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CARL F. MELA, SUNIL GUPTA, and DONALD R. LEHMANN*                                                   The authors examine th...
The Long-Term Impact of Promotion and Advertising                                                                         ...
250                                                                      JOURNAL OF MARKETING RESEARCH, MAY 1997enhance th...
The Long-Term Impact of Promofion and Advertising                                                                         ...
252                                                                                  JOURNAL OF MARKETING RESEARCH, MAY 19...
The Long-Term Impact of Promotion and Advertising                                                                         ...
254                                                                                      JOURNAL OF MARKETING RESEARCH, MA...
The Long-Term Impact of Promotion and Advertising                                                                         ...
256                                                                           JOURNAL OF MARKETING RESEARCH, MAY 1997etTic...
The Long-Term Impact of Promotion and Advertising                                                                         ...
258                                                                                     JOURNAL OF MARKETING RESEARCH. MAY...
The Long-Term Impact of Promotion and Advertising                                                                         ...
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  1. 1. CARL F. MELA, SUNIL GUPTA, and DONALD R. LEHMANN* The authors examine the long-term effects of promotion and advertis- ing on consumers brand choice behavior. They use 8 1/4 years of panel data for a frequently purchased packaged good to address two questions: (1) Do consumers responses to marketing mix variables, such as price, change over a long period of time? (2) If yes, are these changes associ- ated with changes in manufacturers advertising and retailers promotional policies? Using these results, the authors draw implications for manufac- turers pricing, advertising, and promotion policies. The authors use a two- stage approach, which permits them to assess the medium-term (quar- terly) effects of advertising and promotion as weil as their long-term (i.e., over an infinite horizon) effects. Their results are consistent with the hypotheses that consumers become more price and promotion sensitive over time because of reduced advertising and increased promotions. The Long-Term Impact of Promotion and Advertising on Consumer Brand Choice Trade promotions account for 50% oi lhe $70 billion bud- However, support lor value pricing strategy is not univer-get of consumer packaged goods manufaclurcr.s (Progres- sal. There is an uncertainty in both industry and academiasive Grocer 1995). Almost 70% of the finns have increased about the long-term impact of promotion and advertising ontheir trade promotions between 1990 and 1995. Forbes brand perfonnance. Some companies such as Heinz have(1991) also reports an increase in consumer and trade pro- emphasized their continued reliance on promotions as theirmotional spending tor consumer packaged goods manufac- main marketing vehicle (Wall Street Journal 1992). Othersturers—from 50% to 75% of marketing budgets during cite such cases as that of Hi-C, which tried to switch away 1985-90. Il suggests that this increase induced a loss of from promotion in the early 1980s and lost significant share8-15% in the share of the top three brands in categories as (Busine.is Week 1992). The empirical evidence to date is lim-diverse as popcorn, dishwashing detergent, cat food, barbe- ited and mixed. Although many previous studies examinecue sauce, and prepared dishes. The article goes on (o cite the short- and long-term effects of advertising (e.g., Clarkeseveral marketing managers who blame couponing and pro- 1976), few focus on tiie long-term effects of promotions. Asmotions for reduced brand loyalty. Business Week (1991) al- Blattberg and Neslin (1989, p. 93) note, "Almo., no rebc,so contends that a shift of marketing dollars from advertis- has been conducted on the long term elT^ )f promotions.ing to promotions is to be blamed for the drop in the num- Yet, it is critical to a brands strategy." In summary, under-ber of consumers that buy only well-known brands from standing whether promotion and advertising hurt or help a77% in 1975 to 62% in 1990. brand in the long run appears to be relevant, important, and In light of this, some companies now believe that promo- underresearched.tions have made consumers more price sensitive, which con- We empirically examine the long-term effects of promo-sequently has lowered the effective price companies can tion and advertising on consumers brand choice decisionscharge (BrarulWeek 1993). As a result of this belief, Colgate- in mature product categories (i.e., we do not focus onPalmolive. Ralston Purina, Quaker Oats, and Procter & changes due to die product life cycle factors). Specifically,Gamble (P&G) recently have curtailed the frequency of we address the following two questions: (I) Does con-their price promotions {Wall Street Journal 1996). sumers responsiveness to marketing mix elements change over time? For example, are consumers becoming more price sensitive over time? Is the price-sensitive segment of consumers growing over time? (2) If such changes as in- •Carl F. Mela is an assi^lanl professor. College of Business Aclmiiiislra- creasing price sensitivity occur, what factors affect theselion. University of Notre Dame. Sunil Gupia is a professor. GraduaieSchool of Business, Columbia University. Donald R. Lehmann is George E. changes? For example, is the reduction in advertising ex-Warren Professor, Graduate School of Business, Columbia University. The penditures and/or increase in promotions iatluencing con-authors rhank Infornialion Resources. Inc. and an anonymous company for sumers price sensitivity to the product?providing lhe data and managerial inpiil for this sludy. and lhe edilor andthree anonymous JMK reviewers for their valuahle coin men Is. Investigation of these issues not only will enable us to better understand the changes in consumer behavior over theJounml of Marketing ReseanhVol. XXXIV (May 1997), 248-261 248
  2. 2. The Long-Term Impact of Promotion and Advertising 249lotig run. but also will provide useful marketing implications tivity (Comanor and Wilson 1974). The second theory statestor manufaclurers" pricing, advertising, and promotion poli- that advertising increases competition by providing infor-cies. To ensure consistent interpretation of long term mation to consumers that is likely to make consumers morethroughout this article, we begin by defining it. price sensitive (Nelson 1974). Mitra and Lyncb (1995) rec- oncile tbese conilicting theories by suggesting that advertis- What Is Long Term ? ing affects price elasticity through its inlluence on tvto mod- Similar to Fader and colleagues (1992), we characterize erating constructs: Advertising can increase price elasticitythe effect of marketing actions on consumers choice behav- by increasing tbe number of brands considered; it also canior as follows: decrease price elasticity by increasing the relative strength of brand preference. 1. Short-term effects: This is the immediate (e.g., weekly) elTect of proriiotion or advertising tin the sales or share of a brand. Kaul and Wittink (1995) provide an excellent review of Most receni studies focus on these effeet.s. These studies gen- the studies examining tbe impact of advertising on price sen- erally find very strong short term effect.s of promotion hut sitivity. They also suggest tbat the apparently conllicling re- very weak or insignllleant effects of advertising on brand sults of many studies might be indicative of the ditferent as- share (e.g., Guadagni and Little 1983; Gupta 1988; Tellis peeLs of the problem on which tbe researcbers inadvertently 1988a). focus. For example. Eskin and Baron (1977) study new 2. Medium-term effect.s: Some studies attempt to go beyond products, for whieb more advertising migbt attract addition- week-by week effcct.s of promotions. Most of these studies al consumers who are more price sensitive, and Krishna- use a 4- to 16 week time frame. For example, Davis. Inman. murthi and Raj (198.^) analyze a constant set of households. and McAlister (1992) study Ihe impact of promotions on con- Researcbers seem to agree that if tbe advertising message is sumer auiludes; Ehrenberg. Hammond, and Goodhardt (1994) examine promotion cflects on brand shares; and Kim and non-price-oriented and brand building, then it lowers con- Lehmann (I9).l) study changes in consumer sensitivities due sumers price sensitivity, whereas a price-oriented message to pro mot ions. Allhough many of these studies draw imphca- could increase consumers price sensitivity. tions about the long-term effects of promotions, we believe In our application, we use a product category in whicb that effects of advertising and promotion in the following 4- five of the nine brands advertise, all using a non-price ad- to-16 weeks qualify as medium-term rather than long-term ef- vertising message. Furtbermore, tbe price of these five fects. Here, we define the impact of a current peritxls adver- brands is. in general, higher than the price of tbe remaining tising/promotion on sales, share, or consumers sensilivities of the subsequent 1.1 weeks (or quarter) as the medium-term ef- four brands tbat do not advertise. According lo Boulding, fect of advertising/promotion. Lee. and Staelin (1994). who use price as a proxy for the ad- 3. Umg-tenn effecis: Previous studies find that advertising has a vertising message of a brand, firms charging bigher-than-av- substantial carryover etfect. The long-term eftect of advertis- erage price bave a natural inclination to bave a non*price ing (or prointition) is the cumulative eflect on consumers message in their advertising. Tberefore, brand choice behavior, lasting over several years. One ap- HJ: Non-price-oriented advertising is expected to reduce con- proach to capture the long-term impact of advertising is the sumers price sensitivity in the long run. distributed lag model (e.g.. Clarke 1976). We use a similar ap- proach here. Few studies, even those using disaggregate data, involve 4. Effects of changes in marketing strategy: P&Gs move frorTi conducting analysis at tbe segment level. A notable exception high-low pricing to everyday low pricing is a gcKxl example of is Krishnamurtbi and Raj (1985). who find that the impact of a change in marketing strategy. An important task for market- advertising on consumers price sensitivity is stronger for tbe ing managers is to as.sess the impact of these strategy changes on brand share or sales. Notice the distinction between long- more price-sensitive (or nonloyal) segment, and this effect is term effects and effects of a pohcy change. If a company marginal (or the less price-sensitive (or relatively loyal) seg- changes its advertising in one period only and evaluates its cu- ment. Tbis suggests a "tloor" effect; l( tbe loyal segment is mulative effect in future periods, it is measuring long-term ef- completely price insensitive, increased advertising will have fects of advertising. Conversely, if the company cuts its ad- no effect on tbis segments price sensitivity. On tbe basis of vertising expenditure in half in all periods and .studies its ef- these findings, we expect tbe following; fect on consumer choice, it is evaluating the effect of .strategy change. HJ: The long-term impact of advertising on consumers price sensitivity is likely to be stronger for nonloyal consumers Our focus here is to study the long-term efTects of pro- than for relatively loyal (and less price-sensitive) consumers.inoiions and advertising on consumers price and promotion Although several studies address tbe issue of bow adver-sensitivities. We proceed as follows: Wo start with a brief tising affects price sensitivity, almost none focus on thereview of (he relevant literature and development of effect of advertising on consumers sensitivity to promo-hypotheses. We then describe our modeling approach. This tions. To understand advertisings effect on consumers pro-is followed by data, variables, and results sections. We dis- motion sensitivity, we believe that promotions sbould be cat-cuss managerial implications of these results and conclude egorized into two groups: price-oriented and non-price-orl-with the contributions and limitations of this study. ented promotions. Advertising tbat tnakes consumers less LITERATURE REVIEW AND HYPOTHESES price sensitive also sbould make these consumers less sensi- tive to price-oriented promotions tbat primarily focus onUmg-Term Effects of Advertising price cues. Conversely, advertising should increase con- Kconomists have developed iwo theories that predict sumers response to non-price-oriented promotions thatopposite eftects of advertising on consumers price sensitiv-ity. The first theory suggests that advertising leads lo prod- We use the word promotions for all sates and irade promotions such asuct differentiation, thereby reducing consumers price sensi- lemporary price reductions, feature, coupon, display, and so on.
  3. 3. 250 JOURNAL OF MARKETING RESEARCH, MAY 1997enhance the awareness and visibility of a brand without PIMS data at the business unit level to conclude that adver-focusing on price. tising decreases and promotion increases consumers price setisitivity for large brands. H3: In the long mn, advertising will reduce consumers sensitiv- Consumer behavior theories, empirical studies, and the ity to price-oriented promolions. H4: In the long run. advertising will increase consumers sensi- previous discussion about advertising suggest that over the tivity to non-price-oriented promotions. long run, price-oriented promotions will make consumers H5: In general, these effects are likely to be stronger for the non- more price sensitive by focusing their attention on price loyal segment than for the loyal segment. cues. Non-price-oriented cues therefore will become less important to consumers. The opposite is likely to hold with As advertising is expected lo reduce consumers price increasing non-price-oriented promotions, which are likelysensitivity and build loyalty, it is reasonable to expect that it to work like advertisements (consistent with the empiricalal.so will reduce the size of the nonloyal segment. generalizations of Kaul and Wittink 1995). Therefore, Hg: Advertising will reduce the size of the nonloyal segment. H7: Over the long run, price-oriented promotions will (a) In-Long-Term Effects of Promotion crease consumers price sensitivity, (b) increase consumers sensitivity to price-oriented promotions, and (c) decrease Several theories suggest a negative long-term effect of consumers .sensitivity to non-price-orientcd promotions.promotion on consumers attitude and behavior. Self-per- HJJ: Over the long run, non-price-orientcd promotions will (a) de-ception theory implies that consumers who buy on promo- crease consumers price scnsitiviiy, (b) decrease consumers"tions are likely to attrihute iheir behavior to the presence of sen.sitivity to price-oriented promotions, and (c) increase con-promotions and not to their personal preference for the sumers" sensitivity to non-price-oriented promotions.brand (Dodson, Tybout, and Stemthal 1978). Increased pro-motion in a category is likely to lead to a perception that the Are results likely to be the same across the nonioyal andkey differentiating feature of hrands is the price (Sawyer and loyal segments? Some studies suggest that promotion sig-Dickson 1983). This simplifying heuristic then can lead to nals that do not carry price information (e.g., displays) canincreased reliance on sales promotions for choosing hrands. have a dual effect. According to Inman, McAlister, andOperant conditioning principles also suggest that frequent Hoyer (1990), these promotion signals have a positivedealing in a category can cimdition consumers to look for impact on the choice behavior of "low need for cognition"promotions in the future, thereby making them more pro- people. However, high-need-for-cognitlon people react to amotion prone. promotion signal only when it is accompanied by a substan- tive price reduction. By definition, loyal consumers are There are some theories that predict an opposite effect of more habitual buyers and respond less to price and promo-promotion. For example, leaming theory suggests that pro- tions. Therefore, it is reasonable to expect that, like the lowmotions can help a brand through increased familiarity and need for cognition consumers examined by Inman. McAlis-experience. However, this effect is likely to be small for ma- ter, and Hoyer (1990), loyal consumers will be less moti-ture and stable product categories (like the one used in our vated to process non-price-oriented promotion informationstudy), in which consumers have been familiar with almost actively, This suggests that non-price-orientcd promotionsall of the brands for a long period of time. are likely to divert the attention of loyal consumers away Empirical research in the ;irca of promotions typically fo- from price but in fact might make the nonloyal consumerscuses on identifying the short-temi eltects of promotions (e.g., focus on price even more. Inman and McAlister (1993) sup-Guadagni and Little 1983; Gupta 1988; Kamakura and Rus- port this view by suggesting that some consumers think ofsell 1989). These studies show that promotions have a large promotion signals as price-oriented promotions (prohablyshort-term elTcct on consumers brand choice. Few recent the nonloyals), whereas others think of them as a non-pricestudies examine the medium-term effect {over 4-16 weeks) of promotion (probahly the loyals). Therefore,promotion on brand share, brand evaluations, and consumers"price setisitivity. Using data from four weeks before and four H9: Non-price-oriented promotions will decrease the price sen-weeks after major promotions, Ehrenberg. Hammond, and sitivity of loyal consumers and increa.se the price sensitivityGoodhitrdt (1994) conclude that consumer promotions for es- of nonloyal consumers.tablished brands have no noticeable effect on either subse- Consistent wilh our previous discussion, we expect pricequent sales or brand loyalty. Davis, Inman. and McAlister promotions to reduce consumers loyalty by making them(1992) use a controlled experiment over three months to con- more sensitive to price and price promotions. Therefore weclude that promotions have no negative eflect on brand evalu- expect these promotions to increa.se the size of" the nonloyalations. However, these studies do not address whether promo- segment. However, the effect of non-price-oriented promo-tions make consumers more sensitive to short-tenn (weekly) tions on consumers" brand loyalty and price sensitivity isprice and promotion activities. Kim and Lehmann (1993) ex- expected to be mixed (depending on the loyal or the non-amine this issue using a four-month time frame and suggest loyal segment), which makes it hard to assess their impactthat promotions do make consumers more price sensitive. on segment sizes. Therefore, Two studies discuss the long-term effect of advertisingand promotions. Johnson (1984) analyzes 20 product cate- H|n: Price-oriented promotions are likely to increase nonloyalgories to examine changes in brand loyalty over the period segmeni size. 1975-83. He fmds no significant changes in a brands share These hypotheses are summarized in Table 1.2of requirements. However, if promotions have increasedover time, then it is difficult to say if a brands share is high -Price scnsiliviiy i.; generally negative, indicating ihal higher pricesbecause of consumer loyalty for the brand or increased reduce brand choice probahiliiy. Therefore a pttsitivc sign in ihis labie indi-hrand promotions. Bouiding. Lee. and Staeiin (1994) use cates reduciion in price sensitivity.
  4. 4. The Long-Term Impact of Promofion and Advertising 251 Table 1 SUMMARY OF HYPOTHESES (3) Impact on Consumer Sensitivity to where NonloyalLong Term Price Non-price Segment K^ = share of the sth .segment. 0 <K^< .Zn^= 1 andtmpact of Price Pmmotions Promotion.^ Size P*^^, = probability that household h in segment s buys brand i atAdvenising time t.Price PromotionsNon-price Promotions The segment-level choice probability is a logit model: •This effect is expected to be positive for loyal consumers and negative (4)for nonloyal consumers. where Pojo^ and p^^ are segment-specific parameters esti- MODELING APPROACH mated for each quarter q. Although the marketing response pimimeters, [i^^, are not We begin by briefly describing the multinomial logit brand specific within a segment, the net effect ot the mar-model we used to capture the impact of short-tenn (weekly) keting activity of a hrand is a convex comhination of seg-price and promotion activities on consumers" brand choice ment-level response parameters. The weights in this convexbehavior. We then extend this model to a segment-level logit combination depend on within-segment brand shares, whichmodel, which allows for consumer heterogeneity in typically differ substantially across brands and segmentsresponse parameters (Kamakura and Russell 1989), Next, (Kamakura and Russell 1989). This approach allows forwe suggest that the slope or llrst derivative of the logit hrand-specitic responses and consumer heterogeneity with-model with respect to, for example, price is a better measure out making the number of model parameters very large. Inof consumers sensitivity to short-term marketing activities our application, estimating brand-specific response parame-than are the response parameters or elasticities. This is fol- ters does not provide better fit or insigbts.lowed by a discussion of the distributed lag model used tocapture changes in consumers sensitivities over time as a When the segment-level logit parameters and segmentfunction of quarterly marketing activities such as advertis- sizes are estimated, we can obtain a households posterioring. Finally, we discuss estimation issues. probability (Wqjj) of belonging to a segment s in quarter q by Bayes rule:Modeling Consumers Brand Choice Behavior (5) Single-Jiegment logit. The probability that household hbuys brand i at occasion t is captured by the multinomiallogil model (Guadagni and Little 198.^): where L^^is, the likelihood of household hs purchase history given that it belongs to segment s in quarter q.(I) A Measure of Consumers Sensitivities to Short-Term Marketing Activitieswhere the deterministic part of the utility is The segment specific parameters, (J, represent consumers(2) V,f = p , . response to weekly price and promotion activities of hrands. We could use these parameters, which vary over time (quar- The vector X^, includes short-term (weekly) marketing ters), to capture changes in consumers sensitivities to short-variables such as price and promotion, as well as household- term marketing activities. However, logit models pose a spe-specific variables such as brand loyalty. The parameter p^j is cial problem because the logit parameters arc identified onlya brand-specific constant, and p is a vector of parameters for up to a scale constant (Ben-Akiva and Lerman 1985). Thisvariables X;,. One ot our key objectives bere is to test explic- scale constant is inversely proportional to the variance of theitly whether consumers sensitivities to short-term market- error in the utility function. Therefore, comparison of ping activities (which are a function of P parameters) change parameters across time or models is not desirable becauseover time and if these changes are correlated with marketing the comparison is confounded by the error variances {Swaitactivity such as advertising over the long run. Because some and Louviere 1993).long-term marketing variables are reported (at least in our It is possible to use elasticities instead of mt)del parame-data set) on a quarterly basis, we estimate (i for each quarter ters as measures of consumers sensitivities to short-termq, This is consistent with previous research (e.g., Moore and marketing variables. Unlike the elasticities in multiplicativeWiner 1987) and the managerial judgment of the company models (Krishnamurthi and Raj 198,^). the elasticities in log-that provided us the data. (In the Estimation .section, we pro- it tiiodels are scaled by brand choice probabilities. This scal-vide more details about the choice of a quarter as the time ing implies thai low-share brands will tend to have highperiod of analysis.) elasticities, though consumers are not necessarily more re- Multiple-segmt-nt logit. We allow lor consumer hetero- sponsive to the marketing aciions of stiiall-sharc brandsgeneity in response parameters by using segment-level logit {Guadagni and Little 1983). Scaling also introduces asym-models. Instead of specifying these segments a priori, we metry into cross-elasticities, even though there might he nouse the latent class or mixture modeling approach advocat- underiying asymmetry in consumer respon.se (Russell,ed by Katnakura and Russcil (1989): Bucklin, and Srinivasan 1993).
  5. 5. 252 JOURNAL OF MARKETING RESEARCH, MAY 1997 We therefore use ftrst derivatives or slopes as our measure Yi(q - Dsx ~ 3St quarter*.s respon.se value, that is. lag term;of consumers sensitivities. The first derivative represents Zjq = vector of quarterly marketing variables, such aschange in brand share for one unit change in an independent advertising, for brand i. quarter q;variable, such as price. In the logit model, for segment s the Cq = control variables, .such as economy, for quarter q;first derivative of market share of brand i in quarter q with Ctjs, Xjs, Yis. 5s = parameters to be estimated; andrespect to one unit change in an independent variable Xj is Gjq^ = enor term.given by This model has several important features. First, the model parameters (y) represent the medium-term (quarterly)(6) • i qsx impact of advertising and promotion on consumers .sensi- tivities. Second, the decay parameter {X) indicates how long For discrete (0,1) independent variables, such as promo- the effect of advertising and promotion on consumers sen- sitivities last. Third, the cumulative long-term effects (overtion, the discrete analog of the first derivative is infinite time horizon) of marketing activities, such as adver- tising, are captured hy 9 = y/( 1 - X). When the estimates and(7) Y- = the variance-covariance of y and X are obtained, the variance of the long-term effect 6 can be estimated using Cramerswhere APj^, is the change in purchase probability due to theorem, which enables us to test for the significance of &*;change in the Xj variable from 0 to 1 (e.g., from no promo- 2ytion to promotion scenario). (9) Var(9) = Var(y) + Note that there are three important features of this mea- (I - xy- (1 -sure. First, it does not suffer from the scaling problems men-tioned previously. Second, for a given value of P^^^, thismeasure is the highest when Pj^, = .5. This is intuitively ap-pealing because it suggests that consumers have the highest An implicit assumption of the model in Equation 8 is thatresponse to price and promotion when they have the highest the decay parameter X is identical for all quarterly market-level of uncertainty of whether to choose a particular brand. ing variables such as advertising and promotion. AlthoughThird, even if all brands have the same ^^^^ parameters, this makes the model parsimonious, it potentially is a verybrands will have different responses to their marketing ac- restrictive assumption. In other words, if the long-termtivities as measured hy the first derivatives. effects of advertising last for several years but the long-term In summary, in the first stage of our model, we estimate effects of promotions last for only a few quarters, then thissegment-level logits and then compute first derivatives with assumption can lead to erroneous conclusions. Followingrespect to price, price-oriented promotion, and non-price- Johnston (1984, p. 347). we test whether advertising, priceoriented promotion for each quarter of the data. promotion, and non-price promotion have different lag structures.Modeling the Long-Term Impact of Advertising andPromotion Estimation The .second stage of our model captures the impact of The model is estimated in two stages. In stage one. wequarterly marketing variables, such as advertising, on quar- estimate the segment-level logit model by maximum likeli-terly sensitivity {first derivative) estimates using a distrib- hood (Kamakura and Russell 1989). From this stage of theuted lag model. These models (e.g., the Koyck model and analysis we obtain parameter estimates for each quarter. Wethe partial adjustment model) have been used extensively in chose a quarter as the appropriate window or interval lengthmarketing to capture the carryover effects of advertising for several reasons. First, we have SV^ years of data, which(e.g., Clarke 1976). The Koyck model, for example, makes suggests that a larger window (e.g., 1 year) will leave usthe assumption that the advertising effect has a geometric with few observations for the second stage of the analysis;decay over time. The partial adjustment model assumes that second, because the median interpurchase time in our appli-in the short run consumers adjust their behavior only par- cation is approximately six weeks, a shorter window (e.g., atially to changes in the environment. For example, frequent month) will leave us with few observations and unstablepromotions can lead consumers to expect promotions in the coefficients in the first stage of the analysis; third, infonna-future, thereby making them more price sensitive. However, tion on some of the marketing variables (e.g., advertising) isthis shift in price sensitivity is likely to be gradual, as con- available on a quarterly basis; and fourth, the managers ofsumers slowly adjust their behavior to the new market envi-ronment. These assumptions lead to the following modeM: •Var(ei = g l i t where gis ihc vetlor of first derivatives of the function g Ihat define.s the reliilionship between 6 and y. K (i.e.. 6 = g(Y, ) = yl{ - >.)».where and Z is the variance-eovariancc matrix for the parameter estimates y and )L. This implies that Yjqs^ = con.sumer response in quarter q for hrand i in segment s, with respect to .short-term marketing variable X, .such as price; Var(G) = r X) [Cov(y. Cov(y. A.) Var(X) 39 rlX Bolh Ihc Koyck and ihc partial adjuslmeni models formulations arc.similar to Ettuucion 8. Hciwcvcr, in ihc Koyck mtxicl. errors are .•;erially cor-reblcd. wherea.s these errom are independent in ihe partial adjustmentmodel (John.ston 1984). We tesi for error correlation in our eslimalion. Appropriate substitution then leads lo Equation 9.
  6. 6. The Long-Term Impact of Promotion and Advertising 253[he company that provided us the data believe that most sig- dent variables (such as price and promotion) as per Equationnificant marketing decisions are made on a quarterly basis. 6 or 7. We then use these first derivatives as dependent vari- Our initial attempt at estimating .segment-level logit mod- ables in Equation 8. which is estimated by ordinary leastels on quarterly data indicates that even a quarter is too short squares.a period to obtain stable parameter estimates. Given thisdilemma (i.e., a desire lo have quarterly parameter estimates DATAso that we have enough observations for the second stage The data and managerial input for our application wereanalysis, while needing a longer time interval to obtain more provided by a major consumer packaged goods companystable parameter estimates in the first stage), we use a and Information Resources, Inc. The data contain panel,rolling three-quarter window for estimating the segment- store, and demographic infonnation for a consumer non-level logit. Specifically, we assume the parameter estimates durahle category in one market area for 8 ^ years, from Jan-for data covering quarters 1, 2, and 3 to represent quarter 2. uary 1984 to March 1992. The spon.soring company alsoestimates from quarters 2, 3, and 4 to represent quarter 3, provided quarterly advertising expenditures for all brands.and so on. Although this procedure could smooth out some Because scanner data began to proliferate approximately tenof the quarterly fluctuations in coetficients, it also will years ago, it is only now tbat researchers can use this infor-smooth out random error. In the spirit of moving averages, mation to make long-term inferences regarding changes inthis prtKcdure captures the overall trend in coefficients in consumer behavior. Our study is one of the llrst to utilizethe 8!^ years of data. More important, it provides us with this long a period of disaggregate data.more stable parameter estimates, which are essential for We cannot reveal the product category or the brands. Thedrawing meaningful conclusions.^ category is a household nonfood product. The median inter- When we obtain quarterly estimates of p parameters, we purchase time is 6 weeks, and the mean is 12 weeks. Thethen can compute tlrst derivatives with respeet to indepen- category has several characteristics that make it particularly suitable for our application. First, like most other consumer We conducted an oilcnsive sensiliviiy unalysis fur our choice of the packaged goods, advertising in this category bas declined"window" length of ihree quarters. Using a sub-sample of Ihc data, we over time relative to promotion (Figure 1). This enahles uscompared ihe coctTicicnls of the three quiirter window wilh the coefficients to examine the effecis of these changes, if in fact they exist.from one-quarter and five-quancr windows. The results were not slatisti-caliy significanlly diflerent acrt)ss thtrsc scenarios. We acknowledge that Second, this is a mature product category. Therefore, our re-using the three-quarter window parameters could inflaie the lag parameter sults will not be confounded by changes over the productin fy.]u;itinn 8. life cycle (Sethuraman and Tellis 1991; Tellis 1988b). Final- Rgure 1 CHANGES IN CATEGORY MARKETING ACTIVITY Frequency of Discounts Advertising Percent Dollars (000) 40 500 30 200 • 10 100 0 I IIIII IIIM IIIIII I II1IIMM I 0 1984 1985 1986 1987 1988 1989 1990 1991 1992 1984 1985 1986 1987 1988 1989 1990 1991 1992 Time Time
  7. 7. 254 JOURNAL OF MARKETING RESEARCH, MAY 1997ly, there were no major brand entries or exits during the pe- (e.g., advertising, feature) are brand oriented rather than de-riod to complicate the analysis of advertising and promo- signed specifically for a brand size. Previous studies usetional effects. brand, instead of brand size, for similar reasons (Krishna- The category consists of eight major brands that account murthi and Raj 1991).for approximately 85% of the market. We consider all otherbrands, along with private-label and generic brands, to con- VARIABLESstitute a ninth brand. There are four major sizes, and there is Short-Term (Weekly) Variablesa great deal of switching among sizes. The two mediumsizes repre.sent appro.ximately three-quarters of the market We include the following short-term variables X^, (forshare, and the largest and smallest account for approximate- household h, brand i, occasion t) in the logit model.ly one-eighth each. There are 11 stores in this particular Price. Price for a brand is the actual shelf price in dollarsmarket. The panel data include l.i90 households making per ounce, net of all discounts. The existence of multiple54,731 product purchases over 8!^ years, that is. 33 quarters. brand sizes poses a problem for creating a price variable torThe demographics of the panelists roughly match the demo- competing brands. We use mininwm shelf price per ouncegraphics of the United States on age, family size, education, across different sizes of a brand as the price for that brandand children; though the panelists in our study had slightly for several reasons. First, in our data the average unit pricehigher incomes. Approximately 92% of households pur- differential among the three largest brand sizes, which con-chased more than one brand during the period of the study. stitute the bulk of purchases, is approximately 3%. Second,Households did not enter or exit the panel during the study the average price differences across brands are far greaterperiod. The lack of entry or exit could raise questions of than are the average price differences across sizes of thematuration or mortality, however, any disaggregate long-du- same brand. Third, and most important, a weigbted averageration study will suffer from this limitation/ By having the price fonnulation is likely to underestimate the true pricesame households, we are uniquely able to monitor exposure variation in the marketplace. For example, if a retailer offersto promotions and track concurrent changes in behavior. As a 15% discount on the largest size of a brand that accountswe mentioned previously, conflicting results about the im- for, say, one-eighth of the volume, then the weighted aver-pact of advertising on consumers" price sensitivity can be age percent discount will be (.15 x 1/8). or less than 2%.explained partially by whether advertising is attracting new Given size switching in this category, such averaging willprice-sensitive consumers {Eskin and Baron 1977) or reduc- understate price effects. Fourth, consumers swiich heavilying the price sensitivity of existing consumers (Krishna- among sizes, which suggests that minimum price per ouncemurthi and Raj 1985). By having a fixed set of households, more accurately reflects their price search behaviorwe avoid this potential confounding. Table 2 provides some Price promotions. We classify three types of promotionsdescriptive statistics of the data. as price promotions: (!) temporary price reduction (TPR), (2) feature, and (3) coupon. Temporary price reduction and Although most brands in our product category have four coupons always are accompanied by price di.scounts and aresizes, our unit of analysis is a brand, not brand size. This therefore clearly price-promotion signals to consumers. Fea-cboice was made for tbe following reasons: First, with nine tures almost always are accompanied by prominent pricingbrands and four sizes, even a single-segment logit model information with Mttle product information.will have a large number of parameters. This problem in-creases dramatically in a multi-segment logit tnndel. Sec- We define a brand to be on feature or TPR, or as offeringond, a model witb 36 br:ind-size combinations is likely to vi- a coupon if any size of that brand is on feature or TPR or of-olate the UA property of the logit model. Third, manage- fering a coupon. Although the data provide infonnationment believes that most marketing actions in this category about coupon redcmpti<m. there is no infonnation about tbe recency of coupon drop. Therefore, we assume a coupon drop for a brand occurred in a week if the total number of ^tn the spirit of cohort analysis, we checked our data to sec il" the per-centage of purchases on discounl or brand switching for three dilfcrcnt age redemptions for that brand by all households in the panel da-groups of panelists dilTered over time. We found no sigaificant dillerences. ta exceed one standard deviation higher than the average Table 2 DESCRIPTIVE STATISTICS OF THE DATA Market Price Advertising Share (S/oumes) ($000) TFR Feature [hsptuv CouponBrand 1984 199} }9!i4 }99} }984 /yy/ I }99} t984 }99} }984 }99I }984 }99I1 .38 .38 .057 .060 393 128 .27 .55 .04 .09 .08 .19 .07 .102 .07 .10 .054 .059 142 30 .12 .40 .07 .03 .05 .14 .12 .043 .19 .13 .057 .063 225 57 .12 .43 .01 .04 .00 .12 .14 .044 .04 .10 .052 .056 144 0 .10 .54 .01 .04 .04 .19 .00 .045 .08 .07 .057 .062 278 44 .08 .42 .00 .05 .02 .15 .05 .156 .09 .06 .049 049 0 0 .03 .15 .00 .01 .02 .05 .14 .047 .04 .03 .029 .0.34 0 0 .04 .08 .00 .01 .00 .02 .25 .068 .00 .04 .061 .054 0 0 .08 .28 .00 .02 .00 .05 ,00 .429 .11 .10 .024 .030 0 0 .18 .21 .01 .01 .02 .06 .06 .04 TPR (temporary price reduction). Feature. Display, and Coupon variabtes rcpresetit ttie proponioti of times the category was discounted, featured, dis-played, or couponed in that year. i
  8. 8. The Long-Term Impact of Promotion and Advertising 255weekly redemption for that hrand across the entire period of which we use quarterly marketing variables to explainthe study. In other words, a significant increase in coupon changes in consumers sensitivities. These results provide aredemption is assumed to be an indicator of a recent coupon test of our hypotheses.drop. Non-price promotions. We classify display as the non- Stage One: Estimating Segment-Level Logit Modelsprice promotion variable. Typically price is not the dominant Choosing the number of .segments. For each time period,focus of displays. Similar to features and TPR, we define we calibrated the single-segment choice model and thendisplay as a binary (0.1) variable that is 1 if any size of the proceeded to fit multi-segment models for two and threebrand is on display. 0 otherwise. segments.** We used the Bayesian Infonnation Criterion Brand loyalty. To contrt)l for observed differences in (BIC) and segment interpretability as guidelines to select thehouseholds purchasing patterns, we include a brand loyalty appropriate number of segments (Bucklin and Gupta 1992).variable (e.g.. Guadagni and Little 1983). A households For all time periods, the BIC values favored a two-segmentloyalty for a brand is defined as that brands share in the last solution over a single-segment solution. Three-segmentfour purchases of that household (on average four ptirchas- solutions were occasionally better, and ofien worse, than thees represent one year of purchase history in this category). two-scgmont solutions. Across time periods, the average andPrevious studies use similar or even fewer purchases to as- maximum gain in BIC in going from two to three segmentssess brand loyalty (e.g.. Chiang 1991 used four weeks of were .30 and 23, respectively. Even the maximutn gain incoffee purcha.ses). Our tneasure enables a households loy- going from a two-segment to a three-segment solution rep-alty for a brand to cbange over time, tbat is, we do not as- resented a percentage gain oi only .5% in the BIC for thatsume that hrand loyalty for a household remains constant period, at the expense of 15 additional parameters. Becauseover eight years. a constant number of segments over time facilitates inter- pretation (Moore and Winer 1987). we chose a two-segmentQuarterly Variahles solution for all titne periods. Quarterly variahles (Z,^) for each brand i and quarter q are The two segments appeared to be relatively loyal and non-ope rationalized as follows: loyal segments, which is consistent with the conceptualiza- Advertising. Advertising is the total advertising expendi- tion of Krishnamurthi and Raj (1991), Bucklin and Guptature (in intlation adjusted dollars) for brand i in quarter q in (1992). and others.the market area.** Average sensitivities of consumers across all time periods Price promotions. Once again, tbe intensity of price pro- were as follows:motion by a brand in a quarter is captured by tbree types of Consumer Nonloya}promotions: (I) TPR, (2) feature, and (3) coupon. Specifi- Sensiiiviiv lo Segmentcally, we define tbe feature activity for brand i in quarter q(Fn,) as the proportion of times brand i is on feature across Price .28 -1.70 Price promotion .02 .04all stores and all weeks of that quarter. Similarly, we define Non-price promotion .03 .09frequency of price discount.s as the proportion of times abrand is on TPR in a quarter Finally, coupon intensity for These results show ihal, as expected, consumers in thebrand i in quarter q is defined as the proportion of times that nonloyal .segment are more price and promotion sensitivebrund uses C[)upons in that quarter. Quarterly price promo- than are consumers in the loyal segment. The price sensitiv-tion is defined as a sitnple average of quarterly feature, TPR, ity (or slope) estimates are equivalent to price elasticities ofand couponing activity of a brand. -.51 for the loyal segment and -1.02 for the nonloyal seg- Non-price promotions. Our non-price promotion variable ment. These estimates are somewhat lower than the averageis the proportion of times a brand is on display across all of-1.76 reported by Tellis (1988b), who used a meta-analy-stores and all weeks of that quarter, operationalized similar sis of several product categories.to the long-term feature variable. Consumers .sensitivities over time. Over the 29 quarters RESULTS of the calibration period, in addition to segment size and brand-specific constants, we estimated 348 response para- We first present the results of the first stage analysis, in meters in the first stage of our analysis." Of these. 316 co-which we calibrate segment-level logit models to obtain efficients were significant and correctly signed (e.g., thequarterly parameter estimates and consumers sensitivities. price parameter was negative, as expected), 8 were signifi-Next, we discuss results from the second stage analysis, in cant and incorrectly signed, and 53 were insignificant. No- tice that under the null hypothesis of zero paratneter value, Results were insensitive to slightly different operauona!i/;atiiins of using a 5% significance level would yield 348 x .05 = 17 co-lirand loyally. Also, respi.tn.se parameters in the first stage were .similar formodels with or without tlie hrand loyalty variable. "Because [i parameters are esiimated for a quarter, it could be unreason- also compared a single segment mixlel with brand specific parame-able tosuj^gest that adverti.sing tor the f/tdrf current quarter (including cnd- ters for price, promotion, and so on with a .single segment model in whichitf-quiirter advertising) affects consumers sensitiviiies for that quarter the parameters for short.term marketing variables were constrained to be(which include consumer response al the beginning of the quarter as well). the same across brands. LIsing Bayesian Informaiion Critcrmn (whichTherefore, the quarterly advertising variable was created by phase-.s}iifiinf; penali/.es a model for estimating additional parameters), a model with theadvertising by half a quarter. In other words, for the current quarter of .same parameters for brands performed hctier than did a nunlcl with brand-April-June, advenising was detmed as half the advertising expenditure for specific parameters. Moreover, this constrained mi.>del was found to tx;April-June plus half the advonising expenditure for January-March. We more stable over time.also delined the lag terms accordingly. Once again, this adjustment did not "For each quarter we estimated 12 response parameters: 6 parametersaffect Ihe empirical results. We obtained similar results for other quarterly for each of the two segments. Therefore for the 29 quarters, we get 6 x 2 xvariables. 29 = 348 coeftkieius.
  9. 9. 256 JOURNAL OF MARKETING RESEARCH, MAY 1997etTicients as significant, by chance. In other words, it is pos- Table 3sible to obtain approximately 8 coefficients as significant CHANGE IN CONSUMERS SENSITIVITIES OVER TIMEand correctly signed, and 8 coefficients as significant and in-correctly signed, by chance. Consumers Chaniies Significance Next, we used these estimates of parameters and segment Sensitivity to Over Time Levelsizes and Equations 6 and 7 to compute consumer sensitivi- 1. Loyal Segmenlties to price, price promotion, and non-price promotion for Price Increase P<.001each quarter q." We perfonned a simple trend analysis for Price [*romolion Increase p<.00!each brand and segment by regressing quarterly sensitivities Non-price Promotion Decrease p<.05against time. To control for exogenous macroeconomic fac- 2. Nonioyal Segmenltors, we used recession (0,1) as a covariate because con- Price Increase p<.00sumers couid become more price sensitive during a recession Price Promolion Increase p<.00period. Consistent with the economics literature, we define a Non-price Promolion Decrease p<.05quarter to be in recession if it was a period of three or moreconsecutive quarters with negative growth in gross domestic Nonloyal Segmenl Size Increase p<.05product. Two interesting results emerged from this analysis.First, consumers in the nonloyal segment became more pricesensitive over time. whcrea.s loyal consumers showed little changing over time?"—is a resounding yes.2 Of special in-change in their price behavior. Second, the nonloyal con- terest is the result that price and price promotion sensitivi-sumers also demonstrated increasing sensitivity to price pro- ties are increasing over time. Our findings have importantmotions and reduced sensitivity to non-price promotions, implications for managers and researchers who attempt tothough these effects were weaker than were the effects for draw empirical generalizations using meta-analysis or toprice. The promotion effects for the loyal consumers were in compare the results of two or more studies conducted overthe same direction as for the nonloyal consumers, though the different time periods. For example, Tellis (1988b) andbrand-level coefficients were not significant. Sethuraman and Tellis (1991) find average price elasticity to We also conducted a trend analysis for the nonloyal seg- be around -1.7. However, our results suggest that these av-ment size (Ti) with ln(Ti/l - 7i) as the dependent variable, and erages are likely to change over time. We next examine ihetrend and recession as the independent variables. This re- reasons for these changes.gression showed a significant increase in the size of ihe non-loyal segmenl over time, which indicates that an increasing Stage Two: Impact of Marketing Activity cm Consumersproportion of consumers have become more price and pro- Sensitivitiesmotion sensitive over time. The puqxise of the second stage analysis is to test explic- Pooling across brands. It is possible that consumer sensi- itly if advertising and promotion aftect consumers price andtivities for most brands could show a positive but insignifi- promotion sensitivities over the long run. We used the dis-cant trend. However, if we conclude from these results that tributed lag model described in Equation 8. in which con-consumer sensitivities are not changing over time, we may sumers" sensilivities (i.e.. first derivatives) to weekly price,be making a Type II error by not appropriately pooling in- price promotion, and non-price promotion activities for eachformation across brands (Dutka 1984). Pooling results brand and segment were the dependent variables, and qutU"-across brands also can be thought of as a "meta-analysis" teriy advertising, price promotion, and non-price promolionacross several brands. of that brand were the independent variitblcs along with the One possibility is to run u c«/egory-level regression, lagged dependent variable. We also included recession as anwhich gives us a single-trend parameter for the category independent variable to control for exogenous macroeco-rather than separate trend parameters for the nine brands. Al- nomic factors. We first ran Durbins test to check for serialthough this approach increases the sample size and pools ihe correlation of errors in a distributed lag model (Johnstoninformation across brands, it also could lead to erroneous re- 1984, p. 318). Out of 54 regressions (2 segments x 3 depen-sults if the parameters for brands arc so far apart that pool- dent variables x 9 brands). 47 regressions had no correlationing across brands increases the variance of the category-lev- in the errors. Therefore, our formulation seems to be con.sis-el parameter, making it insignificiint. Treating ihe results tcnt with the partial adjustment model. Next, we performedfrom different brands as independent replications, we use a test as per Johnston (1984. p. 347) to see if the decay para-Fishers pooling test (Fisher 1948; Rosenthal 1978). Pooled meter k is different for advertising, price promotion, andresults that arc based on this test arc summarized in Table 3. non-price promotion. For all nine brands, we could not These results suggest that the answer to our first ques- reject the null hypothesis that all of the decay parameters aretion—"Are consumers sensitivities to marketing variables the same. To contlrm furlhcr whether the long-term series of consumers sensi- tivities to price and promotion is evolving, for ail nine hrands we performed the unit roiit test suggested by Dekimpe and Huns.scns (1995a). We found strong evidence of evolving sensitivities in ihe nonloyal segment and some- Tt) ohioin consumers" sensitivity lo price pn>moUon. we used a simple what weaker evidence for the loyal segment, We also fnund the advertisingaverage of eonsumers sensilivity lo TPR. feature, and coupoos. Results and promotion variables lo have unit roots. We thank Marnik DeKimpe forwere not sensitive to difTerenI weighting schemes. making the necessary program available for this test.
  10. 10. The Long-Term Impact of Promotion and Advertising 257 Using this brand- and segment-specific regression, we es- of this effect is on the nonloyal consumers.^ Ftirthermore, wetimated the Y parameters associated with each of the quar- find that less advenising helps to increase the size of the non-terly variahles (see Equation 8). These parameters rellect the loyal segment. This dual effect (i.e., a redtiction in advertisingquarterly (medium-tomi) effect of advertising and promo- leading to an iticrease in the size of the nonloyal segment and making consumers more price sensitive) shows a poweriultion on consumers response sensitivities. Results from these role of advertising in reinforcing consumer preferences lorhrand-speciHc regressions were pooled according to Fish- brands. Table 4 also shows that reduction (increa.se) in adver-ers method. We estimated the long-temi impact o( advertis- tising reduces (increases) consumers sensilivity to non-priceing and promotion on consumers sensitivities [9 = Y( ~ ^)l promotion for the nonloyal consumers. In other words, adver-and its variance for each brand and segment. We then pooled tising makes non-price promotions such as displays more ef-brand-level results using Fishers method. fective. Moreover, our results indicate that if u market has a large "loyal" segment, then using an unsegmented model ean Finally, we also regressed the size of the nonloyal seg- lead to insignificant and/or substantially smaller advertisingment (jc) against quarterly marketing variables. Specifically, effeets. This might explain partially the inability of many re-we used log(7i/l- K) for each quarter as the dependent vari- searchers to fmd any advertising effects iti scanner data.able and quarterly advertising, price promotion, non-pricepromotion, and recession as independent variables along 2. Price pnmwtions. As expected, in the long aiti price promo-with the lagged dependent variable. Because the segment tions make consumers more price .scnsilive in both the loyal and nonloyal segments. They also train nonloyal consumers tosizes were not brand specific, we used the mean category look for promotions, thereby making them more sensitive tolevel variables (e.g., average category advertising) as our in- price promotions.dependent variables. The purpose of this regression was to 3. Non-iyrireptimiotinns. Non-price promotions have signitlcantsee if. for example, decreased advertising expenditure made effects only on consumers priee sensitivities. Consistent withconsumers less brand loyal. our hypotheses, these promotions make loyal consumers less price sensitive and make the nonloyal consumers much moreThe Long-Term Effects of Advertising and Promotions price sensitive. These results are consistent with prior litera- ture that suggests opposite effects of certain promotions for Results of both the medium-term (over one quarter) and different consumers.long-tcnn (over infinite horizon) impact of advertising andpromotion on consumers" price and promotion sensitivities Several additional insights emerge from our analy.ses.are given in Table 4. To facilitate comparison and discus- First, the results are identical for the medium term and kmg-sion, we have also included the hypothesized signs for these term effects of advertising and promotion (except for theeffects. Overall, we found support for many of our hypothe-ses. Although some of the results were insignificant, none I Although our data show advenising expenditures to be declining overwere signillcant in the wrong direction. lime, we ex[K.vt iiur results lo be bidirectional (i.e., we expect an increasc/dccrcjse in advenising expenditure to rcduce/incrcasf consumers . Advertising. Consi.stenl with several previous studies (e.g., price sensiiiviiy). Also, when the price parameter (which i.s negative) is Krishnamurthi and Raj l)85). we tlnd that a reduction in ad- used as a dependent variable, a positive coefficient for advertising implies vertising make.s con.sumers more price sensitive and that most that advertising reduces price sensitivity. Table 4 HYPOTHESES AND RESULTS I. Hvpothisized Signs Loyal Segment Nonloyal Segment Impact on ("onsumer Sensitivity lo Impact on Consumer Sensitivity to Ntmhyaltaring-Term Price Non-price Lfmi; Term Price Non-price SegmentImpact of Price Promotions Promotions Impact of Price Promotions Promotions SizeAdveni.singPrice PromotionsNon-pdco Promotions •«- I + - + Advenising Price Promotions Non price Promotions : I * 2. Results for Medium-Term EffectsAdvenising ns ns ns AdvenisingPrice Promotions ns ns Price Promotions ns nsNon price Promotions ns ns Non price Promotions ns ns HS i. Results for Long-Term EffectsAdveriisini: ns ns ns Advenising + ns 4-Price Promotions ns as Price Promotions •t- ns nsNon price Promotions ns ns Non-price Promotions ns ns ns Note: Price sensitivity is generully negative. Therefore, a positive sign (e.g.. ad-price) indicates a decrease in price sensitivity. Significance is based onpooled Fisher test wilh p < .05.
  11. 11. 258 JOURNAL OF MARKETING RESEARCH. MAY 1997 Table 5 AVERAGE EFFECT OF ADVERTISING AND PROMOTION ON CONSUMERS* SENSITIVITIES REGRESSION RESULTS FROM SECOND-STAGE ANALYSIS iMyat Segment Nnnloyal Segment Price- Non-price- Price- Non-price- Nontoval Price Promotion Promotion Price Promotion Promotion SegmeniImpact of Sensitivity Sen.silivity Sensitivity Sensitivity Sensitivity Sensitivity SizeUg(>.) .384* .461* .410* .516* .285* .389* .639*Advertising .0007 .000 .000 .008* .000 .0003* -.0001*Price Promotion -.374* -.023 -.006 -1.2O7* .080* .Oil .059Non-price Promotion .349* .041 .011 -4.368* .020 -.029 -.047Recession -.106" -.003 -.012* -.015 .005 -.001 -.001R^ .29 .32 .39 .56 .35 .27 .74 Numbers in ihis table are weighted averages of Ihe parameters from brand-level regressions, where weights are proponional lo inverse of Ihe parametervariances (R- is a simple, nol weighied average across brands). Therefore, they represent medium-term effects of advertising and promotion on consumerssensitivities. Parameters with p < .05 based on pooled Fisher test are repre.sented by an a.sterisk."effect size," which we discuss subsequently). Second, the size in the medium term for each of the two segments.average R- for the second stage analy.sis is .37 (R- = .26). long-temi effect size is then simply the medium-term effectThe model fit j s slightly poorer for the loyal segment (aver- size divided by (I - ), where is the decay parameter.age R- = .34. Rl= .21) than tor the nonioyal segment (aver- We give the effect sizes in Table 6. Three key resultsage R2 = .39, R2 = .30). This is an intuitively appealing emerge from this table: First, consistent with previous re-result, which suggests that long-term marketing variables search, advertising effects are generally smaller than pro-can explain better the changes in the behavior of nonloyal motion effects; second, long-term effect sizes are approxi-consumers, who by definition are affected by marketing mately 1.5 to 2 times larger than medium-term effect sizes;variables more than are loyal consumers. This is also and third, the changes in price sensitivities are greater thanreflected in more significant signs for the nonloyal segment the changes in promotion sensitivities.than for the loyal segment. Third, the decay parameter Xvaries from .29 to .52 with an average of .41 (see Table 5). Summary of ResultsThis indicates that on average the long-term effects of adver- In summary, our results suggest lhe following:tising and promotion are 1/(1 - .41). or 1.7, times theirmedium-term (quarterly) effects. The decay parameter also •Advertising helps a brand in the long run by making con.sumerssuggests that 90% of tbe cumulative effect of advertising (especially nonloya! ones) less price sensitive as well as by re-and promotion on consumers sensitivities occurs within ducing the size of the nonloyal segment. "In the long run. price promotions make both loyal and nonloy-three quarters, which is similar to the result obtained by al consumers more sensitive to price. An increa.sed use of suchClarke (1976). Finally, though recession bad no impact on promotions also trains consumers (especially nonloyai ones) toconsumers sen.sitivities in the nonloyal segment, it look for deals in the marketplace. On average, we find these ef-increased consumers price sensitivity and reduced tbeir fects to be almost four times larger for nonloyal consumers tbannon-price promotion sensitivity in the loyal segmeni. for relatively loyal consumers. •As expected, we found thai non price promotions have difFerentHow Large Are the Lang-Term Effects of Advertising and effects for loyal and nonloya! consumers. Although these pro-Promotions? motions act like advertising for loyal consumers, making ihcm less price sensitive, tbey make the nonloyal consumers focus Our previous discussion indicates the directional effects even more on prices. In other words, these promotion.s reduce theof advertising and promotions. It is also useful to assess the price sensitivity of loyal consumers but significantly increa-se themagnitude of these effects. We estimated the medium- and price sensitivity of nonloyal consumers. The effects lor nonloy-long-term effect sizes as follows. als are almost 12 times larger tban the effects for loyals. To estimate the medium-term impact, 7. we note from To the extent that increased price and promotion sensitiv-Equation 8 that the parameter y = dY/3Z represents the ity of consumers can be considered undesirable outcomes,change in consumers sensitivity (Y) for one unit change in our results confirm the conventional wisdom that in the longthe quarterly variable Z. Tberefore, (y/Y) x 100 is the per- run, advertising has "good effects and promotions havecent change in consumers sensitivity for one unit change in "bad" effects on consumers brand choice behavior. Note, We use the average Y and Z values to arrive at the effect however, that these results are ba.sed on the analysis of only one category in one market. ^Striclly speaking, we should evaliiale changes in Y for each brand, seg- f),Share Price ment, and quarier. However, our "aggregale" approach should provide us .,, Price . . HBeeausc price elasiieity ri = - Y It IS easy to with a good approximation. For example, in the conlexl ol di.screte choice e Share Share models, Ben-Akiva and Lerman (19K5) indicate that errors due to aggrega-show thai under certain conditions. X 100 also represents percent tion across consuniers are relatively small if all consumers have the samechange in x for one unit change in Z. choice set.
  12. 12. The Long-Term Impact of Promotion and Advertising 259{.•isues of Causality b) for the following reasons. Increasing promotion and re- This study is correlational in nature. It is possible that duced advertising makes consumers more price and promo-changes in consumer behavior bave led to changes in mar- tion sensitive. This leads finns to offer even more price pro-keting activity rather than changes in marketing activity lead- motions to obtain short-term gains. However, all finns en-ing to changes in bcbavior. We empirically tested for causal- gage in this action. This form of competitive action bas two effects; First, it makes tbe overall promotional intensity inity by running a simultaneous equation model as follows; tbe category higher, thereby making consumers even more Price sensitivity = f(Lag price sensitivity. Ad. PP. NPP, Recession) price sensitive over time (as we find); and second, competi- PP sensitivity = f(Lag PP sen.sitivity. Ad. PP. NPP. Recession) tive matching of promotional spending leaves tbe relative NPP sensitivity = f(Lag NPP sensitiviiy. Ad. PP. NPP. Recession) promotion across firms to be largely the same, thereby bav- Ad=f(Lag Ad of brand. Competitive Ad. Price ing little effect on tbeir market share. In other words, it is sensitivity. PP sensitivity, NPP sensitivity) conceivable tbat though consumers are becoming more price PP=f(Lag PP of brand. Competitive PP. Price sensitive because of increased promotional activities of var- .sensitivity. PP sensitivity. NPP sensitivity) ious fimis. "tbe offsetting competitive activity plays a role in NPP = f{Lag NPP of brand. Competitive NPP, Priee the maintenance of what appears to be stationary and zero sensitivity, PP sensitivity. NPP sensitivity), order behavior" (Bass et al. 1984, p. 284). leading to sta- tionary market shares. This highlights tbe importance ofwhere PP is price promotion and NPP is non-price promo- studying tbe long-term effects of promotion and advertisingtion. The first three equations represent our second-stage on not only tbe changes in market share or sales, but also onanalysis and the last tbree equations capture the reverse changes in consumer behavior.causality (i.e., the effect of changes in consumers sensitivi-ties on marketing activities). We estimated tbese equationsat a brand and segment level using two-stage least squares. CONCLUSIONWe then pooled the results across brands using Fishers pro- Our purpose was to develop and empirically test a modelcedure, as indicated previously. to understand tbe long-temi impact of advertisitig and pro- Of the seven significant results obtained in Table 4. four motion on consumers brand choice behavior. Specifically,sbow causality only in the proposed direction alp < .03. and we set out to find if consumers responsiveness to marketingtbree show bidirectional effects (and two of them have p- mix variables cbanges over time and, if so. why. To addressvalues tbat are almost ten times smaller in tbe proposed di- these issues we used a unique data .set that includes storerection tban in the reverse direction). Although these results environment and purchase history of more than 1500 buuse-do not rule out reverse causality completely, they do lend hold for 84 years for one frequently purchased consumersupport for the proposed causal effects. packaged good in one market. At least in our data set, consumers have become moreCompetitive Reaction price and promotion sensitive over time. We found two seg- Recent articles by Lai and Padmanabban (1995) and ments of consumers—^loyal. or relatively less price-sensitivel^cKimpe and Hans.sens (1995a. b) suggest tbat market consumers, and nonloyal, or price-sensitive consumers. Ourshares arc stationary (i.e.. not evolving over the long run) for results show that the size of the nonloyal segment has growna majority of product categories. Lai and Padmanabban over time. In otber words, a larger number of consumers(1995) also llnd the long-term effect of promotions on mar- have become increasingly more price and promotion sensi-ket shares to be largely insignificant. tive over time. In contrast, we find long-term effects of advertising and We used quarterly advertising and promotion policies ofpromotions on consumers sensitivities to price and promo- brands to help explain tbese changes in consumer behavior.tions, not on tbe market shares of various brands. We con- Our results confirm the conventiotial wisdom tbat in tbe longjecture that our findings are consistent with those of Lai and run. advertising reduces consumers price sensitivity andPadmanabhan (1995) and DeKimpe and Hanssens (1995a, promotions increase consumers" price and promotion sensi- Table 6 MEDIUM- AND LONG-TERM EFFECTS OF ADVERTISING AND PROMOTION ON CONSUMERS SENSITIVITIES Loyal Segment Nonloyal Segment % Change in Consumers Sen.titivily to % Change in Consumer. Sensitivity to s Price Non price Price Non-price1% Increase in Price Promotion Promotion Price Promotion PromotionMvdium-Term Effects Advertising ns ns ns .15 ns .10 Price Promotion -.37 ns ns -1.20 .08 Non-price ProintMion .35 ns ns ^.37 nsUmgTenn Effects Advertising ns us BS .30 .15 Price ProiiTolion -.61 HS ns -2.50 .It Non-price Promotion .57 ns ns -9.02 ns ns We focus on only significant effects {p < .05). Note that negative numbers for price imply increase in price sensitivity.

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