Download pdf - Coupon face value: Its impact on coupon redemptions, brand sales, and brand profitability

Coupon Face Value: Its Impact on Coupon Redemptions, Brand Sales, and Brand Profitability

ROBERT P. LEONE Ohio State University

SRlNl S. SRINIVASAN Drexel University

Although much resecwch has exczmined the impact of coupor~s on redemption rates, incremented sales.

and marker Jhare, only ct few studies have addressed the impact of coupons on bmndprojituhility. One

possible reason is luck of reudily trwilable profitrrbility data In the absence of such dutn, researcher.s

have used munqerictl judgments (Neslin cmd Shormuker, 1983) and experiments (Chapman. 1986) to

inveestigclte the profitability of roupons. We propose an integrative framework for evcduczting the impcrct

of coupon ,filce v&e on brand profitability and implement it by using readily ctvcrilrble sccmner data.

The research reveals that when N manufacturer optimizes the market-level profitrzbility from u coupon

prognrm, projt for individual chains in the market could be suboptimal.

INTRODUCTION

Coupon promotions continue to be among the most popular types of consumer promotions for manufacturers. Donnelley Marketing Inc. (1995) reports that about 327 billion coupons were issued in 1994 and that the average face value of coupons issued by manufacturers increased by 7% in that year. Also, Nielsen Clearing House (1993) reports that more than 75% of households use coupons in some product category. Past research on coupon promotions has investigated redemption rates and redemption patterns (Inman and McAlister, 1994; Reibstein and Traver, 1982), profiles consumers redeeming coupons (Neslin and Clarke, 1987; Teel, Williams, and Bearden, 1980), the impact of coupons on market share (Neslin, 1990), and the profitability of coupon promotions (Chapman, 1986; Klein, 1981; Neslin and Shoemaker, 1983).

To calculate the profitability of coupon promotion programs, we need to understand their costs and benefits. On the benefits side coupons traditionally are viewed as devices used to price discriminate between consumers with different price elasticities (Narasimhan, 1984).

Robert P. Leone, Ohio State University, Department of Marketing. Columbus, OH 43210. Srini S. Srinivasan,

Drexel University, Department of Marketing, Philadelphia, PA 19104.

Journal of Retailing, Volume 72(3), pp. 273-289, ISSN: 0022-4359 Copyright Q 1996 by New York University. All rights of reproduction in any form reserved.

273

274 Journal of Retailing Vol. 72, No. 3 1996

With a regular shelf price cut, every shopper can take advantage of the reduced price. How- ever, with coupon promotions, only consumers who spend the time and effort to redeem coupons can take advantage of the reduced price. Studies by Bawa and Shoemaker (1987) and Srinivasan, Leone and Mulhem (1995) indicate that coupons not only have redemptive value, but also can have advertisement value to some consumers. On the cost side, one of the major costs of a coupon program is the value of the coupons redeemed. If only consumers who would have bought the promoted brand anyway redeem coupons, then coupon redemptions would always result in lower profitability.

Profitability of the couponing operation depends on both the incremental sales and the redemption rate. We believe the question of whether coupon promotions are profitable is too broad to have a single answer. For some brands, in some markets, dropping coupons in some particular range of face values might be profitable whereas dropping coupons with face values outside that range may not be profitable. For some other brands, in some markets, couponing may not be profitable at all.

Unlike data on brand sales and coupon redemption rates, data on coupon profitability are difficult to obtain. Even brand managers do not have good methods for computing the overall profitability of coupon promotion programs (Neslin and Shoemaker, 1983). Further, manufacturers are reluctant to disclose profitability data. It is therefore no surprise that data on the profitability of individual coupon drops are unavailable for studying the effect of coupon characteristics (such as face value and method of distribution) on the profitability of coupon promotion. To overcome that handicap, the few researchers who have investigated the impact of coupons on brand profitability have taken one of two approaches, experimental (Klein, 1981; Chapman, 1986) or judgmental (Neslin and Shoemaker, 1983). Those using an experimental approach conduct coupon experiments comparing sales in a control cell with sales in experimental cells to arrive at incremental sales (and profitability). Such experiments indicate that coupons indeed have a substantial impact on brand sales. Researchers using managerial judgment develop analytical models to calculate coupon profitability and elicit responses from experienced executives to estimate model parameters. The managerial judgments indicate that coupons normal1 y do not accelerate purchases (similar findings have been reported by Neslin, Henderson and Quelch, 1985) and that couponing operations differ widely in profitability depending on the product markets considered.

In the tradition of previous research (Blattberg and Wisniewski, 1989; Davis, Inman and McAlister, 1992), our study addresses the short-term impact of promotions on brand per- formance. We extend past research by integrating the various subcomponents of brand sales and redemption rates to understand the impact of promotions on brand profitability. One purpose of our research is to develop a framework to aid managers in estimating the profitability of coupon promotions from available scanner data. In this paper we investigate the impact of FSI coupons that are not targeted to any particular segment of consumers but are sent through mass media. The proposed framework is unique in two ways: First, because of the lack of coupon profitability data, we use scanner data to investigate the underlying profitability of the couponing operation. Second, we integrate the sales response models and coupon redemption model to arrive at the impact of coupons on brand profitability. The second purpose of our research is to investigate the impact of the manufacturer’s optimal market-level profit-maximizing couponing strategy on the profitability of the couponing operation for each of the chains in the market.

Coupon Face Value 275

CONCEPTUAL MODEL

Overview

Coupon face value affects both the brand sales and the number of coupons redeemed. We

develop a conceptual model that integrates the effects of coupon face value on coupon

redemptions and brand sales to arrive at the profitability due to coupon promotion. The pro-

posed framework has three steps:

Step 1: Investigates the impact of coupon face value on coupon redemptions; Step 2: Investigates the impact of coupon face value on brand sales; and Step 3: Integrates coupon redemptions and brand sales to arrive at the profitability of

coupon promotion.

The overall model incorporating the three steps is shown in Figure 1

STEP I : Coumpon Redemption Model

As noted by Narasimhan (19X4), not all consumers in a market are equally price sensitive. Narasimhan’s research demonstrates that consumers who redeem coupons on at least

some of their purchases (we refer to this segment of consumers as coupon prone or “CR”)

are relatively more price sensitive than consumers who seldom redeem coupons (we refer

to this segment of consumers as coupon-indifferent or “CI”).’

2 Steu J

(Redemptive Vahe + (Ad Value) Ad Value)

1 Step + COUpOIl

Redemptions

Figure 7. Overall Model


Not all households in the CP segment will redeem coupons on every purchase occasion. The decision to redeem coupons can be modeled as a function of the costs and benefits of using coupons (for a detailed discussion of those costs and benefits, see Shimp and Kavas, 1984; Narasimhan, 1984). A fraction of sales to the CP segment will be made with coupons, depending on factors like face value of the coupon, method of distribution and number of coupons distributed. In Step 1 we investigate the proportion of sales of brand i to the CP segment that are coupon sales. Ceteris paribus, the higher the fraction of sales of brand i to the CP segment that are coupon sales, the lower the profitability (as consumers redeeming coupons generate lower contribution than consumers not redeeming coupons). We model that fraction, as a function of the coupon characteristics.

Fi = coupon sales of brand i / sales of brand i to the CP segment

Note: both the numerator and denominator of Fi depend on the face value of the coupon drop.

Relationship Between Fi and Coupon Redemption Rates: Traditionally, coupon redemption rates are measured as the fraction of dropped coupons that are redeemed. As the total number of coupons dropped and the actual number of coupons redeemed for each coupon drop are unavailable in the dataset provided, we use Fi to capture the impact of face value on coupon redemption. Coupon redemption rates typically range from 2% to 6%. In market 1, for a 60-cent coupon, the Fi value for brand A is .09 . Let us consider a market with 100 households and let 6.5 % of those households be classified as belonging to the CP segment. If a 60-cent coupon is dropped (approximately one coupon per household), the number of coupons redeemed (assuming a 3% redemption rate ) is three. An average coupon-prone household makes about two purchases during the life of the coupon and if the brand’s market share is about 30%, the number of coupons redeemed is 65*2.0*.3*.09 = 3.5 (which translates to a 3.5% redemption rate). Hence, the Fi estimates are realistic and are consistent with the redemption rates typically expected in a market.

STEP 2: Modeling Sales to the CP Segment and CI Segment

Sales to the CP Segment: As the price elasticities of consumers in the CP and CI segments are likely to be different (Narasimhan, 1984), we model sales to those segments separately. Consumers in the CI segment normally do not redeem coupons in the product category and hence coupons can only have advertisement value to that segment. However, coupons can have both redemptive value and advertisement value to consumers in the CP segment. Because coupons have different effects on the two segments, combining sales to the two segments and estimating the model at the market level could lead to biased and inconsistent estimates. Hence, we separately model weekly sales to these two segments. Sales to the CP segment is modeled as a function of price of the product and promotional variables such as the face value of coupons dropped.

Sales to the CI Segment: Unlike other types of coupons, FSIs have the advantage of clear and full-color graphics. Further, coupons enhance the value of an advertising mes- sage, and print advertisements containing coupons are more likely to be noticed and recalled than the same advertisements without coupons (Blattberg and Neslin, 1990). Bawa and Shoemaker (1989) and Ward and Davis (1978b) found that coupons cause some house-


holds not redeeming coupons to make incremental purchases. By definition, coupons cannot have redemption value to the CI segment, but can have only advertisement value. The advertisement value of the coupon drop is due mainly to the presence of multicolored FSI highlighting the product. We therefore do not anticipate the advertisement value to depend on the face value of the coupon. Instead we conceptualize the advertisement value to

depend on the presence or absence of FSIs. Hence, we model weekly sales to the CI segment as a function of the price of the product and the advertisement value due to the coupon drop.

STEP 3: Coupon Profitability

In this step we combine the results from the preceding two steps to arrive at the profitability of the couponing operation. The gross profitability of the couponing operation can be viewed as the sum of manufacturer’s profits from sales to the CI and CP segments. None of the sales to the CI segment will be made with coupons. Hence all the sales to that segment will give full contribution margin. However, a fraction of sales to the CP segment will be regular sales (without the use of coupons) and the rest will be coupon sales. The unit contribution from the coupon sales will be reduced by the face value of the coupon (and

coupon handling costs).

EMPIRICAL DEMONSTRATION

Data Used

A. C. Nielsen Company provided us three data sets for empirical validation of our pro-

posed framework. Data set 1 is a household panel data that captured details regarding the quantity bought, price paid and coupon usage. Data set 2 is a store level data set which contains details of the quantity sold and other store causal variables. Data set 3 is a coupon data base that contains details of the various coupons that were dropped in the market. In Step 1, we model the fraction of sales to the CP segment that are coupon sales as a function of the coupon characteristics. To estimate the model parameters in this step, we use household panel data and the coupon data base. We merged these two data bases to capture the details of the total number of units sold to the coupon prone segment, the number of units sold with coupons and the face value of the coupon. In Step 2, we model sales to the CP segment and CI segment as a function of price of the product and face value of the coupon. For this step, we use the segment wise weekly sales, store causal variables, and coupon drop details (face value of the coupon and number of weeks since the coupon was dropped).

To estimate the profitability model, we chose a commonly used consumer product category.2 The category has several unique features, such as absence of double-couponing activity (in the chains for which data were made available), absence of store couponing activity, and homogeneity in the method of coupon distribution and number of coupons distributed (all coupons were in the Sunday newspapers and were free-standing inserts). Sixty- five weeks of data, from January 1990 to March 1991, are used in the analysis. Two brands


(labeled A and B) account for most of the sales in both markets. The dataset has sales data

on five stores in chain 1, three stores in chain 2, two stores in chain 3, and three stores in chain 4. Chains 1 and 2 are located in market 1; chains 3 and 4 are located in market 2. The

price and store causal variables are the same across the stores in each of the four chains.

Market 1 is represented by 1492 households, of which 1088 redeemed at least one coupon

for the study product category in the 65 weeks for which we have data and are classified as

belonging to the CP segment. Market 2 is represented by 924 households, of which 616

redeemed at least one coupon in the product category and are classified as belonging to the

CP segment.

Formulation and Estimation of the Coupon Redemption Model (STEP 1)

The proportion of sales of brand i to the CP segment made with a coupon (Fi) is modeled

as a function of the coupon characteristics. We use a parsimonious model that captures the

impact of coupon characteristics on Fi. At very low face values, customers have no incen-

tive to redeem coupons and hence we expect Fi to be close to zero. That is, most of the sales

to the CP segment will be non-coupon sales. The law of variable proportions (Chambers,

1991) posits that if the quantity of one input is successively increased by equal increments

while all other inputs are fixed, the resulting product increment will first increase and then

decrease. The model therefore must reflect initially increasing marginal productivity fol-

lowed by diminishing marginal productivity when input bundles become sufficiently large.

Further, Fj must be between zero and one. The functional form that satisfies all those

requirements is the S-shaped response function. The model form proposed in equation 1

captures all of the phenomena.3

F; = Exp(yu - [Yl/viI) (1)

where:

Fi = fraction of sales to the CP segment that are coupon sales, Vi = face value of the coupon dropped by the ith brand, and

yo, y1 = parameters to be estimated from the model.

In Equation 1, the parameters y. and yI have interesting interpretations.4 The higher the

value of yr, the higher the coupon face-value sensitivity of consumers in the market. That

parameter is likely to differ across brands (and markets), depending on the coupon-value

sensitivity of the particular target market. Hence, we estimate the model given by Equation

1 separately for each of the two brands in each of the two markets. We linearize the model

by a logarithmic transformation and then estimate it with OLS. The estimation results for

markets 1 and 2 are reported in Table 1. They show that coupon face value significantly

affects Fi for both brands in both markets. In market 1, the estimate of yt is higher for brand

A than for brand B, indicating that coupon redemptions of brand A are more sensitive to


TABLE 1

Parameter Estimates of the Model for Fi (Standard Errors in Parentheses)

Market, Brand YO Yl

(Face Valuej Adjusted R2

Market 1, brand A .I 555 C.6321) 142.8039’ (29.289) .7592

Market 1, brand B -.2764 (0.7755) 121.2229’ (35.0348) .3661

Market 2, brand A .8706 (.6257) 213.3192’ (37.6656) .8614

Market 2, brand B -.5584 (0.6377) 170.32’ (34.5228) .5148

Note: l Significant at .OS level.

the coupon face value than coupon redemptions of brand B. Results for market 2 follow the same general pattern as those for market 1.

Formulation and Estimation of the Brand Sales Model (STEP 2)

Sales to the CP Segment: Ward and Davis (1978a) developed a theoretical model to explain how coupons influence consumer demand. They suggested that coupons have both redemption value and advertisement value. The redemption value represents the reduction in the purchase price to consumers who redeem the coupons. The advertisement value represents the increase in sales caused by coupons acting as a tangible reminder of the brand

name. Sales to the CP segment are likely to be affected by both the redemption value and the advertisement value of the coupons. In modeling sales to the CP segment, we want to capture the net impact of the coupon drop on brand sales. Manufacturers gain access to different consumer segments by distributing their products through different chains. Some

chains might target highly price-elastic consumers whereas others might target relatively less price-elastic consumers. As the chains in a market attract consumers with different profiles, consumer promotional activities of manufacturers are likely to differ in their impact across the chains. In the case of an FSI coupon issued by a manufacturer, though consumers patronizing different chains get coupons of the same face value, the impact of the FSI coupons on brand sales is likely to differ among the chains. The implications for manufacturers are twofold. First, to avoid stockout situations, manufacturers need to predict the impact of their coupon promotions on brand sales at different chains. Such forecasts would help manufacturers to convince the retailers to carry adequate stocks of their products. Second, such a chain level estimate will avoid the inconsistent parameters that might result from estimating the promotional response models at the market level (Hsiao, 1990; W&ink, Porter, and Gupta, 1993).

Typically, the impact of promotions is likely to decay over time (Blattberg and Neslin, 1990). The decaying impact of coupons on brand sales must be incorporated in modeling the impact of coupon drops. The sales of a brand increase in the week when a coupon is dropped and then gradually decay over the life of the coupon. Hence, in modeling the impact of coupon face value on weekly brand sales, we must take into account not only the face value of the coupon, but also time since the coupon was dropped.


To be consistent with the economic demand theory, a model representing the relation-

ship between sales quantity and prices should yield an inverse relationship between quantity sold and the price of the product. While multiplicative model forms are easier to

estimate, they imply constant price elasticity of demand at all price levels. Hence we use

the semilog model form for sales response that is commonly used in the promotions liter-

ature (Mulhern and Leone, 1991; Blattberg and Wisniewski, 1989). Promotions may gen-

erate a high increase in sales in the weeks immediately following the start of the deal, but

their impact on sales tends to decay over time (Blattberg and Neslin, 1990). The proposed

sales response model captures not only the impact of coupon face value on brand sales,

but also how the impact decays over the life of the coupon. As noted by Blattberg and

Neslin (1990), typically exponential decay patterns fit the promotional decays parsimoni-

ously. Considering the preceding factors, we propose the following sales response func-

tion.’

Qtci = EXP PO + PI

(Pj+$) I with 0 < /3* I 1

where:

Qtci = sales of brand i to the CP segment in time t, P, = price of brand i at time period t, Vi = value of the coupon for brand i that is valid at time t, T = number of weeks since the coupon was dropped, and

PO, pt, & = parameters to be estimated.

The parameter p2 captures the decaying effect of coupon face value on brand sales to the

CP segment. In the first week after the coupons are dropped, the impact of coupons of

value Vi on brand sales is equivalent to that of a price reduction of t$ pz In the rth week

after the coupon drop, the coupons’ impact on brand sales is equivalent to that of a price

reduction of l$ /3j. We estimate the parameters of equation 2 by the nonlinear least squares (NLIN proce-

dure in SAS) method. The parameter estimates of the model capturing sales to the CP

segment for both markets are reported in Table 2. For brevity, we limit our discussion to the

results for brand B, chain 1 (market I). Results for the sales response functions of brands

A and B for all chains, though statistically different, follow a similar pattern. Sales of brand

B in chain 1 are affected significantly by both the price of the product and the coupon face value. The estimated value of the parameter is .3535. It indicates that the impact on sales due to a coupon drop of one dollar face value is equivalent to the sales impact of a 35cent price reduction (to the CP segment) in the first week after coupon drop, a 12- cent (.35352)

price reduction in the second week after coupon drop, and so on. Sales to the CI Segment: By definition, coupons do not have any redemption value but

can only have advertisement value for the consumers belonging to the CI segment. As the method of distribution of coupons is homogeneous across all coupon drops, sales to the CI

segment can be modeled as a function of price of the product and a dummy indicating pres-


TABLE 2

Parameter Estimates of the Sales Response Model for the CP Segment (Standard Errors in Parentheses)

Market, Cham Brand P?

(Face Value)

Adjusted

R’

Market 1, chain 1 A

Market 1, chain 1 B

Market 1, chain 2 A

Market 1, chain 2 H

Market 2, chain 3 A

Market 2, chain 3 B

Market 2, chain 4 A

Market 2, chain 4 B

1.8098*

(0.36561

-1.8297**

(1.2706)

.5789**

C.4257)

-1.6861*

(.4 449)

-1.5508’

C.5765)

-5.9792*

(.7607)

-.60

(1.20)

(did not

converge)

PI

(Price)

713.1394'

(123.38)

2163.68’

(570.89)

894.277’

(172.30)

1805.82*

(182.03)

1318.21’

(263.11)

2800.%*

(251.62)

1332.33*

(540.32)

(did not

converge)

1 .oooo

(at bounds)

.3535*

(.18113)

1 .oooo

(at bounds)

0.5674’

(.lOOl)

.I852

t.6764)

.(,a02 *

C.1267)

.5974*

(.1940)

(did not

converge)

0.78

0.79

0.83

0.89

0.73

0.77

0.87

not dpplicable

Noles: * Signifir,3nt dT 45 Irvel.

** Signlflrant at ,111 IPVCI.

ence or absence of couponing activities. The log reciprocal model proposed to capture sales

to the CI segment is given by:

Qrni = ExP f+l

Ps + F + BS * Dit with 0 < p7 < 1 If

(3)

where:

Q,,, = sales of brand i to the CI segment at time t, Pit = price of brand i at time period t, D, = dummy variable to indicate the presence/absence of a valid coupon for brand

i in time t z = number of weeks since the coupon was dropped, and

&, p6, p7 = parameters to be estimated.

The parameter captures the decaying advertisement effect of coupons on brand sales to the

CI segment. We estimate Equation 3 by the nonlinear least squares (NLIN procedure in SAS) method.

The parameter estimates of the model capturing sales to the CI segment for both markets

are reported in Table 3. As expected, there is considerable heterogeneity in the magnitude of the parameters across the different brand-chain combinations. For brevity, we limit our discussion to the results for brands A and B in chain 1. Results for the sales response func-


TABLE 3

Parameter Estimates of the Sales Response Model for the Cl Segment

(Standard Errors in Parentheses)

Market, Chain Brand

Market 1, chain 1 A

Market 1, chain 1 B

Market 1, chain 2 A

Market 1, chain 2 B

Market 2, chain 3 A

Market 2, chain 3 B

Market 2, chain 4 A

Market 2, chain 4 6

Notes: * Significant at .05 level.

*’ Significant at .I0 level

P5

-.9323”

C.5755) -3.7642*

(1.5178) -9.3789’

(1.19935) -3.4648’

C.4187) -2.9045’

C.6999)

-8.1879*

C.7055) -.4082

(1.4013)

(did not converge)

P6

(Price)

1131.18’

(201.6364) 1977.2775’

(674.8387) 4278.9487’

(433.5308) 1574.4599*

(170.2763) 1407.9548*

(316.3943)

2880.8119’: (249.9099) 688.2187

(645.1849)

(did not converge)

P7

(Dummy Variable)

s737’ C.1594) .1387

C.2102)

.1425

C.3958) .2642*’

C.1662) .oooo

(at bounds)

.8065* C.1283) .oooo

(at bounds) (did not

converge)

Adjusted R2

0.74

0.69

0.73

0.83

0.60

0.78

0.82

not applicable

tions of brands A and B for the other chains, though statistically different, follow a similar

pattern. Sales of brand A to the CI segment in chain 1 are affected significantly by both the

price of the product and the advertisement effect of the coupon drop (as indicated by a significant &). For brand B, the price of the brand has a significant impact on sales but cou-

pons do not have a significant advertisement value.

Coupon Profitability Model (Step 3)

In Step 3, we integrate the segment-level sales estimated in Step 2 and estimated in

Step 1. Consider a market with J chains and let a coupon of value Vi be dropped for brand

i with a coupon life of T weeks. Let Qtcti be the sales to the CP segment of brand i in chain

j in week t. Let Qtnti be the sales to the CI segment of brand i in chain j in week t. Then the total sales of brand i across all the J chains in the T weeks is given by

T J T J

C C Qtci,j + C C Q,n;j f=lj=l t=l j=l

Though none of the sales to the CI segment would be coupon sales, a fraction (Fi) of the

sales to the CP segment would be coupon sales. Let Mi be the contribution, in cents per unit, of brand i. Every unit sold to the CI segment will generate the full contribution margin Mi.


Of the total sales to the CP segment, the fraction (1 - Fi) will not be coupon sales and will

give the full contribution margin Mi; the fraction Fi will be coupon sales and its contribution will be (M; - Vi - G ), where Vi is the face value of the coupon and G is the cost of

handling coupons. Let H be the fixed cost of the couponing operation. Then the net profit from the sales of the brand is given by?

T J T J

xi= C CQ,nij*Mi+ C CQ,cij*(l-F;)*Mi f=lj=l t=l j=l

T J (5)

+ G C Q,,.;j * Fi * (Mi-Vi-G)-H t=l j=l

Equation 5 can be evaluated at different face values to investigate the sensitivity of the prof-

itability to the coupon face value. Profitability of Brand A in Market 1: The profitability of the couponing operation in a

market depends on the sales response to coupon promotions across the chains in the market. The average price of brand A in market 1 is $4.04 per unit in chain 1 and $4.58 per unit in

chain 2. If a coupon of face value Vi is dropped in a market, the total profitability of the brand across both chains in the market can be calculated by substituting the appropriate parameter estimates in Equation 5. Sensitivity of profitability of the coupon promotion to the face value of the coupon can be found by numerically evaluating equation 5 for different face values. Similarly, the total market-level sales at different face values can be found by substituting the appropriate face values in Equation 4.

The results obtained by using different face values’ in Equations 4 and 5 are reported in

Table 4. As the face value of the coupon increases, we do not expect the advertisement value to the CI segment to increase. However, because the redemption value to the CP segment will increase we expect the total sales of the brand to increase (or, in the most conser-

vative scenario, to remain the same) with the face value of the coupon. As expected, sales of the brand increase with face value. Unlike sales of the brand, the profitability of the couponing operation need not increase monotonically with the face value of the coupon. The gross profitability of the couponing operation depends on the tradeoff between the incremental sales generated by the coupon drop and the coupon redemptions. In this market, brand A achieves the maximum profit when a 75-cents coupon is dropped. The difference

between the profit at 75cent face value and the profit in the no-coupon condition (given by zero cents coupon value) gives the gross profit due to the couponing operation. If the fixed costs of me couponing operation are below that difference, coupon promotion is profitable in this market.

A brand manager may consider not only the profitability of the couponing operation, but also several other factors such as volume moved, market share, and so on. Table 4

indicates both the incremental profit and the incremental sales that can be expected from the couponing operation. The sales volume increases from 445.63 units when no coupons are dropped to 753.86 units when a one dollar coupon is dropped. The predicted quantity moved at the optimal face value of 75 cents is 644.42 units. Depending on the short-term objective, the brand manager may decide to drop a one-dollar coupon to help move


TABLE 4

Coupon

Value

(Cents)

0

35

40

45

50

55

60

65

70

75

a0

a5

90

95 100

Profit/Sales at Different Coupon Face Values for Brand A

Market I

(Chain 1 + Chain Z) Chain 1 Chain 2 ~____

Total Profit Total Sale> Total Profit Total Sales Total Profit Total Sales

(Dollars) (Units) (Lkdlars) (Um ts) (Dolhrs) IUnits)

371.77 445.63 272.58 337.34 99.19 108.29

435.13 525.80 320.64 399.87 114.49 125.93

442.23 5 3 7 .9 1 325.75 408.95 116.48 i 28.95 448.55 550.68 330.28 418.54 118.27 132.13

4.53.99 564.16 334.16 428.68 119.84 I 35.48

458.49 578.41 337.33 439.41 121.16 139.00 462.01 593.49 339.78 450.78 122.23 142.71

464.52 609.47 341.48 462.84 123.04 146.63

465.99 626.42 342.41 475.66 123.59 150.76

466.42 644.42 342.55 489.29 123.87 155.13

465.77 663.57 341.89 503.82 123.89 159.75

464.01 683.97 340.39 519.32 123.62 164.65

461.10 705.73 338.03 535.88 123.07 169.85

456.98 728.98 334.75 55'1.61 122.23 175.37 451.59 753.86 330.51 572.62 121.08 181.24

higher tonnage. Alternatively, the manager might decide to drop a coupon of some other

face value, with a compromise on the optimal profit but an increase in the sales volume

or market share. Profitability of Brand B in Market 1: The average price of brand B is $4.65 per unit in

chain 1 and $4.85 per unit in chain 2. The profitability and sales of brand B in market 1

(when the profit/sales in the two chains are summed) are given in Table 5. Sales of the

brand increase with coupon face value, but the profitability of the couponing operation does

not. The gross profitability of the brand is marginally higher at a coupon face value of 40

cents than in the no-coupon condition. If a coupon of face value greater than 50 cents is

dropped, the gross profitability of the brand falls below that in the no-coupon condition,

even though the brand sales continue to increase. The brand manager could choose the par-

ticular face value that gives a satisfactory combination of sales and profitability to achieve

short-term objectives. Chain-level Analysis (Market 1, Brand A): A brand manager who makes the couponing

decision at the market level might be interested in knowing how individual chains in the market will contribute to the profitability of the brand. Because the impact of the coupon

drop on brand sales will differ among chains, investigating the sales impact for each chain

is important. Knowing the expected sales in the various chains would give the sales force

a strong selling point in pushing the brand in those chains. The average price of brand A in chain 1 and chain 2 is $4.04 and $4.58, respectively. If

a coupon of face value V is dropped in the market, the sales and profitability of the brand

(in chain 1 and chain 2) can be obtained by substituting the parameter values in Equations

4 and 5, respectively. We calculate the sales and profitability of brand A in both chains


TABLE 5

Profit/Sales at Different Coupon Face Values for Brand B in Market 1

Coupon Value Total Profit Jotal Sales

(Cents) (Dollars) (Units)

0 204.19 216.97

40 206.75 224.67

45 205.09 225.69

50 202.83 226.73

55 199.99 227.79

60 196.64 228.88

65 192.78 229.99

70 188.46 231.14

75 183.71 232.30

80 178.55 233.5

85 173.03 234.73

90 167.15 235.99

95 160.94 237.28

100 154.42 238.6

numerically. The results are given in Table 4. The profitability of brand A reaches a maxi-

mum at a 75cent coupon value in chain I and at an SO-cent coupon value in chain 2. Inter-

estingly the optimal face value of the coupon to be dropped at the market level is 75 cents

(see Table 4). Hence, the optimal face value of the coupon to be dropped at the market level

need not be optimal for the individual chains in the market. As the number of chains in a market attracting consumers of different price sensitivities increase, this disparity between

the optimal market level face value and the optimal face value for the different chains is

likely to increase. While the shelf price at the store level is determined by the various chains

(local decisions), the face value of the FSI coupon is determined by the manufacturer (glo-

bal decision). As Ingene and Parry (1995) illustrate, the optima1 global decision need not

optimize profits from each of the various chains. impact of the Coupon Face-value Sensitivity Parameter (~1) on Optimal Face

Value: The fraction of salesto the CP segment that are coupon sales is modeled by Equa- tion 1. That fraction (Fi) depends on the face value of the coupon and the parameter yl, the

coupon face-value sensitivity parameter. That parameter varies across markets, depending

on the demographic and psychographic profiles of the target consumers in the markets. The profitability of the couponing operation and the optima1 face value of coupons to be

dropped in a market depend on the coupon face-value sensitivity parameter. Even in a sin-

gle market, that parameter value might differ over time in response to changes in the eco-

nomic climate. For a given face value. the higher the y1 the lower the fraction of the sales

to the CP segment that are coupon sales. Hence, as y1 increases, manufacturers can afford

to drop coupons of higher face value (which will increase sales to the CP segment) to optimize their profit. For market 1, brand A, the impact of y1 on the optimal face value of the coupon and the maximum brand profitability is given in Table 6. Both the profitability of the couponing operation and the optimal face value of coupons to be dropped increase with the increase in y, .


TABLE 6

Optimal Face Value and Maximum Profit at Different Values of Coupon Face-value Sensitivity for Brand A in Market 1

Optimal V Maximum Profit

Yl fCent5) (Dollars)

80 35 418.47 90 40 424.84

100 50 431.64 110 55 438.97

120 60 446.73

130 65 454.93 140 75 463.66 150 80 473.25 160 85 483.44

170 95 494.49 180 100 506.52

CONCLUSIONS

This research makes several contributions to our understanding of the impact of coupon promotions on brand sales and profitability. Traditionally, researchers investigating the impact of coupon promotions study the effect of coupon characteristics on redemption rates, market share, and sales. The few studies that have investigated the profitability of a

couponing operation have been based on either experimentation or the use of managerial judgments. To our knowledge, ours is the first study to use scanner data to integrate coupon redemption and the sales impact of coupon drops to arrive at brand profitability. Our findings show that the optimal couponing decision at the market level need not be optimal for the individual chains in the market.

To estimate our proposed model we use currently available secondary data. In this paper we do not investigate the impact of coupons on trial or repeat purchase. Also we do not incorporate the impact of competing retailers’ pricing and promotional strategies on brand sales. We assume that choice of the couponed brand does not affect consumers’ subsequent choices. That is, we do not explicitly incorporate the long-term impact of coupons on brand preferences in the model. That assumption is not a major limitation because currently there

is no consensus among scholars as to the direction of such a change in preference. More- over, coupon promotions on almost all brands are so frequent that, should long-range preference changes occur, the effects may offset one another (McAlister, 1986).

Several interesting extensions of our research are possible and would give us a better understanding of the effects of coupon promotions on brand sales and profitability. As display and deep promotional price cuts were absent during the time period for which the dataset was made available, we did not study the interaction of coupons with those variables. A dataset with variation in display and deep promotional price cuts could be used to investigate the interaction of coupon drops with those promotional variables. Further, not all consumers belonging to the CI segment need be homogeneous in their response to FSI


coupons. It is appealing to conceptualize the consumers in the CI segment as composed of: (1) a truly CI segment (consumers who do not even scan the FSI sections as their opportu- nity cost of the time and effort is very high) and (2) a coupon-scanning segment (consumers who might scan FSIs for informational value or for coupons in some other product category or just out of curiosity). FSIs cannot have any advertising value to consumers belonging to the truly CI segment, but can have an advertisement value to consumers belonging to the coupon-scanning segment.8 As data on the coupon-scanning behavior of consumers in

those two segments are unavailable, we model the total sales to the CI segment (consisting of both the truly CI segment and coupon scanning segment) as a function of price and other causal variables.

The data used in our study do not capture sales to the CP segment and CI segment separately at the store level. Hence, we use the household-level purchase details to arrive at the weekly fraction of sales to the CP segment and then use that ratio to arrive at the chain-level weekly sales to the CP segment. To the extent the panel data are not representative of the product market, the estimates of the sales response model would be biased.

We define the CP segment as households that had redeemed at least one coupon in the

65 weeks for which the data were available. Instead of classifying households as belonging to either the CP segment or the CI segment, researchers could classify households accord- ing to coupon scanning behavior and test whether the advertisement value of the coupons differs across the segments. Also, our model does not consider stockpiling. However, unlike short-term price reductions, coupon drops may not result in stockpiling (Neslin, Henderson, and Quelch, 1985). Hence, the omission of stockpiling is not a major limitation.

With a richer data base, the proposed model could be extended to include the effects of coupon frequency, coupon life, and double couponing activities on brand sales. Our proposed model is estimated for each of four individual chains. If the number of chains in a market is large, instead of estimating the parameters for each of the chains we can use a richer time-series and cross-sectional modeling approach.

The model framework could be extended to analyze the impact of store coupons on brand sales, and the individual chains could use the framework to optimize both the coupon face value and shelf price of the product. Also, as our research suggests that FSI coupon drops might have an advertisement value, further research should be done to investigate whether the advertisement effect holds for other types of coupons (such as difect mail).

Acknowledgment: The authors gratefully acknowledge the help of A.C. Nielsen in providing us with the data sets needed for this research.

NOTES

1. Not all consumers in the coupon-prone (CP) segment are conceptualized to have the same amount of coupon proneness. Rather, consumers belonging to the same segment are relatively more homogeneous than consumers belonging to different segments.

2. The product category, brand names, chain names, and markets used in the research are not identified because of a confidentiality agreement with the data supplier.

288 Journal of Retailing Vol. 72, No. 3 ‘I996

3. Without loss of generality, equation I can be expanded to inco~orate other coupon character-

istics such as number of coupons dropped and method of ~st~bution. As there is no variation in the

dataset on those dimensions, we use the simpler model. 4. The theoretical lower bound and upper bound of Fi are 0 and Exp(y&respectively. If y. is set

equal to zero, the upper bound of Fi will be unity. We do not set y,-, to zero, as typically even for cou-

pons with relatively higher face values, the fraction of sales made with coupons tends to be much

smaller than one. 5. Equations 2 and 3 can be expanded to incorporate other causal variables such as display and

feature activities. As there were no display and featuring activities during the time period for which

the dataset was made available (for the brands in the chains under considerations, we use the simpler model fan. Using a conservative strategy, we classify a household as belonging to the CP segment

if it used coupons {in a given product category) at least once in a given time period or as belonging

to the CI segment if it did not use coupons (in a given product category) even once in a given time

period. Households in the CI segment normally do not redeem coupons in the given product cate-

gory. To arrive at the sales to the CP segment, total weekly sales at the chain level are multiplied by

the fraction of sales to the CP segment derived from the household-level data. 6. We thank an anonymous reviewer for pointing out that to the extent coupons result in stock-

piling, profitability calculated from the equation will be overstated. 7. Ourusoff and Panchapakesan (1993) report the margins for various consumer products. For

the product category/brands in our study, the contribution margins are around 20%. 8. We thank an anonymous reviewer for pointing out that the CI segment can further be viewed

as consisting of a truly CI segment and a coupon scanning segment.

REFERENCES

Bawa, Kapil and Robert W. Shoemaker. (1987). “The Coupon Prone Consumer: Some Findings

Based on Purchase Behavior Across Product Classes,” Journal of Marketing, %(October): 99-

110. Bawa, Kapil and Robert W. Shoemaker (1989), “Analyzing Incremental Sales From a Direct Mail

Coupon Promotion,” Journal of Marketing, 53 (July), 66-78. Blattberg, Robert C. and Scott A. Neslin. (1990). Sales Promotion Concepts, iMethods and

Straregies. Englewood Cliffs, NJ: Prentice-Hall, Inc. Blattberg, Robert C. and Kenneth J. Wisniewski. (1989). “Puce-Induced Patterns of Competition,‘~

~~~~~~~~g Science, 8(Fall): 291-309. Chambers, Robert G. ( 199 1). Applied Production Analysts. Cambridge, UK: Cambridge University

Press. Chapman, Randall G. (1986). “Assessing the datability of Retailer Couponing with a Low-Cost

Field Experiment,” Journal ofRetailing, 62(Spring): 19-39. Davis, Scott J., Jeffrey Inman and Leigh McAlister. (1992). “Promotion Has a Negative Effect on

Brand Evaluations-Or Does It? Additional Disconfirming Evidence,” Journal of Marketing Research, 29(February): 143-148.

Donnelley Marketing Inc. (1995). The 17th Annual Survey of Promotional Practices. Chicago:

Author. Hsiao, Cheng, (1990). Analysis of Panel Data. Cambridge; UK: Cambridge University Press. Ingene, Charles A. and Mark E. Perry. (1995). “A Note on Multi-regions M~keti~g~ ~unugement

Science, 41(July): 1194- 120 1.


Inman, Jeffrey J. and Leigh McAlister. (1994). “Do Coupon Expiration Dates Affect Consumer Behavior?’ Joumul of Marketing Research, 31(August): 423-428.

Klein, Robert L. (1981). “Using Supermarket Scanner Panels to Measure the Effectiveness of Coupon Promotions.” Pp. 118-124 in John W. Keon (ed.), Marketing: Measurement and Analysis, 1981, Proceedings-3rd ORSALUMS Special Interest Conference on Market Measurement and

Analysis. McAlister, Leigh. (1986). A Research Report Preparedfor the Marketing Science Institute and Other

Sponsors. Cambridge, MA: Marketing Science Institute. Mulhern, Francis J. and Robert P. Leone. (1991). “Implicit Price Bundling of Retail Products: A

Multiproduct Approach to Maximizing Store Profitability,” Journal of Marketing, %(October): 63-76.

Narasimhan, Chakaravarthi. (1984). “A Price Discrimination Theory of Coupons,” Marketing Science, 3(Spring): 128-147.

Neslin, Scott A. (1990). “A Market Response Model for Coupon Promotions,” Marketing Science, 9(Spring): 125145.

Neslin, Scott A. and Darral G. Clarke. (1987). “Relating the Brand Use Profile of Coupon Redeemers to Brand and Coupon Characteristics,” Journal ofAdvertising Research, 27(February_March): 23- 32.

Neslin, Scott A., Caroline Henderson and John Quelch. (1985). “Consumer Promotions and the Acceleration of Product Purchases,” Marketing Science, 4(Spring): 147-165.

Neslin, Scott A. and Robert W. Shoemaker. (1983). “A Model for Evaluating the Profitability of Coupon Promotions,” Marketing Science, 2(Fall): 361-380.

Nielsen Clearing House. (1993). Worldwide Coupon Distribution and Redemption Trends. Chicago: A.C. Nielsen.

Oumsoff, Alexandra and Meenakshi Panchapakesan. (1993). “Who Said Brands Are Dead?’ Brand

Week, 34(August): 20-28. Reibstein, David J. and Phillis A. Traver. (1982). “Factors Affecting Coupon Redemption Rates,”

Journal ofMarketing, 46(Fall): 102-l 13. Shimp, Terence A. and Alican Kavas. (1984). “The Theory of Reasoned Action Applied to Coupon

Usage,” Journal of Consumer Research, ll(December): 795-809. Srinivasan, Srini S., Robert P. Leone and Francis J. Mulhern. (1995). “The Advertising Exposure

Effect of Free Standing Inserts,” Journal of Advertising, 24(Spring): 29-40. Tee], Jesse E., Robert H. Williams and William 0. Bearden. (1980). “Correlates of Consumer

Susceptibility to Coupons in New Grocer Product Introductions,” Journul of Advertising, 9(3): 3 l- 46.

Ward, Richard W. and James E. Davis. (1978a). “A Pooled Cross-Section Time Series Model of Coupon Promotions,” American Journal of Agricultural Economics, BO(August): 393-401.

-’ (1978b). “Coupon Redemptions,” Journal of Advertising Research, lII(August): 5 l-58 Wittink, Dick R., John C. Porter and Sachin Gupta. (1993). Dangers in Using Market-Level Datafor

Determining Promotion Effects. Marketing Science Institute Report No. 115, September.