Eppen, G. D., & Lieberman, J. (1981). a Theoretical and Empirical Evaluation of Price Deals for...

A Theoretical and Empirical Evaluation of Price Deals for Consumer NondurablesAuthor(s): Robert C. Blattberg, Gary D. Eppen and Joshua LiebermanSource: Journal of Marketing, Vol. 45, No. 1 (Winter, 1981), pp. 116-129Published by: American Marketing AssociationStable URL: http://www.jstor.org/stable/1251725 .

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ROBERT C. BLATTBERG, GARY D. EPPEN,& JOSHUA LIEBERMAN

Food retailers regularly offer products for less than normal market price in special sales or deals. This paper briefly examines several common explanations for this phenomenon and finds the analyses to be less than complete. It then

presents an explanation for dealing of storable products based on the idea of transfering inventory carrying costs from the retailer to the consumer. An inventory control model is described in which both consumers and the retailer act so as to minimize their own costs. Results derived from this model are then presented. Data relevant to both the consumer and the retailer model are presented and analyzed. The conclusion is that the data are consistent with the predictions of the models. Finally, the strategic implications of the model for manufacturers and retailers are discussed.

I I I I I I I I I I I II IIIIIIIIIIIIIIIIIIIIIIIIII

A THEORETICAL AND

EMPIRICAL EVALUATION OF

PRICE DEALS FOR CONSUMER NONDURABLES

VER the last half century, retailers, on a regular basis, have offered consumers periodic short

term price cuts called deals.' Deals are one of the major forms of price competition used by retailers.

'Dealing will be defined as a short-term, usually a week or shorter, price cut to the consumer. After the deal is over, the price reverts back to its old level.

Robert C. Blattberg is Charles H. Kellstadt Professor of Marketing, and Gary D. Eppen is Associate Dean for Ph.D. Studies, both at the Graduate School of Business of the University of Chicago. Joshua Lieberman is Lec- turer in Economics and Marketing, Bar-llan University, Ramat-Gan, Israel. The research for this paper has been funded in part by National Science Foundation Grant SOC73-05547.

IIIIIIII

III I IIIII IIIIIIIIIIIIIIIII IIIIIIIII IIIIIII IIIIIIIIII IIIIIIIII~~~~~

A natural question is, "Why do retailers prefer to offer substantial price reductions for a short period of time, and then raise the price to its normal level rather than permanently reduce price by less than the deal price?"

There are a number of alternative explanations though almost no published research exists on this topic. The most common explanation is that retailers deal to attract customers from other stores. We note that any given retailer would gain customers if he/she deals and his/her competitors do not retaliate also by dealing. However, it is very likely that other retailers also will respond by offering deals. A possible result is that all the retailers in the market offer deals but none of the retailers have increased their profits. Unless there were other

Journal of Marketing Vol. 45 (Winter 1981), 116-129. 116 / Journal of Marketing, Winter 1981



economic benefits to dealing, it would be unprofit- able to the retailer.

The following analogy illustrates the point. Three gasoline dealers on the same corner engage in a price war. Dealer one reduces prices and gains customers. Quickly his/her competitors respond by reducing prices and their market shares return to their previous levels. The result is that the reduced prices do not increase volume enough so that total profits for the three stations are reduced below the pre-price war levels. It is almost always the case that after some relatively short period, the stations raise the price to pre-price war levels.

Food retailers price numerous items, and therefore, their environment is more complex than for a single-price retailer such as a gas dealer. The question remains, however, "If retailers can imitate one another, why do they continue to offer deals?" Eventually there should be an incentive to eliminate deals and simply charge lower prices. Other forms of promotion such as trading stamps, games and give-aways last a relatively short period of time,2 yet dealing has persisted for 50 years. Another explanation for the persistence of dealing seems to be required.

Many researchers and store managers believe that retailers deal to attract customers. The previous discussion shows that dealing may lead to a zero-sum game. It is, of course, possible that retailers reach a non-optimal equilibrium in which each retailer cannot stop dealing because each retailer is worse off if he/she stops dealing and no one else does. Cases such as gas wars imply that these nonoptimal equilibria do not occur often. However, this propo- sition is very difficult to test with existing data sources. This article will propose an alternative explanation that future research may be able to contrast to the store traffic argument.

An alternative explanation of the cause of dealing is that manufacturers offer trade deals which require price reductions by the retailers. The manufacturers offer trade deals for two reasons: to increase market share, and to get nontriers of their products to learn about their form of attributes at a lower risk level (reduced price).

The first explanation is similar to the one just discussed for the retailer. Suppose the industry

2An example of retail promotions that are started and halted are games that retailers frequently offer to customers. These promotions initially attract customers to the first retailer who introduces the game. Quickly, other retailers adopt similar games and the profits due to the game for the first retailer are driven to zero. Soon all retailers drop the games and the market returns to its initial position. Games do not persist because they do not offer a sustained economic benefit to the retailer.

cannot increase category volume by dealing. Then if one firm offers a deal and its competitors follow, the overall price is reduced and profits are reduced. Thus, only if price reductions increase category volume enough to increase category profits, will dealing be economically viable. In most product categories it is unlikely that this will happen. There- fore, manufacturers will eventually stop offering trade deals. However, this has not happened.

The second explanation, offering consumers a reduced price to try their brand, will be analyzed in more detail in later sections of the paper discuss- ing model implications and empirical results. It will be shown to be inconsistent with the empirical results.

The explanation studied in this paper is that dealing occurs because retailers have higher inventory holding costs than some consumers. The retailer is motivated to take a reduction in sales revenue if the consumer will hold some of the inventory. The consumer is willing to carry some inventory in return for a reduction in price. A deal is the condition needed to complete this exchange, since consumers cannot be persuaded to hold inventory by a constant price.

The inventory holding costs under consideration have two main components: the value of the capital tied up in inventory and the value of the space committed to holding the inventory. It seems clear that the second term is much greater for the retailer. Shelf space is a major concern for food retailers. Products and suppliers vie vigorously for shelf space. On the other hand, for a number of consumers the cost of some additional storage space is ex- tremely low. Another dozen boxes of tissue in the bathroom closet or an additional case of pickles in the fruit cellar is of almost no concern. These costs alone seem sufficient to conclude that the opportunity for a mutually profitable exchange exists.

The idea that consumers buy and hold inventory when deals occur has appeared elsewhere in the literature. Frank and Massy (1967) used distributed lag models to measure the effect of past purchasing on a brand's sales and market share. Kunreuther (1973) and Blattberg et al. (1978) developed models of the household in which they traded off holding costs, transaction costs, and deal discounts to decide the quantity to buy each period. However, none of these papers tested an explicit inventory model. In addition, the retailer, i.e., the supply side of the model, was not considered. The purpose of this paper is to extend the holding cost theory to include the interaction between the consumer (demand) and retailer (supply), and thus be able to

A Theoretical and Empirical Evaluation of Price Deals for Consumer Nondurables / 117



determine the magnitude and frequency of deals and the quantity sold on deal.

This paper is in the spirit of economic models in which the consumer and the firm (retailer) maximize their own objective function. This model is used to understand how deals work and to derive testable implications. Data are then used to test these implications.

Understanding and explaining dealing is complex. Economic reasoning was used to raise ques- tions about several of the explanations of dealing commonly given. More detailed empirical tests are needed before any of these explanations can be accepted or rejected. In all likelihood, different explanations exist for the existence of dealing in different circumstances. The purpose of this paper is to offer an explanation as to why retailers deal in certain cases. It is shown that this explanation is consistent with consumer purchase data.

Section 2 of this article describes the model's structure, while Section 3 gives the testable implications. Section 4 tests these implications, and Section 5 gives a summary and our conclusions.

The Model's Structure This section of the paper will outline two models, one for the consumer and one for the retailer. Each optimizes his/her own welfare in response to an action by the other. The equilibrium result is a dealing policy for the retailer. The retailer's model has implications regarding the quantity sold on deal and the frequency and magnitude of deals; the consumer model has implications regarding the quantity bought on deal and the time between purchases.

Before outlining the model, it is useful to list certain assumptions that will be used throughout this section. (Other assumptions that will be made are described in the appropriate section of the paper.) The first two are not restrictive but simplify the mathematics: time is a continuous variable, and all quantities sold and purchased are perfectly divisible.3 The next four assumptions are more restrictive but are made to make the mathematics more tractable, so that a closed-form solution can be found for the optimal dealing price. These assumptions are: there is a single store in the market; deals are offered on a single brand only; the regular (nondeal) price of the deal brand, P, is fixed; and no trade allowances are offered to the retailer.

These assumptions do not perfectly mirror the

3Perfectly divisible means any amount of the product can be purchased.

real world. The first two assumptions are perhaps the worst offenders in this respect. However, they permit the construction of a relatively simple model that captures the essence of the inventory holding argument, yields analytically tractable results, and leads to conclusions that are empirically confirmed.

The assumption that the regular (nondeal) price is fixed, is not very restrictive because the relative prices in a category do not change substantially over short periods of time. The consumer's and store's behavior would not change dramatically if normal price fluctuations were included in the model.

The assumption of no trade allowances is consistent with the purpose of this paper. The goal is to demonstrate both with models and data analysis that deals by retailers are a reasonable economic activity with no additional outside motivation. It is not necessary or useful to include manufacturers in the model to establish this point. Other environ- ments involving the three-way interaction between manufacturers, retailers, and consumers might also provide motivation for retailer dealing. This research does not claim to exclude all other reasons for dealing, but simply to establish the inventory explanation as a viable alternative. Other explanations and the associated research are left to other papers at another time.

The rest of this section includes an overview of the model, the consumer model, the retailer model, and testable implications.

An Overview of the Model The model begins with the consumer. The consumer is assumed to respond to deals by "forward" buying additional items and holding inventory. The consumer decides his/her inventory level by trading off holding costs against the reduced price of the item. (See Blattberg et al. 1978 or Kunreuther 1973 for a discussion of this assumption.)

The model assumes that not all consumers have the same holding costs. For simplicity, there are two types of consumers: low-holding-cost consumers and high-holding-cost consumers.4 Aggrega- tion is done by weighting the two groups in the market by their relative size.

The retailer's objective is to maximize his/her profits. His/her main trade-off is that by dealing he / she can reduce his/her inventory but lose reve-

4Splitting the market into two groups rather than having more segments or a distribution across the population to represent holding costs should have no effect on the results. The degree of response to deals will vary by segment but the general behavior should be similar.

118 / Journal of Marketing, Winter 1981



per unit of time, ci, and has a fixed holding cost per unit of time, hi, for the product category. (Note that hi is the cost of holding one unit (e.g., a box or a can) of the product for one period of time. Thus, the holding cost for the same product (alu- minun foil) will vary with the size of the box purchased.) In responding to deals the consumer never makes a special trip to the store, thus buying deal items during his/her regular trips, and responds to deals by changing purchase timing but not consumption of the product category.5

Based on these assumptions, the following model has been developed. In buying ahead on deal, the individual consumer, i, incurs a holding cost, hi, on the quantity, qi, which he/she stockpiles on deal but also gains a price reduction, D, for each unit bought on deal. The consumer wishes to minimize the total cost function, TC(qi), when he/she buys on deal subject to the constraint that he/she consumes ci units per period and all consumption demand is satisfied. This leads to:

qTC(i/c

TC(qi) = o

hi(qi - it)dt - Dqi

hiq2 - Dqi.

2c1 (1)

The time period of analysis is qi/ci which is the time required to consume qi units. Equation (1) indicates that the cost of buying qi units for the household is proportional to the holding costs (hi) and the square of the quantity bought (qi). It is inversely proportional to the consumption rate (ci). As the deal (D) increases, costs also decrease.

From equation (1) one can determine the optimal quantity (qi) to buy on deal. This yields

nue from the reduced deal price. The retailer con- trols the quantity bought on deal since the amount consumers will buy depends on the size of the deal. Figure 1 gives an overview of the model and shows that the retailer fixes his /her deal price as a function of the consumer's response to deals and his/her own cost of holding inventories.

Consumer Model The consumer's objective function is to minimize his/her total costs subject to the constraint that he / she satisfy his/her demand at any point in time. It will be assumed that each consumer conducts regular shopping trips to the store, and on each trip he/she buys a bundle of items which may or may not include the deal brand. Each consumer, i, consumes the product category at a fixed rate

Dci q*i = (2)

and the optimal purchase period is

D t* =- (3)

Equation (2) shows that the optimal quantity to

5For products such as soft drinks and non-staple items, it is likely that dealing will increase the consumption rate. Therefore, c; becomes a function of price, c (p). The household will still forward buy, but the quantity bought will depend upon the effect of the deal on consumption as well as on the holding costs. The general results should not change, though it would be difficult to separate the two effects. The products studied in the empirical section are products whose consumption rates will not be greatly affected by dealing.


FIGURE 1 Overview of Model



be bought increases as the deal magnitude (D) and consumption rate (ci) increase, and it decreases as holding costs (hi) increase. Equation (3) shows that the optimal purchase period increases as the deal magnitude (D) increases. The optimal purchase period decreases as the holding cost (hi) increases.

To aggregate across consumers, it is assumed that all consumers have the same consumption rate, c.6 Regarding holding costs, there are two segments in the market, one with a low per-unit holding cost, hL, and the other with a high per-unit holding cost, hH. Let there be N consumers in the market. a is a number lying between 0 and 1 so that there are aN consumers with low holding costs and (1 - a)N consumers with high holding costs. It will be assumed that only consumers with low holding costs buy ahead on deal. The consumers with high holding costs just buy enough to meet their consumption per period.

From these assumptions, the aggregate quantity bought on deal is

D QD= (aN)q* = Nc (4)

hL

Note that in (4) the term Nc is the accumulated rate of demand. Thus, if a deal occurs and another one is not offered for t* or more weeks, the total

D amount bought on sale is aQ - or axQt*, where

hL Q is the cumulative rate of demand. In this deriva- tion, it is assumed that the consumers with high holding costs do not buy any items on deal. In developing the model, it is assumed that the sale is instantaneous; i.e., that all items sold on deal are removed from the inventory instantaneously. It would be a simple matter to include some of the demand of high holding cost consumers in the quantity bought. However, it would add nothing new to the model except for some additional nota- tion, and thus, this option was rejected. By the same token, other patterns for the demand of low holding cost during the deal could be assumed. These assumptions produce a model that is similar to the EOQ model, and thus, seemed worth exploring.

One generalization, however, is included in the model. At least in the early parts of the development, it is assumed that a is a function of D and a(D) is introduced as a nondecreasing function of D that

6This assumption avoids integrating TC(q,) with respect to c which would greatly complicate the mathematics. The results would change, but the direction of the model's implications should not because each household will still forward buy. Only the quantity bought per household will vary.

represents the proportion of consumers who buy on deal.

The Retailer Model

To model the retailer, certain assumptions will also be made. The unit cost to the retailer, m, is fixed. He/she incurs a fixed set-up cost per order, K, and a fixed holding cost per unit, hR. The retailer must satisfy all demand. Deliveries and sales are instantaneous. There is only one deal per inventory cycle, and deals are offered with a fixed time interval between them, k. Lastly, the same offer is made on each deal.

The purpose of dealing for the retailer is to be able to shift inventories to consumers, hence reducing his/her holding costs. The cost to the retailer is reduced profits per unit sold because the deal reduces his/her revenue on each unit sold at the deal price. The retailer will choose an inventory and dealing policy to maximize profits.

The optimal inventory policy will not be derived in this paper. Interested readers are referred to Lieberman (1978) and Eppen and Lieberman (1980). The main results with enough discussion to make them understandable follow.

It is shown that deals are offered at the beginning of the inventory cycle and that if hR > 2hL, the optimal value of the discount, D*, is

D* = khL

where k is the length of the inventory cycle. The retailer's cost as a function of the inventory cycle is

hRQ 22] TC(k) = K + Da(D)kQ + [k - o (D)k2] 2 (5)

It is interesting to note that if ac(D) -> 0 as D -- 0, then TC(k) approaches the standard EOQ model as D -- 0. The objective is to minimize the cost per unit time (TC(k)/k). Using (5) and the fact that D* = khL yields

TC(k) K k -= + KhLa(khL)kQ k k

hRQ 2 ( k2 + [k_ - a(khL)k2] 2 (6)

To proceed, one must assume a functional form for a(D). The results in this paper consider the case where a(D) = a. This is an abstraction of the notion that the proportion of deal buyers is not sensitive to the level of the discount. It also yields closed form expressions that can be conveniently compared to the EOQ model (see Eppen and Lie-




berman 1980). the next most simple assumpti< a(D) = aD yields a cubic during minimization, a thus, immediately turns one to a numerical inv tigation.

The retailer's inventory holding is shown Figure 2. It shows that at the time of the de the retailer sells akQ units on deal. Then the nond consumers buy at the rate of c units per unit time.

The optimal deal magnitude can be shown be

1/2 KhL D* =

" ?(1-,)

- 1 '

(Q), the set-up cost (K), and the retailer's holding cost (hR).

on, nd es-

Discussion in aal, The model just described shows that the consumer eal and retailer decisions are interrelated. The consumer of determines the optimal quantity to buy on deal given

the optimal deal magnitude. The retailer sets an to optimal deal magnitude given the quantity con-

sumers buy on deal. The retailer increases his/her profits by dealing because he/she reduces the inventory carrying cost. A necessary condition for dealing to be profitable is that certain consumers have a holding cost for the product category that is less than one-half that of the retailer.

The optimal reorder period is

k* (8) hL

and the minimum cost per unit of time is

hR 1/2

TC = 2QKhL 2 + (1 -)- _ hL _

Equation (7) shows that the deal magnitude increases with the set-up cost (K), and decreases with the quantity bought by consumers (Q) and the retailer's holding cost (hR). If a product is purchased frequently, then the deal magnitude is less. The optimal reorder time is proportional to the deal magnitude. The minimum cost per unit of time increases with the cumulative demand rate

Implications from the Model In the previous section a jointly optimal retailer- consumer dealing model was developed. The dealing and inventory policies as derived from the model result in a set of implications that will be used to test the model with consumer panel data. The purpose of this section is to determine and discuss these implications.

Optimal Deal Magnitude, Dealing Frequency, and Quantity Bought on Deal There are three parameters that will be used in deriving the testable implications of the model: optimal deal magntiude, D*; optimal dealing fre-




quency, F*; and optimal quantity bought, q**.7 Equation (7) gives the optimal deal magnitude:

KhL - 1/2

D* = . (7)

Q t + (1 - at)2-

The dealing frequency is the number of deals offered during any given time interval, T. From the previous section, the jointly optimal time between deals is t** = D*/hL. The optimal dealing frequency, therefore, is the reciprocal of t** times T, or

1 hL F* = T = T

t** D*

Substituting (7) for D* gives

Q(2ahL + (1 - t)hR) 1/2 F* =T

2K (10)

To compute the optimal quantity bought on deal, the quantity bought by the low-holding-cost consumers is q* = Dc/hL. Replacing D by D* gives the jointly optimal quantity to be bought on deal for each consumer responding to deals,

D*c 2K 1/2

q** =- = C hL _ Q(2ahL + (1 - a)hR -

(11)

As can be seen from equations (7), (10), and (11), the dealing strategy parameters are strongly interrelated because F* is inversely related and q** is directly related to D*. Therefore, factors influencing D* also affect F* and q**. Thus, the effects of D*, F*, and q** should be treated as interdependent when studying the implications of changing a given parameter. This approach is taken throughout the remainder of this section.

Implications The equations derived in the previous section make it possible to trace the effects of the various parameters on the optimal dealing policy of the retailer. Consider K, the ordering cost. As K increases, the retailer is motivated to order less often and thus to offer deals often (F* decreases). Then in order to persuade the consumer to buy enough on deal to carry him/her over to the next deal, the retailer must make a larger price reduction (D* increases). This implies that the consumer will purchase more when the sale occurs (q * * increases).

7**denotes jointly optimal for the retailer and consumer.

As Q increases, it is clear from (7) and (10) that D* increases and F* decreases. Thus, a popular product, one with a large consumption rate, should be dealt often but with a small discount. Since Q = Nc and these two parameters have opposite effects on q** [equation (11)], it is impossible to specify the effect that an increase in Q would have on q*

To see the effect of hL, the consumer's holding cost, on the retailer's decisions, it is convenient to hold the ratio hR/hL constant. Let A = hR/hL. Rewriting (7), (10), and (11) in terms of X yields

KhL D*= } , (12)

Q [a + (1 - a)X/2] J

Q[2o + (1 -ot)X ]h, 1/2 F* = T Q[ -) L (13)

2K

2K 1/2 q** = c . (14)

_Q[2a +(- a)X]hL _ These three equations show that D* and F* increase when hL increases, whereas q** decreases. As hL increases, the consumer is less anxious to hold inventory. The retailer responds to this reluctance by making the deals more attractive, (increasing D *) and by enabling the consumer to carry inventory over a shorter interval (by increasing F*). Since F* increases, the consumer will buy less on deal at each sale, i.e., q** decreases.

These three equations show the following. The deal magnitude increases when holding cost to the consumer (hL) increases and the set-up cost (K) increases. As the rate of demand (Q) increases, more deals will occur, F* increases, but the deal magnitude decreases.

The frequency of deals increases with higher holding costs (hL) and rate of demand (Q) and decreases as the set-up cost increases.

Inventory vs. Information Explanation The implications just derived for the retailer and consumer models can be contrasted to the following explanation. Manufacturers offer trade deals to force retailers to reduce price. For the manufacturers, the purpose of the reduced price is to increase trial among nonusers of their brands. Once new consumers have tried the brand, some percentage will repurchase. Thus, dealing is a mechanism for reducing the consumer's cost of experimenting with a brand they rarely use.

The above explanation fits consumer and manufacturers' behavior for a new product introduced into the market. The consumer is seeking informa-


(9)



Trial

Small sizes will be dealt

Consumers will purchase only the unit on deal

Low market share brands will be dealt more

tion and the manufacturer is trying to reduce the cost of trial. Manufacturers offer free samples, coupons, and smaller trial sizes. This explanation has also been given for the cause of dealing for existing brands.

The implications for the information explanation just given are different than for the holding cost explanation. First, the manufacturer should offer deals on smaller sizes to reduce the risk of trial to the consumer. For new products, manufacturers often introduce a "trial" size which is substantially smaller than the regular sizes. Second, consumers would not stockpile if they are sampling "new" brands. Instead they would try only one unit of the product and would not change their purchase timing. Third, brands which have lower consumption rates relative to other brands in the category (lower share brands) would have greater dealin? activity because fewer consumers use these brands.

The implications of the two theories are contrasted in Figure 3. In the empirical section, data are used to compare the two theories.

Empirical Findings To test the model's implications, consumer panel data for four product categories will be used. The data do not give all the information (e.g., holding costs) that is needed to test the model's implications. However, these data do offer some evidence about the theory. It should be noted that trade dealing activity occurred in all the categories studied. The model given in Section 3 does not incorporate trade dealing. Some of the results may be due to the retailer receiving trade deals. However, if this sector

8Ehrenberg (1972) shows that brand penetration is highly correlated with market share.

of the model were included, one could assume the manufacturer was responding to the retailer. How this would change the result is unknown.

Description of the Data The consumer panel data are from the Chicago Tribune panel over the period 1958-1966. The advantage of this panel is that the data are from one city, and it is possible to identify deals much more accurately.

The purchase data contain number of units purchased, price paid, brand bought, size bought, store, and deal code. The key problem faced in using the deal code produced by the consumer is that it is not accurately recorded. This appears to be true across most panels. Therefore, a separate procedure was developed for coding deal purchases.

Private labels were not included in the study because it was impossible to compare them because of potential differences in quality across time. To keep the analysis consistent, only national brands were used. Casual observation of the data indicated that private labels were dealt and heavily stockpiled.

The Deal Code For each store the price for a given brand and size combination was listed by day. It is possible to identify changes in the price of an item because most chains and voluntary chains have the same price for an item (size/brand combination) across stores. A statistical procedure was developed to identify the regular price in a given period. Once the regular price was determined, the deal price was found by defining the differential between the regular price and the reported price paid. If this differential exceeded a predetermined limit, the transaction was coded as a deal transaction.

While the procedure just described may result in some errors in coding deal purchases, it appears to be far more accurate than the consumer's recorded deal code. By looking at actual transaction data, one can quickly identify when a deal is occurring because of the heavy purchasing matching a price cut. Quite frequently consumers did not record these purchases as deals. Thus, any simple visual analysis of the transaction data shows the improved accuracy of the revised deal code.

Not all of the data were analyzed because certain stores did not deal certain items. This poses a problem because if A&P buyers have different consumption rates than Jewel buyers, and A&P does not deal, this may bias the results. Voluntary chains were not used in the analysis because the data are accumulated, and there is too much variability in


FIGURE 3 Explanation

Holding Cost

Sizes with higher consumption rate will be dealt

Consumers will stockpile dealt brand

High market share brands will be dealt more frequently



TABLE 1 Consumers' Responses to Deals Through Stockpiling

Mean Number of Units

Bought per Package Deal

Size Transaction

Number of Deal

Transactions

Mean Number of Units

Bought per Nondeal

Transaction

Number of Nondeal

Transactions Difference P-value9 Aluminum foila 25.0 ft.

37.5 ft.

75.0 ft.

Facial tissueb 400 ts.

Liquid detergentc 12 oz.

22 oz.

32 oz.

Waxed paperd 100 ft.

125 ft.

1.21 (0.035)e

1.01 (0.005)

1.03 (0.015)

1.99 (0.03)

1.03 (0.006)

1.03 (0.005)

1.08 (0.0296)

1.68 (0.043)

1.29 (0.024)

160

102

60

733

250

602

165

69

106

1.04 (0.01)

1.03 (0.03)

1.00 (0.00)

1.24 (0.01)

1.02 (0.005)

1.01 (0.003)

1.02 (0.009)

1.25 (0.016)

1.02 (0.004)

441 +0.17 less than .001

97 -0.02 .07

230

2,510

1,061

+0.03 .05

+0.75 less than .0001

+0.01 .20

891 +0.02 .001

572 +0.06 .05

789 +0.43 less than .0001

1,409 +0.27 less than .0001

'Reynolds and Alcoa in Jewel. bKleenex and Scotties in A & P, National, Jewel, Kroger, and Walgreens. Clvory, Joy, and Lux (and 22 oz. Gentle Fels) in A & P, National, and Jewel. dRapinwax, Freshrap (100 ft.) and Cut Rite (125 ft.) in A & P, National, and Jewel. "Number in parentheses is standard error. fSome transactions were eliminated when it was uncertain whether they were deal or nondeal purchases. gP-value is the significance level for which the null hypothesis is rejected.

dealing practice (price, amount of discount) within the voluntary chains.

Empirical Results

Stockpiling. To see if consumers were stockpiling, two analyses were done: number of units bought on deal, and average interval between purchases. If consumers stockpile, this is evidence that the inventory theory is influencing the purchasing strategy. The most direct way to measure stockpiling is to see if more units are bought on deal purchases than for non-deal purchases. However, for certain categories consumers rarely buy more than one unit because the rate of consumption is low. They may still stockpile by buying earlier than they would if no deal were offered. Thus, both measures are necessary in analyzing deal purchases.

The results in Table 1 show that consumers do stockpile on deal. In eight of nine cases, the quantity

bought on deal is higher than for non-deal purchases.9 Waxed paper and facial tissues show much greater quantities stockpiled. If consumers are stockpiling by buying earlier than they normally would if a deal occurs, then one would expect that the time before the next purchase would be greater after buying on deal than it would be if they did not buy on deal. To measure this effect, one must first put all purchases in standard units.'1 Next, the number of standard units bought is divided into the number of days in the interval following the purchase resulting in the number of days between purchase for a standard unit. This number is calcu- lated for intervals following either a deal purchase

9A simple sign test reveals this is highly significant. The p-value is .02. Some of the differences are small (e.g., .02). These are categories in which purchase timing is usually affected.

'OThe standard units are: aluminum foil-25 ft. roll, waxed paper-100 ft. roll, liquid detergent-22 oz. size, facial tissue-400 tissue box.


Product Category



or a nondeal purchase in which the next purchase is a nondeal purchase."

The inventory theory implies that the number of units bought per day will be less after a deal purchase than a non-deal purchase. To show this, suppose household 1 buys one unit on deal at time to. At the time of their purchase they had one unit in stock and they normally consume two units per month. They do not buy until they run out unless a deal occurs. Assuming no deals occur until after the next purchase, they will next buy one month after time to. If no deal occurred, they would have waited to purchase for two weeks, purchase one unit and repurchase again two weeks later. Thus, the interval to next purchase is longer after their deal purchase (one month) than after their non-deal purchase (two weeks). Stockpiling is occurring but not through more units per purchase occasion but through a higher on-hand inventory. A measure of this is the number of days between purchases.

The results are given in Table 2. They support the inventory dealing theory. For all four product categories the difference is quite large, and, no doubt, highly significant statistically. If one divides the difference by the interval following non-deal

Since the objective is to see whether dealing changes the purchase interval, one wants to hold fixed one end of the interval, i.e., the last purchase is nondeal. The prior purchase is then on deal or not. These two cases can then be compared. Adding deal to deal purchases does not allow us to hold fixed one end of the interval to make the comparisons required. It was therefore omitted from the analysis.

purchases, one sees that the time between purchases increases after a deal purchase between 23% and 36%, depending upon the category. It is apparent that there is stockpiling occurring even for categories (aluminum foil and liquid detergent) which had small quantity stockpiling.

Deal Magnitude and Deal Frequency. The first of these implications is the effect of consumption rate, c, and number of buyers, N, on the optimal values of deal magnitude and dealing frequency as set by the retailer. Since Nc = Q this implication will be analyzed in terms of Q, or the sales level of the retailer. The prediction is that within given product/size/chain combinations, brands with higher sales levels (higher values of Q) will be offered on deal more frequently and for lower deal magnitudes than brands with lower sales levels. These effects are directly observable from equations (7) and (10). The implication is analyzed at the brand level because this holds the holding cost of the product constant. Performing the analysis for given chains keeps constant chain variables (e.g., set-up cost, which may vary from chain to chain).

To account for possible dealing effects on sales (Q), weekly sales of each brand studied were re- gressed against their weekly prices to obtain an estimate of normal sales after adjusting for dealing effects. Substituting the normal (non-deal) price of the brand for the independent variable in the resulting regression equations and solving the equations, gives normal (deal controlled) weekly sales for each brand. The normal weekly sales were then multiplied


Table 2 The Effect of Deals on Post-Deal Purchase Timing

Mean Time Mean Time Interval Interval

(in Days) (in Days) between between Deal and Deal and Nondeal Number of Nondeal Number of Difference

Product Category Purchases Intervals Purchases Intervals (in Days) P-valueb Aluminum foil 95.65 374 71.65 1,198 24.00 less than

(5.55)8 (0.575) .0001

Facial tissue 63.61 935 47.6 3,543 15.98 less than (2.4) (1.2) .0001

Liquid detergent 66.18 745 53.72 2,524 12.36 .0005 (3.39) (1.58)

Waxed paper 135.88 255 100.25 1,005 35.63 .0002 (8.88) (3.625)

"( )indicates standard error. bP-value is the significance level for which the null hypothesis is rejected.



by the number of weeks included in the recorded history of each brand which resulted in normal (adjusting for deals) sales volumes. Brands are then compared in terms of their normal sales. Section 3 showed that it is expected that brands with higher normal sales will have a higher dealing frequency and be offered at a lower mean deal magnitude than brands with lower normal sales.

These predictions are tested only in terms of their directions, not magnitudes. Using Table 3 as an example, it is expected that for the 25 ft. aluminum foil in Jewel food chain, Reynolds (normal sales = 395) will be offered on deal at a higher frequency and for a lower mean deal magnitude than Alcoa (normal sales = 143). The results show that these predictions are confirmed. A summary

of the results for the aluminum foil category is given by Table 4.

To save space the detailed results for the other product categories are not presented here. However, summary results for all product categories are reported in Table 5. These results seem to support the inventory dealing model. The null model would imply correct prediction 50% of the time.12

The second implication concerns the effect of joint (to consumer and retailer) per unit holding cost on the parameters of deal magnitude and dealing frequency. This implication is tested at the package size level. By varying the package-size of an item (within given product / chain combinations), its holding cost varies. However, by varying the package-size variable the sales variables may change as well, which will have its own effects on both dealing parameters. Since it is impossible to hold

'2A statistical test is very difficult to apply because predictions are not independent. Therefore, a standard binomial test of p = .5 cannot be used.

TABLE 5 Summary of Predictions for Brand Dealing Parameters: All Product Categories

Deal Dealing Magnitude Frequency

46

29

59.2

Dealing Parameters Number of predictions

Number of correct predictions

Percentage of correct predictions

49

31

63.3


Table 3 The Effects of Normal Sales on Deal Magnitude and Dealing Frequency in the Aluminum Foil Product Category

Mean Deal Package Normal Purchasing Magnitude Dealing

Size Chain Brand Rate (in units) (in cents) Frequency 25.00 ft. National Reynolds 96 4.50 2

Alcoa 78 6.00 1 Kaiser 31 7.20 5

Jewel Reynolds 395 6.12 17 Alcoa 143 9.08 6 Kaiser 18 0

37.50 ft. Jewel Reynolds 177 15.63 19 Alcoa 30 17.00 3

TABLE 4 Summary of Predictions for Aluminum Foil

Deal Dealing Dealing Parameters Magnitudec Frequency Number of predictions 5 7 Number of correct predictions 5a 5b

Percentage of correct predictions 100.0 71.4 825.00 ft. (Reynolds, Kaiser) and (Reynolds, Alcoa) in National and Jewel and (Reynolds, Alcoa) in Jewel; 37.50 ft. (Reynolds, Alcoa) in Jewel. b25.00 ft. (Reynolds, Alcoa) in National and Jewel, (Reyn- olds, Alcoa) and (Alcoa, Kaiser) in Jewel; 37.50 ft. (Reyn- olds, Alcoa) in Jewel. CFewer deal magnitude predictions exist because no deals occurred for Kaiser at Jewel. Thus, the deal magnitude is unknown.



sales constant, the analysis will focus on the combined effect of per-unit holding cost and sales on the two dealing parameters. This, however, requires some care since sales (Q) and holding cost (hL)

affect the deal magnitude (D*) and the dealing frequency (F*) differently. Consider equations (12) and (13). It is clear that we can determine the behavior of D* and F* in four cases. these cases are described in Table 7, where + stands for an increase and - stands for a decrease in the variable in question.

Predictions about the behavior of dealing parameters are generated from data by rank order-

TABLE 8 The Combined Effect of Holding Cost and Normal Sales on Deal Magnitude and Dealing Frequency at the Package-Size Level

Standardized Normal Purchasing Rate

Unit Holding Package Cost: Rank

Chain Size Ordering A & P 200 ts.

400 ts.

National 200 ts. 400 ts.

Jewel 200 ts. 400 ts.

A&P 12 oz. 22 oz. 32 oz.

National 12 oz. 22 oz. 32 oz.

Jewel 12 oz. 22 oz. 32 oz.

A&P 100ft. 125 ft.

National 100 ft. 125 ft.

Jewel 100 ft. 125 ft.

2 1

2 1

2 1

3 2 1

3 2 1

3 2 1

2 1

2 1

2 1

Rank Units (in Dealing Ordering thousands) Frequency

2 15.4 0 1 126.0 9

2 77.6 0 1 420.0 23

2 1

3 1 2

3 1 2

3 1 2

2 1

2 1

2 1

51.0 325.6

3.68 5.72 5.25

5.20 9.46 8.58

7.2 18.85 10.34

21.7 63.75

48.6 50.5

39.2 70.75

1 18

12 33 8

27 51 17

27 67 27

1 10

4 10

11 11

Mean Deal Magnitude (in cents)

8.74 13.43

8.12 12.05

8.06 10.49

'Including the brands of Kleenex and Scotties. blncluding the brands of Ivory, Joy and Lux (and Gentle Fels for the 22 oz. package size category). Clncluding the brands of Freshrap and Rapinwax for the 100 ft. size and Cut Rite for the 125 ft. size category.


TABLE 6 Summary of Predictions for Dealing Parameters at the Package-Size Level

Theory Inventory Dealing Deal Dealing

Parameters Magnitude Frequency Number of predictions 3 12 Number of correct predictions 3 8

Percentage of correct predictions 100 66.7

TABLE 7 The Effect of Q and h, on D* and F*

Q hL D* F'

+ + + + + +

Product Category Facial

tissuea

Liquid detergentb

Waxed paperc

Facial



ing different packages-sizes (within given product/chain combinations) by size and normal sales. The data are summarized in Table 8. The relation- ships in Table 7 are then used to make as many predictions as possible. Consider, for example, facial tissues at A&P. Since both hL and Q are greater for the 400-tissue size, Table 7 suggests that it should be dealt more often, and that no prediction can be made regarding D *. The prediction is correct. All of the results on facial tissue and waxed paper can be interpreted this way.

The situation with liquid detergent is somewhat more complicated. Again, consider the A&P data. The model suggests that the 32 oz. size should be dealt more often than the 12 oz. size. This prediction is incorrect. It is not possible to make a prediction about the relative frequency of deals for the 32 oz. and the 22 oz. sizes. However, the prediction that the 22 oz. size will be dealt more often than the 12 oz. size is correct.

It is also possible to use the data on liquid detergent to make predictions about deal magnitude. For example, compare the 32 oz. size to the 22 oz. size at A&P. Note that hL is greater and Q is smaller. Table 7 predicts that the discount on the 32 oz. size will be greater, and the prediction is correct. Table 6 summarizes the results of the fifteen possible predictions.

Inventory vs. Information Explanation Earlier, we stated several contrasting implications for the information and the inventory explanations. Using the data shown, the contrasting implications can be evaluated. The information explanation would imply that consumers would not stockpile products whereas the inventory explanation assumes consumers do stockpile products; the data given above show that consumers do stockpile all four products analyzed.

Next, the information explanation implies that lower volume items would be dealt more frequently, whereas the inventory explanation implies that higher volume items would be dealt more frequently. The results given above show that higher volume items deal more frequently. Again, the results favor the inventory explanation.

Finally the information explanation implies that smaller sizes should be dealt more frequently than larger sizes. The inventory explanation implies higher volume sizes would be dealt more frequently. In Table 1 for liquid detergent, it is shown that the smaller size (12 oz.) is dealt far less frequently than the 22 oz. which has slightly higher volume. Thus, volume, not size, appears to be a more important determinant of which item to deal.

The data presented appear to favor the inventory explanation in all three analyses. A more rigorous comparison is needed before one can reject the information explanation, but on the basis of the data presented in this study, it is less likely that dealing occurs as a form of information (for existing products) than as a form of shifting holding costs. It is also possible that a certain proportion of dealing is done to induce trial and the remainder related to transferring holding costs to low holding cost consumers.

Strategy Implications for Manufacturers and Retailers The implications from the consumer and retailer model can be used to help design dealing strategies for both manufacturers and retailers.

Manufacturer's Dealing Strategy The model and the empirical results suggest that consumer stockpiling is an important reaction to dealing for established, storable products. This fact has serious implications for the manufacturer who wishes to attract new customers to his/her product. Much of the cost of a retailer deal may not be at all related to new trials. A manufacturer thus may wish to devote increased attention to other devices such as home delivered samples and dem- onstrations in stores in attempting to attract new customers.

However, the manufacturer can use stockpiling behavior to his/her advantage. Trade deals can be timed to match production runs so that retailers and consumers stockpile the product, thus carrying inventory the manufacturer would otherwise be forced to carry. For products with high set-up costs and infrequent production runs, dealing can be an efficient mechanism for transfering inventory.

It is important to note the effect of the function a(D), the proportion of consumers who buy on deal, on the effectiveness of dealing. In equation (5) note that if a(D) is small, the retailers' costs are essentially the same as in the no deal case. Thus, the trade deal will have little effect. In these situations manufacturers should consider reducing trade dealing activity and attempting to offer retailers higher margins through lower case prices.

For products with low consumption rates, trade deals are very unlikely to be passed on to the consumer. Again, equation (4) shows that QD depends upon c. Retailers will take small ads in the Saturday paper ("obituary" ads) and reduce price on Monday-Wednesday (weak shopping days) to comply with the agreement. The manufacturer may be more successful using other forms of




promotions or directly advertising to the consumer. For sizes with low consumption rates, the retailer

is unlikely to deal the product even with a trade deal. To increase the purchase of low volume sizes, manufacturers can consider alternative actions like special consumer promotions, tie in promotions with stronger sizes, and special consumer advertising.

Retailer's Dealing Strategy The models discussed in this paper can be used by retailers to develop a more effective dealing strategy. An approach followed by Eppen and Lieberman (1980) is to derive and compare expressions for the cost per unit time with and without dealing. This approach leads to two main conclusions: (a) High-volume items, those with a large value of Q, yield greater profits to the retailer if he/she deals. We have already seen that for such products the optimal dealing policy involves fre- quent deals with relatively small discounts. (b) Items with widely different holding costs between the retailer and a segment of consumers are advanta- geous for the retailer to deal. This suggests that bulky items like paper products are good candidates for dealing.

Another strategy implication of the theory is dealing locally. This follows from the model because dealing is efficient when the proportion of low holding cost consumers is high. This may not be uniform over an SMSA. Obviously there are effi- ciencies in buying newspaper ads throughout an SMSA. However, through the use of flyers and store specials, retailers can also develop local deals which may be successful in one area but not another. Factors which influence the selections of items to deal are similar to those just discussed for the total market. However, consumption rates of products and storage costs may vary by region. For areas where households have freezers, retailers should deal items for the freezer. For areas with larger homes, bulkier products should be dealt. From

scanning data it becomes possible for the retailer to analyze sales data by item by store so that more effective dealing can be done.

Within brands of a category it is rarely profitable for the retailer to deal low share items. Unless a strong performance contract exists, the retailer should run these items during low volume days such as Monday, Tuesday, and Wednesday so that he / she can perform on the trade deal requirements.

Conclusions This paper began with the assumptions that the consumer purchases in order to minimize total cost (purchase cost and holding cost) and that the retailer sets price to maximize his profits. Based on these assumptions and other assumptions made throughout, the optimal deal price, frequency of dealing and quantity bought on deal were derived. Testable implications were then generated and using panel data, they were tested empirically. The empirical results showed that the inventory explanation for dealing is consistent with the data.

A major benefit of the view that consumers respond to deals by inventorying is that it leads to certain implications for the measurement of the consumer's response to deals. Instead of assuming that dealing increases sales through brand switching, the inventory theory explanation implies sales increase because consumers stockpile. This results in a trough in sales after a deal. Unless the dealing response model accounts for this trough, the effect of a deal will be greatly overstated.

This article offers a different explanation than the one most commonly held that dealing is used to attract customers to the store. Both explanations can be true and the cause of dealing. Theoretical and empirical work needs to be done comparing the inventory theory, proposed here, with other explanations for dealing. It is hoped that further work is developed to study these issues.

REFERENCES Blattberg, Robert C., Thomas Buesing, Peter Peacock, and

Subrata Sen (1978), "Identifying the Deal Prone Seg- ment," Journal of Marketing Research, 15 (August), 369-377.

Ehrenberg, A. S. C. (1972), Repeat Buying, Amsterdam, Netherlands: North-Holland Publishing Company.

Eppen, Gary and Yehoshua Lieberman (1980), "Why Do Retailers Deal? An Inventory Explanation," working paper.

Frank, Ronald E. and William F. Massy (1967), "Effects of Short-Term Promotional Strategy in Selected Market Segments," in Promotional Decisions Using Mathemati-

cal Models, Patrick J. Robinson, ed., Boston: Allyn & Bacon, 147-225.

Kunreuther, Howard (1973), "Why the Poor May Pay More for Food: Theoretical and Empirical Evidence," Journal of Business, 46 (July), 368-383.

Lieberman, Joshua (1978), "A Theory of Price Deals in Supermarkets," Ph.D. dissertation, University of Chica- go.

Wagner, Harvey M. and Thomson M. Whitin (1958), "Dy- namic Version of the Economic Lot Size Model," Man- agement Science, 5, No. 1 (October), 89-96.




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