The Long-Term Effects of Price Promotions on Category Incidence, Brand Choice and Purchase Quantity. Koen Pauwels. Dominique M.

Transcription

1 The Long-Term Effects of Prce Promotons on Category Incdence, Brand Choce and Purchase Quantty Koen Pauwels Domnque M. Hanssens S. Sddarth 1 January 11, Koen Pauwels s Assstant Professor at the Tuck School of Busness at Dartmouth (e-mal: koen.pauwels@dartmouth.edu). Domnque M. Hanssens s the Bud Knapp Professor of Management at the Anderson Graduate School of Management, Unversty of Calforna Los Angeles (e-mal: domnque.hanssens@anderson.ucla.edu). S. Sddarth s Assocate Professor at the Marshall School of Busness, Unversty of Southern Calforna (e-mal: sddarth@usc.edu). The authors thank A.C. Nelsen for the data used n ths study, Scott Nesln for the storablty and mpulse scales and Bart Bronnenberg for valuable suggestons. The constructve comments of the edtor and three anonymous Journal of Marketng Research revewers were helpful n strengthenng ths paper. 1

2 The Long-Term Effects of Prce Promotons on Category Incdence, Brand Choce and Purchase Quantty To what extent do prce promotons have a long-term effect on the components of brand sales,.e. category ncdence, brand choce and purchase quantty? Ths paper answers ths queston by usng persstence modelng on weekly sales data of a pershable and a storable product derved from a scanner panel. We test each sales component for permanent changes n ts tme seres, and for whether these changes are due to prce promotons. The tests reveal that the components of brand sales are predomnantly statonary or mean-revertng, so that permanent effects of prce promotons are vrtually absent. Absence of permanent effects does not mean, however, that the dynamc effects of prce promotons are neglgble. For those component seres that are statonary, we develop and apply an mpulse response approach to estmate the promotonal adjustment perod,.e. the length of tme for the dependent varable to revert to ts mean after beng shocked by a prce promoton. We combne ths measure wth the total effect of a prce shock on each sales component to obtan a complete account of the temporal effects of promotons on category ncdence, brand choce and purchase quantty. Specfcally, we calculate the long-term equvalent of Gupta s (1988) 14/84/2 breakdown of promotonal effects. Because of postve adjustment effects for ncdence but negatve adjustment effects for choce, we fnd a reversal of the mportance of category ncdence and brand choce: 66/11/23 for the storable product and 58/39/3 for the pershable product. We dscuss the mplcatons of our fndngs and suggest some areas for future research. 2

3 1. Introducton Snce the early seventes, prce promotons have emerged to account for the man share of the marketng budget n most consumer packaged goods categores (Currm and Schneder 1991), and a substantal body of academc research has establshed the nature of short-term sales response to temporary prce reductons. Whle some recent work has examned the long-term effects of prce promotons, ths area s stll probably the most debated ssue n the promotonal lterature and one for whch the jury s stll out (Blattberg, Bresch and Fox 1995, p. G127). Any study of long-term effects needs to carefully defne and operatonalze the long run. In ths respect, academc research has proceeded along three research streams, each wth dfferent methodologes and fndngs. Frst, Mela, Gupta and Lehmann (1997), Mela, Jedd and Bowman (1998) and Jedd, Mela and Gupta (1999) examne how promotons change consumers prce and promotonal senstvty over tme. The frst paper consders brand choce, the second paper category ncdence and purchase quantty and the thrd paper brand choce and purchase quantty. Ths stream of research defnes long term as the cumulatve effect on consumer brand choce, lastng over several years (Mela et al.1997, p.249) and examnes ths effect wth a dstrbutedlag (Koyck) response model. The key fndngs are that, as a result of repeated promotonal actvty n a category, category ncdence decreases but purchase quantty ncreases. Moreover, promotons ncrease consumer prce senstvty and decrease brand equty over tme. Overall, these studes confrm the exstence of a negatve promoton usage effect on consumer behavor (Blattberg and Nesln 1990). A second research stream confrms the postve mere purchase effect of promotons. Alawad and Nesln (1998) fnd that promotons nduce consumers to buy more and consume faster. They examne both a splne functon and a contnuous functon to model flexble category consumpton rates as a functon of household nventory. The key fndng s that promotonnduced nventory buldup temporarly ncreases usage rates. Alawad, Lehmann and Nesln (2001) examne market share response to a long-term change n the marketng mx (Procter & Gamble s Value Prcng Strategy). They defne deal actvty as the percent of yearly brand sales sold on deal. Ths varable has strong postve effects on market share n a multplcatve response model, and the authors conclude that P&G s loss of market share was attrbutable to ts severe cuts n coupons and deals, and the consequent ncrease n net prce (p.55). 3

4 Whle both research streams model dynamc effects of prce promotons, they can only capture transent, not endurng effects because they assume mean reverson of the dependent varable. Frst, the Koyck model n Mela et al. (1997) assumes that a fxed fracton 0 < λ < 1 of the effects n one perod s retaned n the next perod. Snce lm n λ n = 0, ths model mples that the dependent varable eventually returns to ts hstorcal mean. Second, both flexble functons n Alawad and Nesln (1998) mply that consumpton rates return to ther average levels as the excess nventory depletes. Fnally, the multplcatve response model n Alawad et al. (2001) also assumes mean reverson of the sales components. Therefore, these models do not gve a complete account of the long-term effects of prce promotons on sales. A thrd stream of research uses persstence modelng to capture the potental for permanent effects of promotons (Dekmpe, Hanssens and Slva-Rsso 1999). Frst, sales are classfed as statonary or evolvng. When sales are statonary, they eventually return to ther pre-promoton mean. In ths scenaro, promotons can mpact sales mmedately and over the next several weeks (the adjustment perod), but not n a permanent way. In contrast, when sales are evolvng, they do not have a fxed mean, and therefore could (but need not) be permanently affected by promotons. Dekmpe et al. (1999) apply persstence modelng to 4 product categores and use mpulseresponse functons to estmate permanent versus transtory effects of promotons on sales. A key fndng of ther research s that permanent effects of promotons are largely absent. Ths mples that promotons do not structurally change sales over tme, and that ther long-run proftablty depends only on the magntudes of response and cost parameters. However, t s possble that the absence of permanent promoton effects on sales s due to cancellaton of permanent effects n the three components of brand sales,.e. category ncdence, brand choce and purchase quantty. For example, the use of promotons can tran consumers to buy hgher quanttes on fewer occasons (Mela et al. 1998). Such a long-run scenaro could be attractve to brand managers, because the hgher nventory keeps the consumer out of the market for compettve products, but unattractve to retalers because consumers now need fewer store vsts (Bell, Chang and Padmanabhan 1999). At present, no study has examned the total over-tme mpact of prce promotons on all three sales components. 4

5 In terms of quantfcaton of promoton effects, several studes break down the mmedate mpact on ncdence, choce and quantty. Gupta (1988) found a 14/84/2 breakdown n the coffee market, whle Bell et al. (1999) analyzed 13 categores and reported an average breakdown of 11/75/14. Does a smlar decomposton of promotonal effects hold n the long run? Ths study uses persstence modelng to examne whether, and to what extent, prce promotons have a long-run mpact on the three components of brand sales. Based on a scanner panel, we compute for each store, category ncdence (the total number of panelsts buyng n the product category), brand choce (the share of these consumers buyng the brand) and purchase quantty (the average quantty purchased by the brand s consumers). For each component, we test for permanent changes n the tme seres and examne whether such changes are due to the prce shocks of the major brands n ths store. Furthermore, for component seres that are found to be statonary, we apply an mpulse-response approach to estmate the tme t takes for the dependent varable to revert to ts mean, after beng shocked by a prce promoton. Fnally, we quantfy the total over-tme mpact of a prce promoton on each sales component, and calculate the breakdown for the mmedate and the total effects. Therefore, ths study s the frst to quantfy total promotonal effects as the sum of mmedate, adjustment and permanent effects on each sales component. Ths enables us to compare and contrast the relatve mportance of the three components of promotonal effectveness n the short run and the long run. The remander of the paper s organzed as follows. Secton 2 defnes the tme wndows of promotonal effects on the three sales components and revews prevous studes n ths framework. Secton 3 develops hypotheses on the mpact of promotons on category ncdence, brand choce and purchase quantty. Secton 4 descrbes our methodology and data. The fndngs for a storable product (canned soup) and a pershable product (yogurt) are presented n Secton 5. Secton 6 concludes wth the manageral mplcatons and lmtatons of ths work, and offers drectons for future research. 5

6 2. The tme frame of promotonal effects on three sales components We classfy prevous research on promotonal effects on two dmensons: a) the tme frame n whch the promotonal mpact on sales s measured,.e. mmedate effects, adjustment effects and permanent effects 1 and b) the type of purchase behavor studed,.e. category ncdence, brand choce, and purchase quantty. The mmedate effects of prce promotons are reflected n short-term (contemporaneous) changes n sales. Most prevous research falls n ths category and reports consstently hgh promotonal effects (Blattberg et al. 1995; Blattberg and Nesln 1990). The adjustment effects of promotons refer to the transton perod between the short-term response and the resultng equlbrum, whch can be ether mean reverson or a new sales level. These adjustment effects can be postve or negatve, and ther sgn and magntude greatly mpact the overall proftablty of the promoton (Blattberg and Nesln 1990; Greenleaf, 1995). Dynamc effects are modeled through, among others, purchase loyalty (Guadagn and Lttle 1983), reference prce (e.g. Lattn and Buckln 1989), nventory (e.g. Gupta 1988), tme-varyng parameters (e.g. Papatla and Krshnamurth 1996; Mela et al. 1997) and flexble usage rates (Alawad and Nesln, 1998). Several studes equate these adjustment effects wth long-term effects. Indeed, the mplct assumpton underlyng dstrbuted lag models (e.g., Mela et al. 1998) s that the mpact of marketng effort des out over tme. Whle ths operatonalzaton allows promotons to have more than a contemporaneous nfluence on sales, t gnores the more complex permanent changes found n many sales seres (Dekmpe and Hanssens, 1995b). Fnally, permanent effects of a marketng acton requre that a proporton of the event s mpact s carred forward and sets a new trend. If the sales seres s evolvng, wth no fxed mean, then the permanent effects of marketng efforts can be captured by relatng these efforts to the evoluton of sales. These permanent effects are the focus of our frst research queston, as ther analyss provdes a necessary frst step for a complete account of the long-term mpact of prce promotons on sales. 1 Our termnology s based on the tme-seres lterature and wll be used throughout ths paper. Prevous authors ntroduced alternatve terms that ft our framework: effects are ether contemporaneous (mmedate) or dynamc (long term), whch could be transent (adjustment) or endurng (permanent). 6

7 Recent dsaggregate models n the marketng lterature (Chang 1991; Chntagunta 1993; Buckln et al. 1998) also dstngush the brand-sales components of category ncdence, brand choce and purchase quantty. For retalers, prce dscounts manly serve to create store traffc and to ncrease category sales (Putss and Dhar 1999). Therefore, promotons lose ther attracton f the mmedate gans n category ncdence and quantty are offset by negatve effects durng the off-promoton weeks. For manufacturers, the over-tme mpact on brand choce s the most mportant metrc. In the current study, we analyze the dynamc effects of prce promotons on tme seres of all three sales components. Specfcally, we compute the total number of panelsts buyng n the product category (category ncdence), the share of these consumers buyng a partcular brand (brand choce) and the average quantty purchased by the brand s consumers. Ths breakdown, whch s calculated separately for each store n the sample, allows us to compare results from the tme-seres analyss of the three sales components wth the fndngs from dsaggregate analyses that apply ths dstncton. Table 1 summarzes prevous research on promotons along the two dmensons of tme frame and type of data. Three nferences from Table 1 provde the motvaton for the current study. Frst, emprcal evdence on permanent effects of prce promotons s vrtually non-exstent (Dekmpe et al. 1999; Njs, Dekmpe, Steenkamp and Hanssens 2001). Second, none of these studes consders the breakdown of sales n category ncdence, brand choce and purchase quantty. Thrd, all prevous persstence modelng has focused on market level data, whereas managers often need analyss at the account or store level (Buckln and Gupta 1999). We seek to fll ths vod by studyng the permanent, adjustment and mmedate effects of prce promotons on each of the dfferent sales components for each store n our scanner panel data set. 3. Hypothess development Consstent wth our framework, we frst develop hypotheses on the temporal dmenson of promotonal effectveness. In partcular, we focus on the sgn and magntude of the total promotonal mpact on the sales components. Table 2 summarzes our hypotheses on the mmedate, adjustment, permanent and total effects of prce promotons for storable and pershable products. Our hypotheses on the length of the adjustment perod are more tentatve, as ths topc has not receved much attenton n the context of prce promotons. 7

8 3.1. Immedate effects of prce promotons Promotons cause a substantal mmedate ncrease n all three sales components (Blattberg et al. 1995; Bell et al. 1999). The economc ratonale s clear: temporary prce reductons ncrease the value of the product to the consumer and requre mmedate acton. The marketng lterature dstngushes dfferent consumer behavors that contrbute to the mmedate sales boost (Blattberg and Nesln 1990). Category ncdence ncreases due to tmng acceleraton (purchasng earler), mpulse purchases and category swtchng (substtutng purchases between categores). Brand choce benefts from consumers swtchng to the promoted tem. Fnally, purchase quantty benefts from quantty acceleraton (forward buyng) and stockplng behavor. As for the relatve magntude of the promotonal effect, Bell et al. (1999) report an average elastcty breakdown (ncdence/choce/quantty) of 3/75/22 for storable products and 17/75/8 for pershable products. Our frst hypothess s therefore: H1: The mmedate promotonal effects are hgher for brand choce than for the other two sales components. 3.2 Adjustment effects of prce promotons We dstngush three reasons for adjustment effects: 1) dynamc consumer response, ncludng the post-deal trough, the mere purchase effect and the promoton usage effect, 2) compettve reacton and 3) performance feedback. The post-deal trough logcally follows from tmng and quantty acceleraton. Gven ther larger stock, consumers wll reduce ther purchases n subsequent weeks. Interestngly however, the emprcal evdence on post-deal troughs n brand sales s mxed (Blattberg et al. 1995). On the one hand, Blattberg et al. (1981), Nesln et al. (1985), Leone (1987), Jan and Vlcassm (1991) and Van Heerde et al. (2000) report post-deal troughs. On the other hand, Grover and Srnvasan (1992) and Vlcassm and Chntagunta (1992) fnd no post-promoton dps. Ltvack, Calantone and Warshaw (1985) consder several categores and do not observe a post-promoton dp for the categores that could experence purchase acceleraton. Morarty (1985) fnds sgnfcant post-promoton dps for only 3 out of 15 cases, by ncludng one-week lagged promoton varables n the sales-response functon. As a general rule, the post-deal trough appears small n comparson wth the mmedate sales ncrease (Abraham and Lodsh 1987). 8

9 The mere purchase effect holds that promoton-nduced purchases ncrease future sales (Blattberg and Nesln 1990). Three behavoral theores could account for ths effect. Frst, learnng theory holds that promotons offers a rsk premum for tral by new consumers, some of whom wll lke the product and repurchase t n the future (Mela et al. 1997). Second, promotons remnd exstng consumers to buy the brand and renforce ther tastes for t (Erdem 1996). Both theores mply benefts for both category ncdence and brand choce. Fnally, promotons nduce consumers to buy n larger quanttes (stockplng), whch can ncrease ther consumpton rates (Chandon and Wansnk 1997; Alawad and Nesln 1998). The promoton usage effect concentrates on the mpact of promotons on consumer perceptons. Frst, self-percepton theory (Bem 1967) mples that consumers are lkely to attrbute ther purchase to an external cause (takng advantage of a promoton) nstead of an nternal cause (e.g. brand lkng). Second, prce percepton theory holds that consumers form a reference prce for the brand based on past prces (Kalyanaram and Wner 1995). Ths reference serves as an nternal standard aganst whch current prces are compared (Helson 1964). Promotons lower the reference prce, makng consumers reluctant to buy the brand at all n nonpromoton perods (Lattn and Buckln 1989). Fnally, object percepton theory (Blattberg et al. 1995) postulates that promotons wll damage the brand s qualty mage. Gven the opposte drectons of the mere purchase effect versus the post-deal trough and the promoton usage effect, the net mpact of promotons on dynamc consumer response remans an emprcal puzzle n marketng lterature. Early research reported postve total effects on brand choce and sales (Guadagn and Lttle 1983; Blattberg and Nesln 1990; Davs et al. 1992), whereas later studes reported predomnantly negatve dynamc effects (Mela et al. 1998; Jedd et al. 1999). In addton to dynamc consumer response, compettors may react to the focal brand's promoton (Leeflang and Wttnk 1992; 1996). The mpact of compettve reacton should dffer for brand choce versus ncdence. For brand choce, we expect compettve reacton to hurt the focal brand (Bass et al., 1984). In contrast, category ncdence should beneft from promotonal reacton by compettors n the same category (Putss and Dhar 1999). If the focal brand's promoton attracts consumers to the category, so should the compettve promotons. Fnally, performance feedback and company decson rules may lead to repetton of marketng actons that were consdered successful (Dekmpe and Hanssens 1995a; 1999). 9

10 Therefore, a successful promoton can ncrease future promotonal actvty. Promotonal effectveness may ether beneft from ths renforcement, or suffer as consumers adjust to a hgher level of promotonal actvty (Assunςao and Meyer 1993; Krshna 1992; 1994). In summary, the net adjustment effects of prce promotons could be postve or negatve and wll dffer for the three sales components. For category ncdence, both the mere purchase aspect of dynamc consumer response as well as compettve reacton and performance feedback, yeld postve effects, whereas the tmng acceleraton and promoton usage aspect of consumer response yelds negatve effects. As a net result, we predct postve adjustment effects for category ncdence. In contrast, brand choce has been found to suffer from post-deal trough, promoton usage effects and compettve reactons. Therefore, we predct negatve adjustment effects. Fnally, average purchase quantty s negatvely affected by quantty acceleraton but postvely affected by tmng acceleraton. Nether drecton has strong emprcal support. H2: The adjustment effects are a) postve for category ncdence, b) negatve for brand choce. 3.3 Permanent effects of prce promotons Wth the excepton of the post-deal trough, all the above dynamc effects could be permanent n nature. Frst, the mere purchase effect may persst f promoton-nduced tral results n repeat purchase (Blattberg and Nesln 1990). However, ths phenomenon s most lkely to occur for new-product categores and for new consumers n the geographc area or n the store (Gjsbrechts 1993). Any mpact would be small for mature products (Mela et al. 1997). Second, the promoton-usage effect may persst f consumers contnue to assocate the brand wth the negatve percepton of the promoton. Agan, such a permanent phenomenon s unlkely. Moreover, exstng models of dynamc choce, ncludng nventory management, predct that sales wll eventually return to ther pre-promoton level (Assunςao and Meyer 1993; Krshna 1992; 1994). Fnally, compettve reactons to the promoton typcally de out over tme, wth the rare excepton of a dscount that escalates nto an all-out prce war. In summary, permanent sales effects by promotons seem unlkely n mature product categores. Emprcal studes nvestgatng permanent promotonal effects are scarce, because most models assume mean-revertng behavor. Dekmpe, Hanssens and Slva-Rsso (1999) report postve permanent effects for one brand s promotons n one out of four category sales seres. 10

11 Njs et al. (2001) fnd evoluton n category demand n only 36 out of 560 product categores (6.5%) of ther Dutch data set. Three percent of product categores experence a postve permanent mpact, one percent a negatve permanent mpact. In other words, permanent effects of promotons on category sales are the excepton rather than the rule. In both studes however, the absence of permanent effects could be caused by the cancellaton of postve effects on one sales component by negatve effects on the other. For nstance, nventory management changes could persst, resultng n lower ncdence but hgher purchase quantty levels. Wthn a meanrevertng model, Mela et al. (1998) fnd that ncreased expectatons of future promotons reduce the lkelhood of category ncdence and ncrease purchase quantty, gven ncdence. The present study s the frst to emprcally nvestgate whether the assumed absence of permanent promotonal effects holds for a storable and a pershable product. We expect to fnd that H3: Permanent effects of promotons are absent for all sales components Total effects of prce promotons Whereas the tme frame of promotonal effects may be of some nterest to practtoners, ther major queston s: what s the total over-tme mpact of the prce promoton I am plannng to run? As dscussed above, both the mmedate effects and the adjustment effects are expected to dffer for each sales component. Therefore, our hypotheses on the total promotonal mpact logcally follow from hypotheses 1-3. Frst, we expect a postve promotonal mpact on all three sales components. In other words, negatve adjustment effects wll not completely cancel out the postve mmedate mpact (Alawad and Nesln 1998; Jedd et al. 1999). However, the dfferent sgns of the proposed adjustment effects for ncdence and choce wll greatly affect ther relatve magntude n the total effect decomposton. Whereas the mmedate benefts for brand choce are largely reduced n the adjustment perod (Jedd et al. 1999), we expect postve adjustment effects to enhance the category ncdence hke. Therefore, the emprcal generalzaton that prce promotons have the largest mmedate mpact on brand choce (Bell et al. 1999), wll not hold when the total effect horzon s consdered. Instead, we expect category ncdence effects to domnate the total promotonal mpact. The relatve mportance of quantty effects should depend on product storablty. For ther storable product, Jedd et al. (1999) report larger total effects for purchase quantty than for brand choce. We expect the opposte orderng for pershables, whch 11

12 are dffcult to stockple and therefore less lkely to yeld ncreased consumpton. Thus our hypotheses are: H4: The total promotonal mpact s postve for all sales components. H5a: The total promotonal effects are hgher for category ncdence than for the other two sales components. H5b: For storable products, the total promotonal mpact s hgher for purchase quantty than for brand choce. H5c: For pershable products, the total promotonal mpact s hgher for brand choce than for purchase quantty. Product storablty may also nfluence the magntude of total effects compared to other categores. Bell et al. (1999) fnd that mmedate effects on all sales components are larger for storable products than for pershables. In the long run, we see no theoretcal ratonale for ths phenomenon for category ncdence and brand choce. In contrast, purchase quantty effects should depend on product storablty, as consumers are more flexble n ther quantty decsons when t s easy to stockple the product. Therefore, H6: The total promotonal mpact s larger for storable products than for pershables on purchase quantty only. 3.5 The tme wndow of promotonal adjustment Advertsng research has long developed an nterest n estmatng the tme wndow of advertsng carry-over effects on sales. Leone (1995) concludes that 90% of the effects of advertsng on sales de out wthn 6 to 9 months. In contrast, the promotonal lterature has been surprsngly vague about the duraton nterval of promotonal effects. The two exceptons are the studes by Mela et al. (1997; 1998), who report ntervals of, respectvely, 33 weeks and 21 weeks and conclude that promotons have a slghtly less endurng effect compared to advertsng. However, these fndngs may be specfc to the non-food product under study, and to the assumpton of exponental decay of promoton effects. 12

13 In contrast to advertsng, prce promotons are tools for generatng mmedate sales (Blattberg and Nesln 1990). We therefore expect that promotonal effects de out soon,.e. wthn the standard short-term management plannng horzon of one quarter. Moreover, the exponental decay assumpton n the Koyck model s less approprate for promotonal adjustment effects, whch may nclude both postve and negatve coeffcents. In our framework, the adjustment perod refers to the number of weeks between the short-term response and the long-run equlbrum. In the absence of permanent effects, ths perod corresponds to the number of weeks wth adjustment effects that are sgnfcantly dfferent from zero. The length of the adjustment perod may depend on the sales component and on product storablty. As for the former, there s no prevous lterature that can generate a pror predctons. As for the latter, product storablty enables ratonal consumers to sharply adjust ther quantty decsons to the promotonal pattern n the category (Assunςao and Meyer 1993). For a substantve perod after the promoton, these consumers wll not return to ther prevous quantty levels. For pershables, quantty effects are necessarly short-lved because of consumer stockplng lmtatons. Therefore, we expect the storable products to show longer quantty adjustment perods than pershable products. In concluson, H7a: H7b: For each sales component, promotonal effects de out wthn a quarter (13 weeks). The quantty adjustment perod s longer for storable products than for pershables. 4. Methodology and data descrpton 4.1 Overvew Three steps are necessary for the assessment of the long-term mpact of prce promotons on the dependent varables (Dekmpe and Hanssens 1995; Bronnenberg et al. 2000). Frst, unt-root tests dentfy whether or not there s evoluton n the data-generatng process of the varables. If evoluton s detected, then contegraton analyss determnes whether a long-run equlbrum exsts between the seres of the dependent varable and the ndependent varables of nterest. In that case, a vector-error correcton (VEC) model s estmated, otherwse a vector-autoregressve (VAR) model n dfferences s specfed. Fnally, mpulse-response and multvarate persstence estmates vsualze and quantfy the long-term mpact of prce shocks. 13

14 If the unt-root tests fal to dentfy evoluton, we conclude that the tme seres are statonary, they return to ther mean (or a determnstc trend) after the effects of a shock have ded out. In that case, vector-autoregressve models are estmated on the levels of the data, and the coeffcents of the mpulse response functons can be used to compute the fnte total effect of promotons, as well as the length of the promoton-response (adjustment) perod. 4.2 Data descrpton Our dataset s constructed from the A.C. Nelsen household scanner data n the Soux Falls market (South Dakota) for the perod 7/14/1986 untl 9/5/1988. These data have been made avalable through the Marketng Scence Insttute and have been wdely used n the marketng lterature. Based on the above dscusson on product storablty, we consder canned soup, a storable product that s not generally bought on mpulse, and yogurt, a pershable product that s often bought on mpulse (Narasmhan et al. 1996). The number of consumers n the panel s 2399 for yogurt and 1826 for soup. Total consumpton s oz for yogurt and oz for soup. The consumpton average s 288 oz for yogurt and 295 oz for soup. To prepare the scanner data for tme seres analyss, we frst compute the number of consumers and the total number of ounces sold per brand and per store. We then select the brands that occupy the major postons n the market (more than 80% for both categores), and the 5 best sellng stores, provded they belong to dfferent chans. Ths procedure resulted n 4 stores, each wth 3 soup brands (two natonal brands and one prvate label) and 3 stores, each wth 5-6 yogurt brands (4 natonal brands and 1-2 prvate labels). Table 3 descrbes the average market shares, prces 2 and promotonal actvtes. As the VAR approach requres equally-spaced tme seres, we transform the purchase-occason based scanner data nto weekly rato-scaled data on the store level. In partcular, we compute the weekly number of panelsts who made a purchase n the category (category ncdence), the fracton of these consumers who bought the brand (brand choce 3 ) and the average quantty per purchasng consumer. Although some aggregaton bas mght result from ths procedure (Pesaran and Smth 1995), the fact that all consumers are exposed to the same marketng-mx varables, that the brands are close 2 We obtaned SKU market shares from separate store sales nformaton over the full perod. These full perod market shares then served as weghts to aggregate SKU-level prces to brand-level prces. 3 Because choce share s a lmted dependent varable, the normalty assumpton on ts error term may not hold. We therefore perform our analyss usng choce = share/(1-share) as a dependent varable. 14

15 substtutes, and that the prce dstrbuton s not concentrated at an extreme value, greatly reduces ths bas (Allenby and Ross 1991). Separate analyses for each store provde varablty n the prcng and promotonal pattern of the same brand n dfferent retaler settngs. 4.3 Unt-root testng of the sales components and prces The Augmented Dckey-Fuller test s performed for each seres n several versons (see Dekmpe, Hanssens and Slva-Rsso 1999 for detals). Frst, we decde on the number of lags to nclude n the test by the Schwarz Bayesan Informaton Crteron and by the maxmum lag for whch the regresson coeffcent s sgnfcant. Schwarz s Bayesan Informaton Crteron (BIC) s desgned to consstently estmate the lag structure as a unform crteron that mnmzes the sum of squared errors whle takng model complexty nto account. In order to assess the robustness of ths procedure, we also performed the tests usng the number of statstcally sgnfcant lagged terms as the crteron for lag selecton. Except for a few seres, test conclusons were dentcal. In comparson wth the maxmum sgnfcant lag crteron, mnmzaton of the BIC has the addtonal advantage of model parsmony, snce fewer lags wll be ncluded. Second, we account for structural breaks n the data, such as the two new-product entres n the yogurt category. We ncorporate these potental breaks n the unt-root test and n the vector-autoregressve model estmaton. Before we proceed wth estmatng VAR models, we also need to nvestgate whether a unt root s present n prces. For the yogurt category, prces are mean-statonary wth a few exceptons due to two new-brand entres. When accountng for these nterventons by dummy varables, these cases test as statonary as well. Prces n the soup market are trend-statonary,.e. they become statonary after removng a postve trend, possbly due to nflaton. 4.4 Specfcaton of vector-autoregressve models The order of the VAR models s based on Schwarz s Bayesan Informaton Crteron. For all our seres, ether a frst-order or a second-order VAR was selected. In order to facltate comparson across brands and stores, we proceed by estmatng a second-order VAR for all seres. Such a second-order VAR model for a store wth 3 brands s presented n Fgure 1. Statonary varables are ncluded n levels, dfference-statonary varables n dfferences and 15

16 trend-statonary varables n detrended levels. When two or more varables are evolvng, we also test for contegraton usng the Johansen Lkelhood Rato (trace) test. An mportant decson s whch varables to nclude n the VAR estmaton, and whether to treat them as endogenous or exogenous. The smple base model, as depcted n Fgure 1, features the three response varables for the focal brand and the prces of all brands n the market as endogenous varables, and feature and dsplay of all brands as exogenous varables. The treatment of prces as endogenous mples that lagged effects of the performance varables (performance feedback) and compettor prces (compettve reacton) are accounted for. Feature and dsplay are treated as control varables wth contemporaneous effects on the response measures. The contemporaneous effects among the endogenous varables are modeled through the resdual covarance matrx (Lütkepohl 1993). Modelng assumptons for tractablty nclude ndependent errors for each brand and for each sales component. Specfcally, the estmaton of a VAR-model for each brand mples that choce share errors are assumed ndependent and that category ncdence effects are estmated separately for each brand. Even for ths relatvely smple model, the second-order VAR-model estmates 2 * (3+n) 2 -coeffcents, wth n beng the number of brands. A frst extenson of the base model s the ncluson of feature and dsplay as endogenous varables, because they too can dsplay dynamc effects (Papatla and Krshnamurth 1996). Although feature and dsplay are not the focus of our research, we valdate our results by estmatng the extended model and reportng the correlaton of estmated promotonal effects. Second, the functonal form of the depcted model s lnear n ncdence, choce and quantty, smlar to prevous models n ths research stream (Dekmpe et al. 1999). An alternatve specfcaton s the multplcatve model, yeldng lnear equatons after takng logarthms. Compared to the constant elastcty of the log-log model, the lnear model yelds an elastcty that s ncreasng n prce. The mpled decreasng returns to prce promotons are ntutve, gven our promotonal defnton. An unexpected, one-standard devaton error shock to prce wll yeld ncreased consumpton (.e. wll n all lkelhood cross the threshold of beng notced and acted upon by some consumers). However, doublng ths promotonal depth wll not result n twce the effect of the lower dscount, because of lmts to ncreases n all three sales components. Incdence gans are lmted by the number of consumers consderng buyng nto the category. Choce-share gans are lmted by hard-core loyals for the other brands. Quantty gans are 16

17 lmted by storablty and nventory carryng costs. By means of flexble parametrzaton methods, Van Heerde et al. (2000b) recently found that most promotons ndeed show decreasng returns to the magntude of the dscount. The authors conclude that "test results ndcate that the assumpton of constant elastctes s untenable" (p.28) and that "one possble nterpretaton s that consumers tend to swtch at relatvely low prce dscount levels, and that hgher prce dscounts do not result n much further swtchng" (p. 27). For these reasons we use a lnear specfcaton, and we valdate our results by applyng the log-log model and examnng the correlatons between the parameters of the two models. 4.5 Impulse- response functons The selected VAR models are used to smulate the over-tme effects of one-standarderror prce shocks on the system, usng mpulse-response functons. Ths method yelds estmates of the ncremental effect of the prce promoton on the response varable relatve to ts baselne 4. The mpulse-response functon s calculated from an ntal shock, whch requres the specfcaton of a causal orderng of the contemporaneous shocks (see Dekmpe and Hanssens 1999 for a detaled dscusson). We shock brand prce frst, allowng for contemporaneous effects on compettve prces and the response varable (category ncdence, brand choce, or purchase quantty) 5. Gven that the marketng data are weekly, ths orderng s meanngful because frms need tme to react to compettve prce promotons or demand fluctuatons (Leeflang and Wttnk 1992). Our smulaton only excludes nstant prce reactons of the focal brand to compettve-prce or performance shocks. 4.6 Operatonalzaton of the promotonal effects In the framework of the mpulse-response functons derved from our VAR model, the mmedate promoton mpact s the effect of a one-standard-devaton prce shock on the response varable (for prevous applcatons of ths operatonalzaton, see Dekmpe et al and Srnvsan et al. 2001). Note that ths mmedate effect s captured by the resdual correlaton matrx, as contemporaneous prce does not drectly appear n the regresson equaton for the 4 For statonary seres ths baselne s the mean value of the full tme seres and for evolvng seres t s the last observaton of the tme seres. 5 Note that ths smulaton s numercally equvalent to the smultaneous-shockng approach used n Dekmpe & Hanssens (1999), provded the focal varable s shocked frst. 17

18 response varable. The sgn (negatve for own prce, postve for cross-prce effects) and magntude of the mmedate effect provdes a valdty check on our fndngs. We operatonalze the total effects of a prce promoton as the sum of all mpulse-response weghts wth a t-statstc greater than one n absolute value (Dekmpe et al. 1999). Snce ths generous cutoff-pont translates nto wde confdence ntervals, we focus on the sgn and relatve magntude of total effects. The length of the adjustment perod s operatonalzed as the number of weeks t takes to observe 90% of the sgnfcant mpulse-response weghts. We test the correspondng hypothess for both product categores by computng the average adjustment perod for each sales component, weghted by category sales share for each store. Fnally, the decomposton of promotonal effects requres several choces. Frst, we need a common scale to compare the promotonal mpact on the three sales components. In order to facltate a comparson wth prevous lterature, we choose to compute elastctes. To that end, we wrte brand sales as: Brand Sales (S) = # Category Consumers (I) * Share of consumers for (C) * Average purchase quantty (Q) Therefore, assumng ndependence among the three components, we wrte ncremental sales as: In ths equaton the change n demand on the total number of consumers (ncdence) s weghted by choce and quantty, choce changes are weghted by ncdence and quantty and quantty changes by ncdence and choce. Ths procedure parallels the elastcty calculatons n household-level models (e.g. Buckln et al. 1998). The elastcty decomposton becomes: 18

19 We apply equaton (2) to calculate the mmedate, adjustment, permanent and total effects. For the adjustment and total effects, the sgns of elastcty estmates may dffer, whch complcates the computaton of average elastctes over all brands and stores. Our calculaton procedure s the followng: for each store, we compute the elastctes per decson varable and per brand. Next, we compute the weghted average across all brands for each decson varable. The weght used s the average choce share of the brand, dvded by the sum of the average choce shares of all analyzed brands n the store (n order to ensure that the weghts add up to 100%). Ths procedure s repeated for each decson varable and each store, after whch we compute the weghted average across the three stores for each decson varable 6. Category sales are used as store weghts. Fnally, we compute the elastcty decomposton by dvdng each elastcty by the sum of the absolute values of three elastctes for ease of comparson. Because of dfferent model assumptons and the possblty of negatve effects, our decomposton analyss does not completely correspond to the decomposton procedures of Gupta (1988), Buckln et al. (1998) or Bell et al. (1999). Therefore, we wll verfy that our mmedate effect breakdown s smlar to the mmedate elastcty breakdown obtaned n prevous research. Moreover, we estmate the household-level model n Buckln et al. (1998) and compare the mmedate effects obtaned by the two dfferent methodologes on the same data. Fnally, we check the robustness of our results by repeatng the analyss usng logarthms of sales components and prces. 4.7 Comparson wth prevous models n the promoton lterature Table 4 compares and contrasts our methodology wth prevous approaches to capture the dynamc effects of prce promotons on ncdence, choce and quantty. Frst, nested logt models have allowed for flexble nventory (e.g. Gupta 1988) and consumpton (Alawad and Nesln 1998). Second, tme-varyng parameter models have analyzed the dynamc effect of promotonal actvty on consumer prce and promotonal senstvty (e.g. Mela et al. 1997; Jedd et al. 1999). Fnally, Vector-Autoregressve models have been appled to marketng problems by Dekmpe et al. (1999) and Bronnenberg et al. (2000). For ease of exposure, we summarze the typcal elements of each approach and focus on ther dfferences. 6 We nvestgated the nfluence of forecasts errors for the adjustment and total effects by also weghtng the brand elastctes by the t-statstc of the accumulated mpulse response functon. Results are smlar: the substantve fndngs on the decomposton of adjustment and total effects contnue to hold. 19

20 The models dffer n mathematcal formulaton, promotonal defnton, ncorporaton of dynamc factors and tme-varyng components. As for model formulaton, the man dfferences are that 1) the VAR model treats prces as endogenous,.e. t allows lagged effects of sales components (performance feedback) and compettor prces (compettor reacton) on the brand s current prce and 2) the VAR model requres equally spaced tme seres n contrast to the unequally spaced purchase occason data at the household level. We dscuss these ponts n turn. Frst, VAR models capture not only drect (mmedate and/or lagged) consumer response to promotons, but also the performance mplcatons of the nduced compettve reacton and company performance feedback. As for the former, a promotonal shock may trgger compettve prce reactons. As for the latter, the promoton may generate a strong boost n the managerally relevant performance varable(s), whch nduces further promotons for the same brand. For ths reason, own and compettve prces are endogenous varables: they explan performance and are explaned by past prces and performance varables. Our man nterest les n the net result of all these actons and reactons, whch can be derved from a VAR model through ts assocated mpulse-response functons. Second, the VAR approach requres the endogenous performance and marketng varables to be equally-spaced tme seres. Therefore, we transform the purchase-occason based scanner data nto weekly data at the store level. Prevous lterature has compared advantages and dsadvantages of household-level purchase occason data and weekly store-level data (Allenby and Ross 1991; Buckln and Gupta 1999). An mportant dfference s the unt of analyss: purchase occasons and ndvdual probabltes of ncdence, choce and quantty n householdlevel models versus store-level varables n the VAR-model (number of category consumers, the fracton of these consumers who bought the brand and the average purchase quantty per consumer). For our decomposton approach, t s mportant to note that n the VAR model, the total number of consumers reflects only those occasons when a purchase was made, whereas a household-level approach also models no-purchase ('non-ncdence'). As a result, the ncdence and choce measures are related: choce s only observable when a category purchase s made 7. The defnton of a prce promoton also dffers among the three modelng approaches n Table 4. Nested logt models typcally consder the prce elastcty, whereas the tme-varyng parameter models look at the elastcty for a temporary prce reducton and at long-term 7 We thank an anonymous revewer for ths nsght. 20

21 promotonal actvty. Our analyss consders the ncremental mpact of an unexpected prce shock. If consumers ndeed ncorporate prce expectatons n ther buyng behavor, they wll only respond to the unantcpated part of a gven prce reducton (Helson 1964; Kalyanaram and Wner 1995). By defnton, all the prce shocks n our models are "unexpected", whch s not true of the prce reductons n a typcal household-level model. Therefore, we expect our approach to yeld larger elastcty estmates than typcal household-level models. Fnally, dynamc effects of promotons are captured by nventory, loyalty and reference prce measures n the nested logt models, by Koyck-type regressons on promotonal actvty measures n tme-varyng parameter models, and by mpulse-response functons n the VAR approach. Impulse-response functons are the most nclusve n dealng wth dynamc effects, because they do not mpose a lag structure and allow for compettve reactons and performance feedback. VAR models also allow past promotonal actvtes to nfluence the current prce and promotonal elastctes through ther mpact on the shock value of a current prce promoton. Frequent, predctable promotons are ncorporated n consumers expected prce levels, so that larger deals would be needed to obtan a one-standard devaton prce shock. 5. Emprcal results Followng our research desgn, we start wth a dscusson of the temporal behavor of the three sales components. Our fndngs on evoluton and tme-to-mean reverson descrbe the temporal boundares of the promotonal effect. Next we focus on the sgn and magntude of the promotonal mpact, broken down as mmedate effects, adjustment effects and permanent effects. We compare these effects across sales components and product categores. 5.1 Permanent effects of promotons on the sales components The unt-root test results n Table 5 show that the tme seres for all three sales components are statonary for 82% of the brand-store combnatons. In other words, the most common compettve scenaro n our data s busness-as-usual (Dekmpe and Hanssens 1999). In these cases, no permanent promotonal effects are present and all seres revert to ther means after the mmedate and the adjustment effect. 21

22 The few cases wth at least one evolvng sales component are not of a unform nature. For the yogurt category, evoluton s present only at the brand-choce level for two brands n the frst store. Both are small natonal brands, wth average market shares of, respectvely, 7% and 6%. In the frst case (brand 2), ths evoluton s not caused by prce changes. Only n the second case (brand 4) s there evdence of permanent effects of prce promotons, wth a small persstence of 6.5% of the mmedate effect. For clarty purposes, we wll abstract from ths solated and small permanent effect n the remander of our analyss. For the soup category, evoluton s present only at the category ncdence level for the fourth store. Ths store experenced a steady declne n the total number of soup consumers, whch cannot be explaned by prce evoluton. We expect that external factors may have caused the declne n category ncdence, because both panelsts vsts to and spendng n ths store showed negatve evoluton as well. Subsequent contegraton tests demonstrate that soup category ncdence s n a long-run equlbrum wth these store-wde varables. Possble reasons for the store-wde declne nclude new competton from a nearby store or mall, or nterruptons due to store remodelng. Because t s dffcult to measure and nterpret promotonal effectveness aganst ths anomalous background, we do not nclude ths store n the subsequent analyss. In summary, the emprcal evdence supports hypothess 3 for all sales components n both product categores. Permanent effects of promotons on category ncdence or purchase quantty exst for none of the products n none of the stores n our data. Moreover, only 2 out of 29 cases (7%) show evoluton n brand choce, and only n 1 case (3%) do we fnd permanent effects of prce promotons. These results are consstent wth the fndngs of Njs et al. (2001): examnng 560 product categores n the Netherlands, they fnd sales evoluton n 6.5% of all cases, and permanent effects of prce promotons n only 4% (3% postve, 1% negatve). 5.2 VAR model results For each brand n each store, the VAR model n Fgure 1 s estmated on statonary varables, after dfferencng or detrendng as needed 8. For model comparson purposes, we focus on ft ndces and on the covarance estmates between each brand s prce and ts response varables. The expected negatve prce elastcty mples negatve covarance estmates of the brand s prce wth category ncdence and wth brand choce. Average purchase quantty may actually decrease 8 Detaled model estmates, ft ndces and resdual varance-covarance matrces are avalable from the frst author. 22

23 f a promoton attracts manly lght users. Therefore, we only nvestgate the ncdence and choce covarance estmates for sgn consstency wth expectatons (18 estmates for the soup category and 34 for the yogurt category). For model valdaton purposes, we compare the ft and the sgn of the covarance estmates of the base model wth those of the multplcatve model (log-log specfcaton) and the extended endogenous model (wth feature and dsplay as endogenous varables). Compared to the multplcatve model, the base lnear model always yelds lower values for the Akake Informaton Crteron and the Bayesan Informaton Crteron. Moreover, the number of ncorrectly sgned error covarance estmates for the log-log specfcaton s 3 out of 18 (16.7%) versus none for the lnear specfcaton n the soup category. For the ncdence and choce components n the yogurt category, the number of ncorrect sgns s 11 out of 34 for the log specfcaton (32.4%) versus 4 out of 34 (11.8%) for the lnear specfcaton. Compared to the extended endogenous model, the base model yelds lower values for the Akake Informaton Crteron and the Bayesan Informaton Crteron (on average respectvely 30% and 24% lower). The number of ncorrectly sgned error covarance estmates s smlar. In summary, the estmated VAR model outperforms both alternatves n model ft and yelds theoretcally meanngful relatons between prce and market response, whch allows an analyss of promotonal effects by means of the derved mpulse-response functons. 5.3 Immedate effects of prce promotons Table 6 presents the ncdence, choce and quantty elastctes calculated from the mmedate response to an own-prce shock. Because promotons are defned as unexpected prce decreases, we reverse the sgn of the mpulse-response estmates to obtan promotonal elastctes. Ths procedure enhances ease of nterpretaton throughout the paper, as postve elastctes ndcate benefcal promotonal effects on the sales components. All mmedate promotonal elastctes ether have the expected postve sgn, or do not sgnfcantly dffer from zero. The average mmedate elastctes for category ncdence, brand choce and purchase quantty are 1.78, 2.84 and 1.26 for soup and 0.88, 4.45 and 0.81 for yogurt. These estmates are smlar to deal dscount elastctes n the range [0.49, 14.34] reported n prevous lterature (Blattberg and Nesln 1990, p.356). The mmedate elastcty decomposton s 30%/48%/21% for soup and 14%/73%/13% 23