ncrease ther purchase levels to qualfy for a reward, analogous to the artfcal ncrease n sales durng a promoton. Drawng on the goalgradent hypothess, Kvetz, Urmnsky, and Zheng (2006) fnd that ths pont pressure effect ncreases as consumers get closer to a reward, resultng n purchase acceleraton. However, they also fnd that after the reward s obtaned, the postve change n behavor dsspates, smlar to the sales dp after a promoton. In contrast, the rewarded behavor effect refers to long-term sustaned purchase ncrease, whch can be a result of factors such as apprecaton of the reward receved and stronger loyalty toward the frm. These two effects manfest dfferently n short-term and contnuous loyalty programs (e.g., frequent-fler programs). Short-term programs are analogous to sales promoton and sgnfy frms temporary commtment. They create a pr- The Long-Term Impact of Loyalty Programs / 21

4 marly pont pressure effect due to the programs novelty and a clear tme lne and goal. Conversely, contnuous loyalty programs represent frms long-term commtment and are analogous to everyday low prce. Although they may be novel at frst and consumers may experence pont pressure each tme they get close to a reward, the long-term effects of such programs are manly the rewarded behavor type. Emprcal fndngs on the effects of contnuous loyalty programs across tme are more sparse. Allaway and colleagues (2006) offer an ndrect examnaton of longtudnal effects by segmentng retal loyalty program members accordng to ther behavor. They fnd that the program postvely affects only a small porton of consumers. Lews (2004) examnes the loyalty program of an onlne retaler and fnds that the level of reward receved n a pror perod postvely affects the probablty of makng larger-szed transactons n the current perod. Note that focusng on postreward effects presents two lmtatons. Frst, t creates a recursve relatonshp because the level of reward a consumer receves n one perod s tself contngent on the consumer s behavor change. As a result, a hgher-level purchase n subsequent perods may smply be a contnuaton of the prevous postve reacton to the loyalty program rather than a result of hgher-level rewards receved n pror perods. Second, postreward effects capture only one type of loyalty program effects. The pont pressure research dscussed prevously suggests that behavor changes can occur not only after but also before the recept of a reward (Kvetz, Urmnsky, and Zheng 2006; Taylor and Nesln 2005). There are also subtler aspects of a loyalty program, such as a sense of belongngness (Dowlng and Uncle 1997) and the percepton of effort advantage (Kvetz and Smonson 2003). Because consumers can earn free rewards only nfrequently n most programs, ther behavor changes are typcally drven by these subtler effects. Summary The emprcal studes revewed here provde mxed support for loyalty programs, and there s stll much controversy over whether the loyalty program s an appealng marketng tool (Leenheer et al. 2003; Shugan 2005). The foregong analyss reveals the need to address two ssues. Frst, gven the future orentaton of loyalty programs, t s necessary to ntegrate a long-term focus nto the research of these programs. Ths need s supported by the fndng that CRM efforts often do not produce obvous short-term gans but rather should be assessed n the long run (Anderson, Fornell, and Lehmann 1994). Most of the longtudnal studes revewed focus on loyalty programs that ran only for a short perod. Lews s (2004) study s the only one that systematcally examnes the dynamc effects of a contnuous loyalty program. However, as dscussed prevously, ts focus on postreward effects represents an ncomplete vew of loyalty program effects. Future longtudnal research should extend Lews s research to nclude more general effects of contnuous loyalty programs. Second, not all consumers respond to loyalty programs n the same way, because the appeal of a program can dffer among consumers, dependng on factors such as current usage levels and percepton of effort advantage (Km, Sh, and Srnvasan 2001; Kvetz and Smonson 2003). These ndvdual dfferences may have contrbuted to the mxed fndngs n the lterature. A few studes revewed have begun to examne the moderatng effects of ndvdual characterstcs (Lal and Bell 2003; Lews 2004; Taylor and Nesln 2005). However, the operatonalzaton of some of these varables lmts ther manageral relevance. The purchaselevel ters n Lal and Bell s (2003) study were based on the same tme span (.e., the entre analyss perod) as the focal varables, and the reward redempton n Taylor and Nesln s (2005) study occurred after the focal varables. Although these varables are useful n assessng the success of a short-term loyalty program after t s run, they provde lmted foresght for contnuous loyalty programs. For a contnuous program, knowledge of preexstng dfferences among consumers early on n the program wll provde more meanngful manageral mplcatons and better gudance on the desgn and management of such a program. Lews (2004) studed one such preexstng dfference namely, demographcs. Ths should be extended to nclude other a pror ndvdual dfferences that may nfluence consumers reactons to a loyalty program. Research Hypotheses Overvew The current research ams to answer three questons: (1) How do consumers change ther usage levels after jonng a loyalty program? (2) Does the program make consumers more loyal over tme? and (3) How do consumers wth dfferent ntal spendng levels respond dfferently to the program? These research questons specfcally tackle the two ssues rased n the lterature revew. Because the purpose of loyalty programs often s to ncrease the loyalty and value contrbuton of consumers, t s mportant to know whether ths goal s fulflled. Ths study examnes how consumers gradually adapt ther purchase behavor across an extended tme span after ther ntal enrollment n a long-term loyalty program. As dscussed prevously, the mplcatons of short-term versus long-term loyalty programs are dfferent, and so far, dynamc studes of the latter have been far more sparse and nconclusve. Ths research contrbutes to a better understandng n ths area. By capturng program effects through the movement of tme, ths research extends Lews s (2004) fndngs to nclude more general effects of a loyalty program and enables a frm to predct the path ts consumers wll take after jonng such a program. Ths research also examnes how a loyalty program affects consumers dfferently by examnng the moderatng effect of consumers exstng usage levels. It s of strategc mportance to examne the responses of consumers wth dfferent ntal usage levels because ther value contrbutons to a frm vary and can ether ncrease or dlute the frm s proftablty (Dowlng and Uncles 1997; Renartz and Kumar 2000). Marketng researchers have suggested that usage level s an mportant consderaton n loyalty program desgn and effects (Bolton, Kannan, and Bramlett 2000; Dowlng and Uncle 1997). However, ths premse has not been subject to much emprcal scrutny. Although Lal and Bell (2003) ncorporate usage levels, they operatonalze the 22 / Journal of Marketng, October 2007

5 varable as total spendng durng the entre course of a short-term loyalty program, whch, as mentoned prevously, provdes lmted foresght for the management of a contnuous loyalty program. Ths research remedes ths by usng consumers spendng levels durng an ntalzaton perod. The followng secton consders how dfferent ters of consumers change ther usage levels and exclusve loyalty over tme. Impact of Loyalty Programs on Usage Levels Most loyalty programs are desgned to encourage ncreased usage of a frm s products or servces. In general, the more a consumer buys, the more rewards he or she s lkely to earn. Thus, loyalty programs create an expectancy of postve outcomes assocated wth makng a purchase (Vroom 1964). When consumers realze that ther purchase behavor s nstrumental n achevng a postve outcome, they wll be more lkely to engage n the behavor (Latham and Locke 1991). From an operant condtonng pont of vew (Sknner 1953), rewards from loyalty programs serve as the condtonng stmulus to sustan desred behavor. In support of these vews, emprcal studes show that rewards can drect behavor and ncrease task performance (Esenberger and Rhoades 2001; Strohmetz et al. 2002). Thus, t s expected that loyalty programs wll postvely affect consumers usage levels, whch leads to the frst hypothess: H 1 : Consumers gradually ncrease ther usage level after jonng a loyalty program. In ths research, consumers usage levels are captured by two varables: purchase frequency and transacton sze. Purchase frequency s consdered an mportant predctor of consumers status wth a frm (Schmttlen, Morrson, and Colombo 1987; Venkatesan and Kumar 2003) and has been used n the past as an ndcator of loyalty program success (Bolton, Kannan, and Bramlett 2000; Sharp and Sharp 1997). However, transacton sze has rarely been ncluded n prevous studes. Because consumers can maxmze rewards by ncreasng how much they spend n a transacton and because ths amount s an essental part of ther value contrbuton to the frm, t s mportant to nclude transacton sze n the study of loyalty programs. Although usage levels can be measured as the total amount a consumer spends, studyng purchase frequency and transacton sze separately s deemed to be more approprate for two reasons. Frst, purchase frequency and transacton sze have dfferent mplcatons for a frm. Wth hgh fulfllment costs per order (e.g., due to payment processng or shppng costs), basket sze s an mportant determnant of the frm s proft margn. In comparson, for a frm n whch recency of purchase s an mportant predctor of future behavor, purchase frequency may be a more crtcal busness factor. Studyng purchase frequency and sze separately s more n lne wth such frms strateges. Second, reflectng the strategc dfference between purchase frequency and transacton sze, some loyalty programs reward consumers for frequency (e.g., rewardng a set number of ponts for each purchase), and others encourage larger purchases (e.g., by settng a mnmum transacton sze). Dfferentatng between purchase frequency and transacton sze allows for a more accurate assessment of these programs effects. 1 Methodwse, studyng ndvdual transacton sze nstead of total spendng ncreases the power of the analyss and reduces aggregaton bas. It also allows for the assessment of exclusve loyalty usng the relatonshp between transacton sze and nterpurchase tme, as s demonstrated subsequently. Moderatng Effect of Intal Usage Level The extent to whch consumers ncrease ther spendng because of the ncentves can depend on ther ntal usage levels. In the only exstng research that has studed the moderatng effects of consumer usage levels on loyalty program mpact, Lal and Bell (2003) fnd the least behavor change among the heavest spenders and more sgnfcant changes among low and moderate spenders. The current study extends these fndngs to contnuous loyalty programs, n whch sustaned behavor change, rather than temporary shock, s more lkely to be the goal. Furthermore, unlke Lal and Bell, who segment consumers accordng to ther spendng levels durng the entre duraton of a program, ths research uses consumers usage levels at the begnnng of the program, thus ncreasng the predctve usefulness of the fndngs. Two consderatons underle how dfferent consumer segments may respond dfferently to the same loyalty program. Frst, dependng on consumers exstng usage levels, a loyalty program may be appealng to varous degrees. If a consumer buys lttle from the frm, he or she wll need to wat a long tme for a reward. Thus, the consumer may not consder the program relevant. In contrast, heavy and moderate buyers have an effort advantage over lght buyers because they do not need to work that hard or to wat that long for the rewards (Kvetz and Smonson 2003). Ths effort advantage can enhance the perceved ft and attractveness of the loyalty program to such consumers. Although the dosyncratc ft theory has been confrmed on consumers ntenton to jon a loyalty program, t may contnue to nfluence consumers after program enrollment, especally for moderate buyers. For heavy buyers, ths nfluence of effort advantage on purchase behavor s more ambguous. The reward lterature suggests that when rewards do not offer enough challenge to task performance, they lack the motvatonal effect to nduce behavor change (Esenberger and Rhoades 2001). Because heavy buyers already obtan rewards easly at the current purchase level, they wll have less ncentve to ncrease ther efforts. The second consderaton n studyng ntal usage level as a moderator s the ultmate lmt of a consumer s demand for a product or servce, such as how much he or she travels (Dowlng and Uncles 1997). Motvaton notwthstandng, consumers wll rase ther usage level only f t s below ther consumpton lmt. Consequently, ths creates a celng effect that s most lkely to affect frequent buyers 1Although the partcular loyalty program studed n ths artcle ssues reward ponts based on the amount consumers spend at the store, studyng purchase frequency and transacton sze separately helps establsh baselne effects of the program on these two varables, wth whch future studes of other types of loyalty programs can be compared. The Long-Term Impact of Loyalty Programs / 23

6 because they already consume heavly (Lal and Bell 2003). Together, these consderatons suggest that moderate buyers should experence the greatest change n usage levels because they perceve effort advantage and hgher relevance of the program but are not as lkely to be subject to the celng effect as heavy buyers. Ths leads to the followng hypothess: H 2 : Moderate buyers usage levels ncrease faster than those of heavy and lght buyers after loyalty program enrollment. Impact of Loyalty Programs on Consumer Loyalty A man advantage of loyalty programs s ther ablty to ncrease swtchng cost (Km, Sh, and Srnvasan 2001). When consumers jon a loyalty program, to accumulate rewards more quckly, they are lkely to concentrate ther purchases on one frm, such as bookng all flghts through one arlne. Furthermore, because loyalty program members tend to overlook negatve experences wth the frm and are less lkely to compare the frm wth compettors (Bolton, Kannan, and Bramlett 2000), they are more lkely to buy exclusvely from the frm. In the long run, the ncrease n swtchng costs has mportant mplcatons for customer loyalty. Frst, the longer a consumer has been n a program, the more vested nterests he or she wll have n the program, and the more the consumer wll have at stake f he or she were to leave the frm. Ths creates a long-term customer lock-n (Sharp and Sharp 1997). Second, hgher swtchng costs mean that loyalty program members are less lkely to have extended experence wth compettors, further reducng ther ablty to wegh compettor comparson n ther decsons (Bolton, Kannan, and Bramlett 2000). Consequently, consumers are expected to become more loyal after jonng a loyalty program, whch leads to the next hypothess: H 3 : A frm s loyalty program members become more loyal to the frm over tme. Smlar to the change n usage levels, a loyalty program can nfluence dfferent consumers loyalty levels dfferently. At one end of the contnuum, lght buyers may not be motvated to become more loyal to a frm, because the loyalty program s not hghly attractve to them. At the other end, heavy buyers already can enjoy frequent rewards and thus do not have a strong ncentve to change ther behavor. Agan, moderate buyers are the most attractve target. These consumers perceve enough relevance and benefts from the program to change ther purchase behavor and shft ther purchases to one frm. Ths leads to the followng hypotheses: H 4 : Moderate buyers loyalty levels ncrease faster than those of heavy and lght buyers after loyalty program enrollment. Data and Methodology The Data The data used n ths study came from a convenence store chan s loyalty program. For the purpose of confdentalty, the name of the frm s not dsclosed here. The loyalty program allows consumers to earn ponts for every dollar they spend at the store. Ters of rewards, such as a bottle of soda, are related to the total number of ponts accumulated. The program rewards consumers an average of $1 n free products/servces for every $100 spent (.e., 1% reward rato), wth hgher-ter rewards carryng a hgher reward rato. Consumers need to enroll n the program to earn free rewards, but program membershp s free. A random sample of 1000 consumers was extracted from the program usng two crtera: (1) The consumer joned the loyalty program n ts frst year of operaton, and (2) the consumer made at least two purchases. The latter constrant ensures that there are meanngful data for every consumer. The data cover purchases durng the frst two years of the program, whch started n March Altogether, the sample made 42,788 purchases. The number of purchases made by a consumer ranged from 2 to 369, wth a medan of 25. The medan transacton sze was $ Modelng Purchase Frequency Consumers purchase frequency s modeled as Equaton 1, where Frequency m s the number of transactons by consumer n month m and Month m s the number of months consumer has been n the loyalty program at month m. Logarthmc transformaton of Month m s used to accommodate the noton that purchase frequency s unlkely to ncrease forever and wll gradually reach a maxmum pont that reflects consumpton lmt. 2 The model has a dummy varable LastMonth m, whch s set to 1 f month m s the last month of transacton by consumer and 0 f not. Ths varable s ncluded because relatonshp marketng lterature shows that consumers tend to reduce ther purchase frequency at the end of a relatonshp (Venkatesan and Kumar 2003). Thus, t s necessary to control for the effect of the last month s purchases when studyng the trend n purchase frequency. To allow for temporary nactvty, LastMonth m s set to 1 only f a consumer s prevous purchase occurred at least three months before the end of the analyss perod. (1) Frequency m = α 0 + α 1 Log(Month m ) + α 2 LastMonth m + ε m. Two-level herarchcal lnear modelng (HLM) s used to estmate the parameters n the model. Compared wth tra- 2Although analyss of supermarket purchase data often uses week as the tme unt of analyss, month s chosen as the ndependent varable here for three reasons. Frst, convenence store purchases occur less frequently than supermarket shoppng; ndustry data show that 68% of consumers shop at convenence stores about or less than once a week (Chanl 2004). Ths causes the number of data ponts n weekly ntervals to be too small and produces a dsproportonate number of zero frequences, whch can skew the results. Second, behavor changes due to a loyalty program are not expected to happen overnght, and usng month as the ndependent varable allows for a reasonable tme nterval to observe vsble behavor changes. Thrd, the convenence store chan regularly makes decsons on a monthly bass, whch renders monthly analyss meanngful from a practcal pont of vew. 24 / Journal of Marketng, October 2007

7 dtonal lnear regresson, HLM has two advantages. Frst, t does not requre ndependent observatons, as s often assumed n tradtonal regresson. Ths accommodaton of nonndependent observatons s mportant here because the purchase frequences for a consumer are lkely to be correlated across tme. Second, HLM allows the model coeffcents at lower levels to be randomly dstrbuted, thus accommodatng ndvdual heterogenety. Explanatory varables can be ncluded at hgher levels to explan such heterogenety. In the purchase frequency model, the regresson coeffcents n Equaton 1 for an ndvdual consumer are assumed to be normally dstrbuted across the sample, and the expected parameter values for each ndvdual consumer depend on whether the consumer s a lght, moderate, or heavy buyer. Equatons 2 4 show the second-level equatons used: (2) α 0 = β 0 + β 1 LghtBuyer + β 2 HeavyBuyer + μ 0, (3) α 1 = β 3 + β 4 LghtBuyer + β 5 HeavyBuyer + μ 1, and (4) α 2 = β 6 + β 7 LghtBuyer + β 8 HeavyBuyer + μ 2, where LghtBuyer and HeavyBuyer are two dummy varables ndcatng lght and heavy buyers, respectvely. Appendx A explans n detal how these and other varables were operatonalzed. To nterpret the coeffcents n the equatons, Equatons 2 4 can be substtuted nto Equaton 1, whch produces the followng: () 5 m = Frequency β β LghtBuyer β HeavyBuye r + β Log( Month ) 3 + β4log( Monthm) LghtBuyer + β Log( Month ) HeavyBuyer 5 where υ m = ε m + μ 0 represents random error. The ntercept β 0 shows the expected purchase frequency of a moderate buyer durng the frst month of the program (when Log[Month m ] = 0), the coeffcents for the LghtBuyer and HeavyBuyer dummy varables (β 1 and β 2 ) represent the dfferences n these consumers ntal purchase frequency compared wth that of moderate buyers, and β 3 s a key parameter that corresponds to the longtudnal effect of the program on moderate buyers purchase frequency. The change n behavor due to Month m s β 3 log(month m / Month m 1). Because consumers purchase frequences are hypotheszed to ncrease as a result of the program, β 3 should be postve. The rate of purchase frequency growth gradually slows down as Month m ncreases and log(month m /Month m 1) becomes smaller and eventually approaches zero. m m + β6lastmonth m + β7lastmonthm LghtBuyer + β8lastmonth HeavyBuyer m + μ Log( Month ) + μ LastMonth +υ m, 1 m 2 m The terms β 4 and β 5 represent the dfferental effects of the loyalty program on lght buyers and heavy buyers n comparson wth moderate buyers. Accordng to H 2, both lght and heavy buyers are not expected to ncrease ther usage levels as quckly. Ths mples a negatve value for both β 4 and β 5. The coeffcent for the LastMonth dummy varable, β 6, ndcates how much purchase frequency decreases durng a consumer s last month n the relatonshp. The μ 1 Log(Month m ) and μ 2 LastMonth m terms represent the unexplaned ndvdual heterogenety n purchase frequency change over tme and durng the consumer s last month as a customer. Modelng Transacton Sze and Exclusve Loyalty Tradtonally, researchers have evaluated consumers loyalty to a brand or store through ther brand-swtchng behavor. In realty, however, a frm often does not have nformaton on ts customers purchases other than those related to ts own products. To solve ths problem, ths research adopts an approach smlar to that of Boatwrght, Borle, and Kadane (2003) that examnes the relatonshp between nterpurchase tme and transacton sze. The basc premse for ths approach s that nterpurchase tme and transacton sze should have a proportonal relatonshp for regularly purchased products. For the same consumer, the longer he or she wats before makng a purchase, the more he or she wll need to buy n that shoppng trp. For example, f a consumer who normally buys groceres once a week must wat for two weeks nstead, he or she wll lkely need to buy twce the amount. If the consumer s loyal to one store, ths proportonal relatonshp should be strong. However, f the consumer frequents multple stores, the observed purchases from a sngle store are not lkely to reflect such a systematc relatonshp. Mathematcally, a three-level HLM (see Appendx B) s used to model loyalty and transacton sze. Frst, the amount consumer spent n the kth transacton n quarter j (Sze jk ) s modeled as a functon of the elapsed tme snce the prevous transacton, or nterpurchase tme (IPT jk ). As dscussed prevously, wth exclusve loyalty, the expected value of ρ j1 should be close to 1. 3 If lttle loyalty exsts, ρ j1 should be 3Assumng constant total demand from the consumer and perfect loyalty to the store, there should be a proportonal relatonshp between nterpurchase tme and transacton sze. In other words, f nterpurchase tme s doubled and two shoppng trps are combned nto one, the amount spent n the trp should also double. Mathematcally, accordng to Equaton B1, f the nterpurchase tme changes by a factor of α, the new transacton sze (NewSze jk ) wll be exp[ρ j0 + ρ j1 log(α IPT jk )+ ς jk ]. Thus, NewSze jk /Sze jk = α ρ j1. In a perfectly proportonal relatonshp, NewSze jk /Sze jk should be equal to α, whch means that ρ j1 should be equal to 1. If a consumer s totally dsloyal or f total demand s unstable, the change n nterpurchase tme may have no mpact on transacton sze at all, and ρ j1 wll be equal to 0. Ths does not mean that ρ j1 s bound between 0 and 1. Mathematcally, ρ j1 can be larger than 1 or smaller than 0. If ρ j1 s larger than 1 for example, f t equals 2 doublng nterpurchase tme wll result n the quadruplng of transacton sze. Although such stuatons may exst, logcally, t s more lkely to be the excepton rather than the norm. It s also possble for ρ j1 to be negatve, whch sug- The Long-Term Impact of Loyalty Programs / 25

8 close to 0. At the second level, the Level 1 ntercept and slope are assumed to depend on the quarter n whch the transacton occurred (Quarter j ). Here, the unt of quarter nstead of month s used to accommodate consumers wth lower purchase frequences and to ensure more accurate estmates based on a stable trend. At the thrd level, consumer heterogenety s captured by allowng Level 2 parameters to depend on a consumer s ntal usage ter. Equaton B8 n Appendx B combnes all three levels of equatons. For transacton sze change due to the loyalty program, the relevant parameters are λ 0 λ 5. The parameters λ 0, λ 1, and λ 2 are related to the ntercept term n Equaton B8, whch reveals consumers baselne transacton szes at the begnnng of the program, assumng daly transactons (.e., IPT = 1), and the parameters λ 3 λ 5 have to do wth the changes n transacton sze over tme for dfferent segments of consumers. Specfcally, λ 3 refers to the changes n moderate buyers transacton sze because of the loyalty program, and λ 4 and λ 5 ndcate the moderatng effect of ntal usage levels on the change n transacton sze. For consumer loyalty, the parameters of central nterest are λ 6 λ 11. The parameters λ 6 λ 8 show the loyalty of the three consumer segments durng the frst quarter n the program. The parameters λ 9 λ 11 reflect the longtudnal change n consumer loyalty. As H 3 predcts, consumer loyalty should rse after program enrollment. Thus, λ 9 should be postve, reflectng the ncrease n loyalty for moderate buyers. In contrast, λ 10 and λ 11 should be negatve, ndcatng the slower ncrease n loyalty for lght and heavy buyers. Note that though convenence store purchases occur less regularly than supermarket purchases, ndustry statstcs show that many consumers shop at convenence stores consstently and extensvely (General Electrc Captal Franchse Fnance Corporaton 2001). In addton, the store chan from whch the data were obtaned sells both fuel and convenence store tems. The regular nature of fuel purchase adds regularty to the sample s purchases. Mathematcally, the model s estmated wth entre quarters of transactons, whch helps smooth out the randomness n purchase patterns and makes the comparson across tme meanngful. Data Analyss Overvew Both the purchase frequency model and the loyalty/transacton sze model were ftted usng HLM6. The standard HLM assumes that hgher-level unts (n ths case, consumers) are drawn from the same populaton, whch mples homogeneous error varance at Level 1. Ths assumpton may not be realstc here, because dfferent segments of consumers may behave dfferently. To accommodate such consderatons, the assumpton can be relaxed to allow for heterogeneous Level 1 error varance and to nclude predctor varables to explan the heterogenety (Raudenbush and Bryk 2002). Thus, two specfcatons were ft for each of gests that an ncrease n nterpurchase tme results n a decrease n transacton sze. Agan, ths s lkely to be an excepton to what s usually observed n the marketplace, gven that purchases occur relatvely often. the man models, one wth homogeneous varance and the other wth heterogeneous varance. Gven the prevous dscusson on the effects of ntal usage levels, the two dummy varables for consumer ters (HeavyBuyer and LghtBuyer ) were used to explan Level 1 error varance. The results show that the heterogeneous varance specfcaton outperformed the homogeneous varance specfcaton for both models, suggestng the exstence of heterogeneous error varance at Level 1. However, the parameter estmates from the two specfcatons dd not dffer substantvely from each other. The results are based on heterogeneous error varance specfcaton. Results Change n Purchase Frequency The maxmum lkelhood estmates of the purchase frequency model and ts goodness of ft appear n Table 1. The HLM does not produce an R-square as n tradtonal regresson. However, t yelds a devance statstc, whch equals 2 tmes the value of the log-lkelhood functon and can be used to evaluate alternatve models (Raudenbush and Bryk 2002). As Table 1 shows, compared wth an uncondtonal model that does not have any explanatory varables, the proposed purchase frequency model shows a sgnfcantly better ft (χ 2 = , p <.001). Recall that the model ntercept represents expected purchase frequency of moderate buyers durng the frst month, whch was 2.59 tmes (p <.001). The coeffcents for the LghtBuyer and HeavyBuyer dummy varables (β 1 and β 2 ) ndcate the dfferences n these consumers ntal purchase frequences compared wth moderate buyers. Thus, they serve as a check of the correct classfcaton of consumers. Consstent wth ther segmentaton, lght buyers ntal purchase frequency was.93 tmes lower than that of moderate buyers (p <.001), and the average ntal frequency for heavy buyers was 3.00 tmes hgher (p <.001). H 1 and H 2 predct that consumers purchase frequences wll gradually ncrease and that ths ncrease wll be fastest for moderate buyers. The results show a postve coeffcent for Log(Month) (β 3 =.26, p =.002), suggestng that moderate buyers purchased more frequently over tme. At the end of the two years, the average purchase frequency for moderate buyers was 4.42 tmes, nearly doublng ther ntal frequency. Consstent wth H 2, the ncrease n purchase frequency for moderate buyers was sgnfcantly hgher than that for heavy buyers (β 5 =.32, p =.008). The combned effect of Log(Month) for heavy buyers was nonsgnfcant (β 3 + β 5 =.05, p =.48), suggestng an unchanged purchase frequency for these consumers. Ths s further confrmed by the fndng that heavy buyers average frequency at the end of the perod (5.68 tmes) was vrtually unchanged from the ntal frequency. The combned effect of Log(Month) for lght buyers was.34. Contrary to H 2, the Log(Month) LghtBuyer nteracton was nonsgnfcant, ndcatng that lght buyers showed the same level of ncrease n purchase frequency as moderate buyers. Ther purchase frequency more than doubled to 3.73 tmes at the end of the perod. These ncreases n purchase frequency by lght and moder- 26 / Journal of Marketng, October 2007

9 TABLE 1 Model Results Purchase Loyalty/Transacton Reward Clam Frequency Model Sze Model Model Intercept 2.59*** (.13) 1.43*** (.04).32*** (.03) LghtBuyer.93*** (.18).48*** (.09).17*** (.04) HeavyBuyer 3.00*** (.20).38*** (.07).25*** (.04) Log(Month).26*** (.08) Log(Month) LghtBuyer.08 n.s. (.11) Log(Month) HeavyBuyer.32*** (.12) LastMonth 1.21*** (.19) LastMonth LghtBuyer.46* (.26) LastMonth HeavyBuyer 1.12*** (.36) Log(Quarter).16** (.07).13*** (.02) Log(Quarter) LghtBuyer.07** (.03).01 n.s. (.03) Log(Quarter) HeavyBuyer.14*** (.05).06** (.03) Log(IPT).15*** (.03) Log(IPT) LghtBuyer.05** (.02) Log(IPT) HeavyBuyer.21*** (.08) Log(IPT) Log(Quarter).10*** (.04) Log(IPT) Log(Quarter) LghtBuyer.03* (.02) Log(IPT) Log(Quarter) HeavyBuyer.09*** (.02) Devance ( 2 log-lkelhood) 50, , χ *** *** *** d.f *p.10. **p.05. ***p.01. Notes: The numbers n parentheses are standard errors of the estmates. The ch-square statstc compares the devance of the estmated model wth an uncondtonal model that does not contan predctor varables at any of the levels. n.s. = not statstcally sgnfcant. ate buyers are especally mpressve n lght of ndustry statstcs showng that more than two-thrds of shoppers frequent convenent stores about or less than once a week (Chanl 2004). In other words, after two years, most of the sample became the top thrd of all convenence store shoppers n terms of purchase frequency. Fgure 1, Panel A, dsplays the observed average purchase frequences of the three consumer segments for each month. For both lght and moderate buyers, the most vsble jump n purchase frequency occurred wthn three months of jonng the loyalty program. These hgher frequences sustaned and steadly ncreased at a slower pace after the frst three months. In contrast, heavy buyers purchase frequency remaned mostly flat durng the analyss perod. Pared comparson tests suggest that at the end of the two years, there was stll a sgnfcant dfference n observed purchase frequences between lght buyers and heavy buyers (t = 1.99, p =.05). However, there was no sgnfcant dfference between lght and moderate buyers or between moderate and heavy buyers. Overall, H 1 and H 2 are partally supported for purchase frequency. Change n Transacton Sze The loyalty/transacton sze model showed a sgnfcantly better ft than an uncondtonal model (χ 2 = , p <.001). The model estmates appear n Table 1. Recall that the logarthmc transformaton of transacton sze s used n the model. Thus, the expected begnnng daly transacton sze by moderate buyers should be e λ0, and the baselne sze for lght and heavy buyers should be exp(λ 0 + λ 1 ) and exp(λ 0 + λ 2 ), respectvely. The results show a baselne transacton sze of $4.18 for moderate buyers (λ 0 = 1.43, p <.001). Consstent wth ther classfcatons, heavy buyers ntally spent $1.93 more n a transacton than moderate buyers (λ 1 =.38, p <.001), and lght buyers ntal daly transacton sze was $1.59 less than moderate buyers (λ 2 =.48, p <.001). The observed average transacton szes for these consumers appear n Fgure 1, Panel B. Consstent wth H 1, moderate buyers spent more n a transacton over tme (λ 3 =.16, p =.03) and had an average observed transacton sze of $20.11 at the end of the analyss perod. Agan, heavy buyers transacton sze dd not ncrease as fast as moderate buyers (λ 5 =.14, p =.006), and the combned coeffcent for Log(Quarter) was nonsgnfcant (λ 3 + λ 5 =.02, p =.76), suggestng that heavy buyers dd not spend more n a purchase after they joned the loyalty program. Surprsngly, lght buyers showed a faster ncrease n transacton sze than moderate buyers (λ 4 =.07, p =.03). Ther average observed transacton sze ncreased to $11.29 at the end. Ths contradcts H 2, whch predcts a slower ncrease for lght buyers. For all three segments, the average transacton sze far exceeded the ndustry average of $7.60 per transacton (Chanl 2004). To explore further the source of lght buyers transacton sze ncrease, an addtonal analyss was performed on ther shoppng basket composton. The convenence store chan The Long-Term Impact of Loyalty Programs / 27

10 FIGURE 1 Observed Changes n Consumer Purchases A: Observed Average Monthly Purchase Frequences B: Observed Average Transacton Szes sells products n two major categores: fuel and store merchandse. Durng ther frst quarter n the program, only 26% of lght buyers bought both fuel and store merchandse, and the other 74% bought only ether fuel or store merchandse. By the end of the two years, the percentage of double-category buyers ncreased to 58%. The percentage of transactons that ncluded both fuel and store merchandse also ncreased from 20% to 40%. Ths ncluson of more product categores n one purchase explans why lght buyers spent more n a transacton. Although these consumers may not ntally experence a strong ncentve n the loyalty program, they were able to dversfy ther purchases nto more categores and thus make the program more attractve. 28 / Journal of Marketng, October 2007

11 Exclusve Consumer Loyalty Estmates related to consumer loyalty appear n Table 1. Recall that the more loyal a consumer s to the store, the closer the coeffcent of Log(IPT jk ) (herenafter, the loyalty parameter ) should be to 1. At the begnnng, the loyalty parameter for moderate buyers was.15 (p <.001), ndcatng relatvely low loyalty. Lght buyers were even less loyal, wth the loyalty parameter lower by.05 (p =.035). Heavy buyers were most loyal among the three groups, wth the loyalty parameter hgher than that of moderate buyers by.21 (p =.007). The dynamc change n the loyalty parameter s of central nterest. As hypotheszed, moderate buyers loyalty ncreased sgnfcantly (λ 9 =.10, p =.005). Consstent wth H 4, the ncrease n loyalty among heavy buyers was slower than that for moderate buyers (λ 11 =.09, p <.001). A nonsgnfcant combned coeffcent for heavy buyers (λ 9 + λ 11 =.01, p =.99) suggests that these consumers loyalty levels dd not change durng the analyss perod. Conversely, lght buyers experenced a larger ncrease n exclusve loyalty than moderate buyers (λ 10 =.03, p =.07). Ths contradcts H 4, whch predcts that moderate buyers wll experence a faster ncrease n loyalty than lght buyers. Overall, H 4 s only partally supported. Alternatve Explanatons Learnng Effect An alternatve explanaton for the fndngs s that lght buyers may be new customers who learn to spend more over tme as they become more famlar wth the store. In contrast, heavy buyers may already be long-term loyal customers and do not experence the same ncremental learnng. Thus, the dfferences among the consumer segments may be due to a learnng effect rather than dfferent reactons to the loyalty program. To rule ths out, consumers reward redempton was examned. For ths program, to receve a reward, consumers needed to request a certfcate from the store, whch could then be redeemed for the reward. In realty, only some consumers wll make the effort to request ther reward certfcates (Lal and Bell 2003). Because all three consumer segments were exposed to the loyalty program at the same tme, there s no learnng advantage on reward clam for any segment, and thus any systematc dfferences should truly reflect ther dverse responses to the program. Exstng research shows that reward redempton tends to be the hghest among heavy buyers (Lal and Bell 2003). The followng analyss retests such fndngs and examnes longtudnal change n reward redempton behavor of loyalty program members, whch has yet to be answered by exstng research. Smlar to purchase frequency, consumers reward clam behavor was modeled wth a two-level HLM model, as shown n Equatons 6 8. The man dependent varable s RCRate q, whch s the number of reward certfcates requested as a percentage of the rewards consumer qualfed for n the qth quarter. It s modeled as a functon of a logarthm transformaton of the correspondng quarter (Log[Quarter ]). Here, the unt of quarter nstead of month s used because of the relatvely nfrequent reward ssuance. Agan, the consumers ntal usage levels were entered as explanatory varables at the second level. Equaton 9 shows the combned model: () 6 RCRate = γ + γ Log( Quarter ) + π, ( 7) 8 q 0 1 q j γ 0 = θ 0 + θ 1 LghtBuyer + θ 2 HeavyBuyer + τ 0 γ = θ + θ LghtBuyer + θ HeavyBuyer () τ 1,and (9) RCRateq = θ0 + θ1lghtbuyer + θ2heavybuyer + θ Log( Quarter ) 3 + θ Log( Quarter ) LghtBuyer 4 Ths two-level HLM model was estmated wth heterogeneous error varance at Level 1, and the results appear n Table 1. The devance of the model was , showng a sgnfcantly better ft than an uncondtonal model (χ 2 = , p <.001). The ntercept estmate ndcates that moderate buyers had an ntal reward clam rate of 32.22% (p <.001). Consstent wth Lal and Bell (2003), heavy buyers ntally clamed 25.42% more of ther rewards than moderate buyers (p <.001), and lght buyers ntal reward clam rate was 16.71% lower than that of moderate buyers (p <.001). The results on the longtudnal change n consumers reward clam behavor revealed notable patterns. In two years, moderate buyers reward clam rate ncreased to 59.79% n the eghth quarter (θ 3 =.13, p <.001). In contrast, heavy buyers reward clam rate dd not ncrease as fast (θ 4 =.06, p =.029). Lght buyers showed the same level of ncrease n reward clam rate as moderate buyers (θ 5 =.01, p =.563). Because clamng a reward s an extra step that consumers need to take to reap the benefts of the loyalty program, ther decson to do so reveals ther level of nterest n the loyalty program. Gven ther hgh spendng levels and, thus, the ablty to obtan many rewards, t s not surprsng that heavy buyers had the hghest nterest n rewards. In the meantme, the faster ncrease n lght and moderate buyers reward clam rates shows a rsng nterest n the loyalty program from these consumers. As these consumers change ther purchase behavor to make the loyalty program more proftable for them, they gradually become more nvested and nterested n the program. Hgher reward clam rates further allow them to beneft more from the program, formng a vrtuous cycle that provdes more ncentve for these consumers to become better customers. Because reward clam behavor s not subject to the same learnng dfferences among consumer segments as purchase behavor, these fndngs support the concluson that the change n behavor of the three segments was ndeed a result of the loyalty program. q q + θ Log( Quarter ) HeavyBuyer + τ 5 1 q Log( Quarter q ) + τ 1 + π q., The Long-Term Impact of Loyalty Programs / 29

12 Consumer Attrton Another alternatve explanaton for the current fndngs s consumer attrton. It s natural that some consumers would drop out durng the two years. Because hgh-value consumers who consder the program attractve are more lkely to stay, ths creates a self-selecton effect. Consequently, the trends found may be a result of a sample composton change toward a denser concentraton of hgh-value consumers rather than a result of the program. To rule out ths alternatve account, the models were reestmated wth only data from consumers who were stll wth the frm at the end of the two years. The model estmates appear n Table 2. The patterns of fndngs from ths actve consumer group are smlar to those from the entre sample. That s, lght and moderate buyers exhbted postve change n purchase behavor/loyalty, whereas heavy buyers dd not. Not surprsngly, the extent of change by lght and moderate buyers was even more promnent n ths actve consumer group because they were lkely to perceve the program as more valuable. In contrast, the heavy buyers n ths group mantaned the same no-change pattern, as the estmates from the entre sample suggest. Overall, after sample composton change was controlled for, the substantal fndngs stll reman the same, suggestng that consumer attrbuton s not the reason for the fndngs. Store-Level Trends The trends dscovered n ths research may also be attrbuted to other factors concurrent wth but unrelated to the loyalty program, such as sales promoton. The most desrable way to rule out such extraneous factors s to nclude non loyalty program members as a control group and compare ther behavor changes over the same perod. Unfortunately, the frm does not track ndvdual purchases of ts non loyalty program members, makng data about such a control group unavalable. However, t records companylevel total sales and overall number of transactons. Ths allows for the dervaton of the total spendng and number of transactons made by all non loyalty program members as a whole. Although comparson of ndvdual consumer behavor s stll mpossble wth such data, t s possble to study the trends n all loyalty program members versus nonmembers purchase behavor as two aggregated unts. In the sprt of pror analyss of purchase frequency and transacton sze, the trends n total number of transactons per month (TotalTransactons ) and average transacton sze by members versus nonmembers (AvgSze ) are examned wth the followng regresson equatons: ( ) 10 = alogm ( onth) + a3log( Month) Loyalty + ζ, and 2 TotalTransactons a a Loyalty ( ) AvgSze = b + b Loyalty + b Log( Month ) blog( Month ) Loyalty + ξ, 3 where Loyalty s a dummy varable that equals 1 for loyalty program member group and 0 for the nonmember group, and ζ and ξ are errors. Although these equatons are not n herarchcal forms, they resemble pror models n that the dependent varables are also a functon of the number of months snce the start of the program (.e., Log[Month ]). If loyalty program members behavor change was ndeed due TABLE 2 Model Estmates wth Actve Consumers Only Frequency Model Loyalty/Transacton Sze Model Intercept 2.69*** (.22) 2.23*** (.08) LghtBuyer.95*** (.34).62*** (.14) HeavyBuyer 3.22*** (.30).32*** (.10) Log(Month).55*** (.13) Log(Month) LghtBuyer.03 n.s. (.20) Log(Month) HeavyBuyer.54*** (.18) Log(Quarter).27*** (.04) Log(Quarter) LghtBuyer.05*** (.02) Log(Quarter) HeavyBuyer.26*** (.05) Log(IPT).22*** (.03) Log(IPT) LghtBuyer.11*** (.05) Log(IPT) HeavyBuyer.14*** (.04) Log(IPT) Log(Quarter).17*** (.05) Log(IPT) Log(Quarter) LghtBuyer.05*** (.02) Log(IPT) Log(Quarter) HeavyBuyer.15*** (.03) Devance ( 2 log-lkelhood) 31, , χ *** *** d.f *p.10. **p.05. ***p.01. Notes: The numbers n parentheses are standard errors of the estmates. The ch-square statstc compares the devance of the estmated model wth an uncondtonal model that does not contan predctor varables at any of the levels. n.s. = not statstcally sgnfcant. 30 / Journal of Marketng, October 2007

13 to the program, there should be a larger ncrease n these consumers purchases than n nonmembers purchases. That s, the coeffcents for the Loyalty Log(Month ) nteracton term should be postve and sgnfcant. Ths was confrmed for both the number of transactons per month (a 3 = 1.14, p <.001) and the average transacton sze (b 3 =.83, p <.001). 4 Consstent wth these results, the number of purchases that loyalty program members made ncreased from 4.98% to 8.11% of total transactons by the end of the two-year perod. When ths rato s calculated for dollar sales, loyalty program members accounted for 73.66% of total sales at the begnnng of the program, whch ncreased to 88.91% after two years. In contrast wth the transacton rato, the dollar amount rato suggests that these consumers spent much more n each transacton than nonmembers. Regresson analyss wth these two ratos as the dependent varables and Log(Month ) as the ndependent varable showed sgnfcant, postve trends over tme. Together, these fndngs suggest that loyalty program members exhbted more postve trends than nonmembers, provdng further support that the program caused the purchase ncrease beyond other factors that may exst n the envronment. Conclusons Ths research examnes the mpact of a loyalty program on consumers purchase behavor over a two-year perod. It extends pror studes by explctly modelng the dynamc change n consumers spendng levels and ther behavoral loyalty to the store. The results suggest that dependng on consumers ntal usage levels, the loyalty program had dfferent effects on ther behavor. Consumers who were heavy buyers at the begnnng of the program were most lkely to clam the rewards they earned and thus benefted the most from the program. However, ther spendng levels and exclusve loyalty to the store dd not ncrease over tme. In contrast, the loyalty program had postve effects on both lght and moderate buyers purchase frequences and transacton szes, and t made these consumers more loyal to the store. The most vsble change for these two segments occurred wthn three months of jonng the program, and the growth contnued at a steady but slower pace n the followng months. At the end of the analyss perod, these consumers average purchase frequences were not statstcally dfferent from that of an adjacent ter. Ths supports the argument that loyalty programs can accelerate consumers loyalty lfe cycle and make them more proftable customers (O Bren and Jones 1995). The dverse responses from consumers suggest a need to consder consumer dosyncrases when assessng the mpact of loyalty programs. By ther very nature, loyalty 4Note that the sample sze for the regressons s small (24). Thus, the power of the analyss s lmted. Furthermore, because of the lack of ndvdual data for nonloyalty consumers, t s not possble to derve the loyalty parameter or to account for possble changes n sample composton over tme for these consumers. programs are one-to-one programs. How much a consumer can beneft from such a program depends on hs or her nvestment n the relatonshp wth the frm. However, ths one-to-one nature of loyalty programs has not been thoroughly examned n exstng research. A surprsng fndng from the current research s that consumers who started wth low usage levels changed ther behavor as much as or more than moderate and heavy buyers. Ths contradcts the commonly held belef that lght buyers are less-than-deal targets for loyalty programs and that they wll not perceve much value n the program (Dowlng and Uncles 1997; O Bren and Jones 1995). In the current case, the loyalty program dd not ntally appear very attractve to lght buyers. However, these consumers dversfed ther purchases and branched nto the frm s other servce areas. By clamng a hgher porton of rewards, they also gradually nvested more efforts nto the program. Through these measures, the opportunty for these consumers to beneft from the loyalty program ncreased, further motvatng them to spend more and patronze the store more exclusvely. Lmtatons and Further Research Ths study has several lmtatons that need to be addressed n further research. For example, ths study examned loyalty program members behavor wthout a control group of consumers who dd not enroll n the program. Although the use of longtudnal data reduces the self-selecton bas that often complcates cross-sectonal analyss of loyalty programs (Leenheer et al. 2003), the lack of control leaves the possblty that extraneous factors produced the trends rather than the loyalty program, such as concurrent marketng actvtes or envronmental factors. Although measures were taken to address several alternatve explanatons, they do not cover the full range of ssues. A complete test of loyalty program effects s needed n the future, whch should use longtudnal data from both loyalty program members and nonmembers. Such a comparson of trends between the two groups wll reveal more precse loyalty program effects. More comprehensve tests of loyalty program effects should also go beyond spendng levels and purchase tmng to nclude brand choce (Gupta 1988) and atttudnal loyalty (Olver 1999). When nterpretng the current fndngs, t s necessary to keep n mnd that the results are bound by the context and structure of the program studed and thus may not generalze to other programs. In realty, the performance of dfferent loyalty programs vares. It s mportant to understand why some programs acheve ther goals whereas others fal to do so (Bolton, Kannan, and Bramlett 2000). Several factors are proposed n the lterature, such as the effort requred to earn rewards (Kvetz and Smonson 2002) and the convenence of partcpatng n the program (O Bren and Jones 1995). Further research should test how these and other factors can affect a program s effectveness. Wthn a loyalty program, the effects of consumer self-segmentaton also need to be examned. A partcularly worthwhle topc s how reward redempton behavor nteracts wth purchase behavor to moderate the nfluence of a loyalty program. Exstng studes have begun to examne ths n the context The Long-Term Impact of Loyalty Programs / 31

14 of short-term loyalty programs (Lal and Bell 2003; Taylor and Nesln 2005). However, more research s needed to study ths ssue n the context of contnuous loyalty programs. At the frm level, the performance of a loyalty program may depend on the frm s other marketng actvtes, such as sales promoton. Future studes should examne the nteracton between loyalty programs and other CRM technques and marketng actvtes n enhancng consumers relatonshps wth a frm. Research n ths area wll provde theoretcal and manageral gudance on formulatng the most effectve CRM strategy. Another lmtaton of ths research s the lack of compettve nformaton. As a result, t reled on the proportonal relatonshp between transacton sze and nterpurchase tme to nfer consumer loyalty. Although ths s a useful way to assess loyalty when data are lmted, t can be subject to other nfluences, such as stockplng and cherrypckng behavor. The more rregular nature of convenence store purchases can also make the relatonshp more tenuous than t would be n more regular purchase scenaros. Thus, when compettve nformaton s avalable, share of wallet s stll a better way to assess consumer loyalty. Relatedly, further research should go beyond a sngle program to examne the market dynamsm of loyalty programs because multple frms often compete wth one another through ther loyalty programs. How does the ntroducton of new loyalty programs nfluence the effectveness of exstng programs? Does order of entry affect loyalty programs performances? These are all mportant questons for further research. Fnally, ths research used HLM to accommodate correlated observatons wthn each consumer and estmated two separate models for transacton sze and purchase frequency. However, because consumers may make purchase tmng and quantty decsons smultaneously, the two models may actually be related models. As a result, modelng the two decsons separately can lead to neffcent and based model estmates (Krshnamurth and Raj 1988). The amount of bas depends on the degree of smultanety between the two decsons and the level of correlaton between the two models error terms (Leeflang et al. 2000). Ths ssue can be remeded n future studes through alternatve modelng technques, such as multvarate Tobt II models n a smultaneous equatons approach (Kamakura and Wedel 2001; Leenheer et al. 2003). Manageral Implcatons The loyalty program s an mportant form of CRM strategy. It s costly to ntate and mantan and often requres a frm s long-term commtment. For many frms, a loyalty program s consdered a defensve marketng mechansm, used to keep a core group of best customers from defectng. Ths s especally the case when competng loyalty programs are offered n the same market. Because the best customers often are already heavy buyers of a frm s products and servces, the possblty of obtanng addtonal revenues from these consumers s low. Thus, when treated as a defensve strategy, loyalty programs are almost purely cost tems used to prevent potental sales loss. In contrast to ths tradtonal vew, the results from the current research show that lght and moderate customers enrolled n the loyalty program ncreased ther value contrbuton and accelerated ther relatonshp lfe cycle wth the frm, turnng the program nto much more than a passve loss-preventon nstrument. These fndngs suggest a need for managers to expand ther mentalty toward loyalty programs beyond mere reactve tactcs. The addtonal sales from the lght and moderate buyers n ths study came from two sources: (1) concentraton of purchases orgnally scattered at other frms and (2) expanson of the relatonshp wth the frm nto other busness areas. These fndngs suggest a few prerequstes for the success of a loyalty program as a more actve marketng tool. Frst, the lower spendng among lght and moderate users should be manly due to polygamous loyalty (.e., flyng on multple arlnes) rather than nsuffcent need for the product/servce category (.e., nfrequent need to travel). If most consumers are not spendng much because of low absolute demand, a loyalty program s unlkely to have a sgnfcant mpact. Second, the loyalty program structure should be set n such a way that t creates enough ncentve for lght and moderate buyers to strve for the rewards. In other words, the possblty of obtanng a reward should not be so remote that these consumers smply gve up and dsmss the program as rrelevant to them. From ths perspectve, smaller but easer-to-acheve rewards are lkely to be more effectve than larger rewards that requre a sgnfcant amount of effort. Fnally, ganng addtonal sales through a loyalty program s more lkely when a frm has multple busness areas that t can cross-sell to consumers, such as n the case of retal and fnancal ndustres. The avalablty of such addtonal venues for consumers to accumulate program ponts can make better use of consumers creatve mnds and nvolve them n deeper, more extensve relatonshps wth the frm. Across consumer segments, a loyalty program redstrbutes revenues and costs among consumers. The eventual beneft of the program depends on the trade-off between the costs of rewards (a majorty of whch wll be ncurred as a result of heavy buyers, who wll enjoy the free rewards wthout changng ther behavor) and the ncreased profts from moderate and lght buyers as they purchase more and become more loyal over tme. In hghly compettve markets n whch loyalty programs are wdely used (.e., the arlne ndustry), consumers may come to expect loyalty programs as a standard offerng from each frm. When ths happens, the cost of a loyalty program can become an essental cost of busness, creatng a compettve equlbrum n whch consumers are redstrbuted among competng frms. The ultmate beneft for the frm s the ablty to attract repeat busness and to have more proftable loyal customers. From an analytcal standpont, a loyalty program can produce rch data about customers, whch should be used to enhance a frm s relatonshp marketng efforts. The varaton n dfferent consumer segments responses to the loyalty program found here suggests a need to assess the effects of loyalty programs beyond overall sales mpact. Ths research demonstrates a smple way of evaluatng loy- 32 / Journal of Marketng, October 2007

15 alty program effects usng ndvdual purchase data. The HLM method better accommodates heterogenety and correlated observatons than tradtonal regresson, wthout ncurrng substantal executon costs. The models can also be easly adapted by addng other consumer varables, such as demographcs, nto the consumer-level equatons. As frms often have only nternal transacton data, the exclusve loyalty model presented here can be useful n assessng consumers behavoral loyalty usng lmted data. It s approprate for stuatons n whch consumer purchases occur frequently and only nternal data are avalable, such as consumers patronage choces at a supermarket. For such hgh-frequency purchases, retrospectve self-report data tend to be hghly naccurate (Schacter 1999). The current model s easy to compute and does not requre knowledge of where and to what extent consumers buy outsde the frm, thus makng t practcal wth mnmal data or computatonal requrements. However, a precauton s that the loyalty parameter can be affected by extraneous factors, such as stockplng and subsequent consumpton rate change (Sun 2005). Thus, t should be used for product categores wth relatvely stable consumpton. It s not sutable when the sample sze s relatvely small or the data tme duraton s relatvely short. Appendx A Varable Operatonalzaton Purchase frequency (Frequency m ): Total number of purchases made by a consumer n a certan month. If consumer dd not purchase anythng durng month m but that month s not the last month of the relatonshp (see the subsequent explanaton for the LastMonth m varable), Frequency m s set to 0 to ndcate zero purchase frequency. Transacton sze (Sze jk ): Total dollar amount spent n a transacton. Month (Month j, Month m ): Number of months snce jonng the loyalty program. Quarter (Quarter j, Quarter q ): Number of quarters snce jonng the loyalty program. Consumer ters (HeavyBuyer, LghtBuyer ): Classfcaton of a consumer based on hs or her total spendng durng the frst month of the program. Consumers n the top, mddle, and bottom thrds were classfed as heavy, moderate, and lght buyers, respectvely. Interpurchase tme (IPT jk ): Number of days between the pror purchase and the current purchase. Last month n the program (LastMonth m ): A dummy varable ndcatng the last month of transactons a consumer made. Ths varable s set to 1 f (1) the prevous transacton conducted by consumer occurred n month m and (2) month m was at least three months before the end of the analyss perod, offerng a three-month lapse wndow before labelng a consumer as dead. If consumer made any purchase after month m or f month m s wthn three months of the end of the analyss perod (to allow temporary nactvty), the Last- Month m varable s set to 0. Reward clam rate (RCRate q ): The percentage of rewards that consumer clamed n the qth quarter; ths equals the rato of the number of reward certfcates requested to the total number of rewards qualfed for n that quarter. Appendx B Three-Level HLMs of Exclusve Consumer Loyalty Level 1 ( B ) Log( Sze ) = ρ + ρ Log( IPT ) + ς. 1 jk j0 j1 jk jk Level 2 ( B2) ρ = φ + φ Log( Quarter ) + ϖ,and Level 3 j0 0 1 j j0 ( B3) ρ = φ + φ Log( Quarter ) + ϖ. j1 2 3 j j1 ( B ) φ = λ + λ LghtBuyer + λ HeavyBuyer + π, ( B ) φ = λ + λ LghtBuyer + λ HeavyBuyer + π, ( B ) φ = λ + λ LghtBuyer + λ HeavyBuyer + π ( B7) φ = λ + λ LghtBuyer + λ HeavyBu yer + π Interpretve Equaton (Combnng All Levels nto a Sngle Equaton) ( B ) Log( Sze ) = λ + λ LghtBuyer + λ HeavyBuyer 8 jk λ Log( Quarter ) + λ Log( IPT ) 3 j 6 + λ LghtBuyer 4 where Sze jk s the dollar amount consumer spent n the kth transacton durng the jth quarter n the program, IPT jk s the number of days snce the prevous transacton, Lght- Buyer and HeavyBuyer are dummy varables that ndcate whether consumer s a lght buyer or a heavy buyer, Log(Quarter j ) s the logarthm transformaton of the jth quarter, π 0 + π 1 Log(Quarter j ) + π 2 Log(IPT jk ) + π 3 Log(Quarter j ) Log(IPT jk )+ ϖ j1 Log(IPT jk ) ndcate the random effects of these varables that are not systematcally accounted for by the fxed coeffcents, and ϖ j0 and ς jk are error terms. jk Log( Quarter ) + HeavyBuyer Log( Quarter ) λ 5 + Log( Quarter ) Log( IPT λ 9 j jk j j k + λ LghtBuyer Log( IPT ) 7 + λ8heavybuyer Log( IPTjk ) + LghtBuyer Log( Quarter ) λ 10 Log( IPT ) + jk λ 11 ) HeavyBuyer LogQuarter ( ) LogIPT ( ) + [ π0+ π 1Log( Quarterj) + π Log( IPT ) 2 j jk jk + π 3Log( Quarter ) Log( IPTjk ) + ϖ Log( IPT )] + ( ϖ + ς ), j1 jk j0 jk j j j,and The Long-Term Impact of Loyalty Programs / 33

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