IV. Substantive Findings andApplications

ROBERT BLAHBERG, THOAAAS BUESING, PETER PEACOCK, and SUBRATA SEN*

A model of consumer buying behavior is used to identify householdcharacteristics that should affect deal proneness. The model treats householdpurchasing and inventory decisions like those of a firm. In other words, thehousehold's purchasing decisions are assumed to be based on such factorsas transaction costs, holding costs, and stockout costs in addition to productprice. Household characteristics then are related to these cost parametersto identify households that are likely to be deal prone. The predictions aretested empirically by use of panel data for five frequently purchased products.The empirical results indicate that deal prone households can be identifiedand that the key variables affecting deal proneness are household resource

variables such as home ownership and automobile ownership.

Identifying the Deal Prone Segment

Blattberg and Sen [4,5] present a method of definingmarket segments based on purchase patterns. Theusefulness of this approach is enhanced if segmentmembership can be identified on the basis of availabledemographic data. The purpose of this article is toshow that deal prone consumers as defined by Blatt-berg and Sen [4, 5] are identifiable.

Marketing managers always have been interestedin identifying the deal prone household on the basisof available demographic data. If such householdscan be identified precisely, specific marketing strate-gies designed to appeal to such households are like-ly to be more effective. For example, demographicinformation is available by zip codes or census tracts.If certain demographic groups are more deal prone,coupon distribution could be restricted to those areaswhere households with higher deal proneness reside.This approach would reduce couponing costs with aless than proportionate reduction in response. Similar-

'Robert Blattberg is Professor of Marketing and Thomas Buesingis a Ph.D. student in Marketing, University of Chicago; PeterPeacock is Associate Professor of Management, Wake ForestUniversity; and Subrata Sen is Associate Professor of BusinessAdministration, University of Rochester.

This research was funded in part by National Science FoundationGrant SOC73-05547.

ly, more accurate identification of deal prone house-holds would increase the marketer's ability to matchdeal prone households and media audience charac-teristics, and thus increase the efficiency of mediadistribution of coupons and other promotional items.

Several researchers have tried to identify the dealprone household. Webster [13] and Montgomery [12]published two of the better known studies. The resultsof these and other studies are summarized by Franket al. [6, p. 124], who state:

The results of cross-sectional studies, almost withoutexception, indicate that there is, at best, only a modestdegree of association between demographic, socioeco-nomic, and/or personality characteristics, and selectedaspects of household purchasing behavior, such as totalconsumption, brand loyalty, and deal proneness.

One reason for this "modest degree of association"may lie in the methodological approach usually takenin these studies. Typically, a large number of potentialexplanatory variables is regressed against the pro-portion of purchases made on deal in a search forstatistical significance. For example, Webster [13]ran 200 regressions with different combinations of45 explanatory variables. This approach is open toserious question because one cannot always determinewhether "significant" relationships refiect a validrelationship or a spurious one which has arisen by

369

Journal of Marketing ResearchVol. XV (August 1978), 369-77

370 JOURNAL OF MARKETING RESEARCH, AUGUST 1978

chance alone. Without a theory to indicate whichvariables should affect deal proneness, the researcherrisks accepting spurious results.

A related deficiency of prior studies of deal prone-ness arising from the absence of a clearly statedtheory is improper specification of explanatory vari-ables. For example, Montgomery [12] included"presence of children" as an independent variablein a regression model but was unable to predict apriori whether the presence of children should orshould not increase deal proneness. However, if onehypothesizes that it is the age of the children thataffects deal proneness (rather than their presence perse), it is possible to predict a priori the impact of"age of children" on deal proneness. If the childrenare below the age of six (and are consequently notyet in school), they require more of their parents'time, thus reducing the time the parents have availablefor shopping. Less time for shopping results in fewershopping trips and fewer opportunities to take advan-tage of deals. Consequently, the household's dealproneness is reduced. Montgomery's results [12] ledhim to conclude that presense of children was notrelated to deal proneness. However, by consideringthe presence of children without determining how theirpresence should affect deal proneness, Montgomerymay have arrived at an incorrect conclusion abouta variable which, if properly specified, could wellbe related to deal proneness.

Like the researchers cited, the authors attempt toidentify the deal prone household. However, theapproach used here is different from that used in mostof the earlier work. First a model of householdpurchasing behavior is formulated. The model thenis used to predict how certain demographic variablesshould affect deal proneness. Finally, an empiricalevaluation of the predictions is made. The empiricalresults show that it is possible to identify the dealprone household by using demographic variables andthat the effect of these variables is substantial.

MODEL OF HOUSEHOLD PURCHASINGBEHA VIOR

Model AssumptionsDevelopment of a household inventory model is

based on the assumption that the household is aproducing unit which needs to stock inventory andmeet demand. This assumption follows from the notionof the household as a production unit which Becker[1] and others used to model consumer behavior inthe economics literature. This approach is used herebecause it has proved to be very fruitful in severalapplied studies in economics [11] and also becauseit appears to be a promising approach in marketing[10]. The inventory model proposed here also corre-sponds closely to models developed by managementscientists to make better inventory decisions in moretraditional production environments [8, p. 472-527].

The initial assumption in the model is that house-holds make long-term decisions about whether to usea given product at all and the average number of unitsof the product to use per period. Decisions aboutproduct use and the average usage rate are determinedexogenously by such factors as family size, familyincome, etc. Another assumption is that ratios of pricesof the product in question and prices of substitutesand complements are constant during the period con-sidered so that households need not evaluate theirusage rate decision because of changes in relativeprice. The latter assumption is made in order to obtaina tractable model and because it permits concentrationon the purchase timing decision which is the basicfocus of this article.

The model is based on two additional assumptions:(1) aU brands in the product class yield the same utilityto the consumer and (2) the consumer purchases onlyone brand of the product class. Again, these assump-tions are made only to obtain a tractable model.However, neither assumption is particularly restric-tive.

Consider first the assumption that all brands in theproduct class provide the same utility to the consumer.For certain multibrand segments defined by Blattberget al. [3, 4] (such as national brand switchers, nationalbrand switcher deals, private label switchers, andprivate label switcher deals), it is clearly reasonableto assume that consumers are indifferent in termsof preference among a subset of brands. It is alsoreasonable to make a similar assumption about buyersof a single brand (e.g., members of the national brandloyal segment [3, 4]). If all brands provide the sameutility to the consumer, it is sensible for him to restrictpurchases to a single brand because such a purchasingstrategy minimizes decision-making costs on eachpurchase occasion (see, for example, [14]). If,however, different brands provide different utilitiesto the consumer, a model which jointly considers utilitymaximization and purchase timing would have to bedeveloped.

The assumption about the purchase of a single brandis even less restrictive. To extend the present modelto households that purchase many brands that areabout equally preferred, one only needs to recognizethat such households have a larger array of pricesto consider in the decision process. Slight differencesin behavior would result because there are more dealsavailable to, say, national brand switcher consumers(who are willing to buy two or three brands) thanto, say, national brand loyal consumers (who arewilling to buy only one brand). The result should beless stockpiling for national brand switcher house-holds.

Cost Structure of the HouseholdFour categories of cost affect household inventory

decisions: (1) transaction cost, (2) storage cost, (3)stockout cost, and (4) the actual price of the item.

IDENTIFYING THE DEAL PRONE SEGMENT 371

Transaction cost is the opportunity cost of the timerequired to purchase an item once the consumer isactually in a store plus the opportunity cost of traveltime required to get to and from the store where thepurchase takes place. Transaction cost will vary acrossstores. And if a consumer has a "regular" or preferredstore, one would expect the transaction cost of anitem purchased there to be less than if a special tripwere made to purchase the item at some other store.Storage cost represents interest on the capital requiredto maintain a given level of inventory plus the costof the required space. Stockout cost relects the fore-gone utility of not consuming an item which is notin stock at the time it is demanded. If the householdcan easily substitute other items in the event ofstockout, or if it derives little utility from consumingthe item, stockout cost should be low. Observed priceper unit is the fmal component of cost. For purposesof the analysis, the observed price in a store is assumedto be constant within any given period, e.g., a week.Prices may differ across stores and may change fromperiod to period.

Mathematical Formulation of the ModelThe household's purchase decision process is repre-

sented mathematically as:

(1)

subject to:

(2)

mm u,s\J

{'^i-'*'"''"' i f ; i f , , > 0 f o r i = l , . . . ,

l o lf^, . , = O

(3) /, =ifrf,>/,.,+ 2^,,,

(4) 5, =

(5)

where:

0 < / < / for aU

T,, = transaction cost at store / at time t,P,, = price per unit of product at store / at time /,X,, = quantity purchased at store /' at time /,

/, = inventory on hand at beginning of time /,d, = quantity at time /,h, = unit holding cost at time /,S, = amount of stockout at time t,u, = unit stockout cost at time {,/ = the maximum inventory that can be stored each

period, andK = the number of stores.

The model described in equations 1-5 indicates thatthe household's objective is to purchase the productat a minimum "total cost." Given the assumptionthat all brands in the product class yield the sameutility to the consumer, minimization of cost is theappropriate objective function. The costs being mini-mized are expected costs over a finite time horizonwhich includes present as well as future periods. Thus,expectations about future demand ahd future pricesaffect the present period's decision. Note that thoughthe household's average demand per period is known,the exact quantity demanded in each period is unknownat the start of the period. Thus, this quantity, J,,is represented as a random variable in the model.Future price expectations are generated by probabilitydistributions of the time between deals and the lengthof time a given deal is in effect. These distributionsare based on actual household experience with dealduration and time between deals in each store.

The model described is different from a one-periodminimization problem. It incorporates the consumer'sexpectations about future prices. If consumers cananticipate accurately when deals occur as well as theirduration, their current behavior should be affected.For example, if a store always deals a product formore than one period, a consumer's purchase timingbehavior should be significantly different from hisbehavior when the deals last only a single period.By extending the decision horizon to more than oneperiod, the model is able to incorporate consumerexpectations.

Finally, equation 5 indicates that there is a storageconstraint in the model. A consumer can have nomore than a preset maximum number of units (/) ininventory in any period. This storage constraint isincluded in the model only because it simplifies thesolution to the problem. The solution technique usedis probabilistic dynamic programming [8, p. 269-74]and the storage constraint makes the number of statesin the dynamic programming formulation finite.

VA L UE OF THE IN VENTOR Y MODELThe inventory model described provides a theoreti-

cal basis for selecting demographic and householdresource variables which should be associated withdeal proneness. Consider Figure 1 which is a dia-grammatic representation of the manner in which theinventory model links demographic and resource vari-ables with household deal proneness. This diagramis used to indicate how the model enables the re-searcher to identify household variables that affectdeal proneness.

Deal Proneness and Household Cost ParametersThe first link of interest in Figure 1 is the one

that connects household cost parameters with dealproneness through the inventory model. This linkindicates how the cost structure facing a householddetermines whether or not it will be deal prone. For

372 JOURNAL OF A^RKETING RESEARCH, AUGUST 1978

Figure 1DEAL PRONENESS AND HOUSEHOLD DEMOGRAPHIC

AND RESOURCE VARIABLES

DEMOGRAPHICVARIABLES

(e.g., family size, income)

HOUSEHOLD RESOURCEVARIABLES (e.g.,housing and trans-portation)

PRICEDISTRIBUTIONACROSS STORES

HOUSEHOLD USACERATE FOR THE

PRODUCTHOUSEHOLD COST

PARA^!ETERS

INVENTORY MODEL OFHOUSEHOLD BUYING

BEHAVIOR

IDEAL PRONENES

example, if a household's storage costs were low,one would expect it to stock up on a commodity whena deal is on. Similarly, if the household's transactioncosts were low for all stores, one would expect itto buy primarily on deal because the household couldeasily take advantage of deals offered by any of thestores.

Cost Parameters and Household Demographics andResources

Having seen how the household's cost structureaffects deal proneness, one now must identify thefactors that determine a household's cost structure.For this purpose, consider the link in Figure 1 whichconnects demographic variables (such as income) tohousehold cost parameters through household re-source variables (such as housing and transportation).Note first that income is an important determinantof household resources, i.e., households with higherincome are more likely to own homes (as opposedto being renters) and are also more likely to ownone or more cars. These household resources, in turn,affect the cost parameters of the model. For example,home owners typically have more storage space avail-able than apartment dwellers and hence should incurlower storage costs. Similarly, car ownership makestransportation easier, thereby reducing the house-hold's transaction costs.

It was noted that low storage costs and low transac-tion costs both lead to deal proneness. Because lowstorage costs are associated with home ownership andlow transaction costs with car ownership, specificpredictions can be made, such as: home owners andcar owners will tend to be more deal prone thanapartment dwellers and households without cars.

One can see, therefore, that the inventory model'shnks with deal proneness and with household costparameters make it possible to (1) identify the relevant

variables that should affect deal proneness and (2)predict the direction of their effect.

EFFECT OF DEMOGRAPHIC ANDHOUSEHOLD RESOURCE VARIABLES ON

DEALPRONENESSIn this section, the authors formally state predictions

of how some specific household resource and de-mographic variables lead to deal proneness. Threetypes of variables are studied: (1) household resourcevariables such as car ownership and home ownership,(2) time-related variables such as the housewife'semployment status and age of the youngest child, and(3) income.' Note that the data for these variablesare available by zip codes or census tracts. Therefore,if these variables do affect deal proneness, the mar-keting manager can implement the results easily. Incontrast, some of the variables found by Webster [13]and Montgomery [12] to affect deal proneness areless directly applicable. For example, both found thatbrand loyalty was associated negatively with dealproneness. However, one must first identify who theless brand loyal consumers are before one can usesuch a fmding.

Household Resource Variables and Deal PronenessA key component of the transaction cost of shopping

is transportation cost. Households that do not havecars available are forced to shop at stores that arenearby. They are also more likely to shop at a singlestore [ 10, p. 376]. Because the ability to take advantageof deals depends on the freedom to shop often andat many stores, households without cars should beless deal prone. ^

The second household resource variable is homeownership. This variable should be related to holdingcosts. Apartment dwellers usually have less storagespace available than homeowners simply becauseapartments are smaller. Therefore, holding costsshould be higher for apartment dwellers. Becauselower holding costs should lead to greater deal prone-ness, homeowners should be more deal prone thatapartment dwellers.

' Numerous other variables could have been considered but theinventory model concentrated on resource variables. Thus, theseare the ones examined here.

^This heuristic argument and the ones that follow are consistentwith the results of a simulation conducted to check the sensitivityof the model's solution (obtained by dynamic programming) tochanges in the model's parameters. The parameters that were variedin the simulation were such items as transaction costs and storagecosts, and the simulation allowed assessment of their quantitativeimpact on deal proneness. The simulation could not be publishedbecause of space constraints. However, interested readers canobtain a copy of the simulation results by writing to Subrata Sen,Graduate School of Management, University of Rochester, Roches-ter, New York 14627.

IDENTIFYING THE DEAL PRONE SEGMENT 373

Effect of Income on Deal Proneness

The usual argument given in support of a negativerelationship between deal proneness and income isthat low income households have lower opportunitycosts of time, and thus lower search and transactioncosts. Furthermore, economic theory suggests thatlower income households should be more price sensi-tive. Empirical research in marketing rarely has shownthat income affects deal proneness (see [13] forexample). If an effect is found at all, higher incomeseems to be associated with greater deal pronenessrather than less.

The problem with studying the effects of incomeis that income effects are confounded by the effectsof household resource variables. For example, higherincome households are more likely to buy capital goodssuch as cars and homes, household resources whichincrease their ability to buy on deal. The interactionbetween the negative effects of income and the positiveeffects of household resources may result in theanomalous fmding that high income households aremore deal prone than low income households. Ifresources available were held constant, however, oneshould observe the opposite outcome.

Effect of Time on Deal PronenessAn important household decision is the amount of

time to allocate to shopping. This decision will dependon other time demands facing the household. Twofactors which shotild affect these time demands greatlyare (1) the presence of children under six and (2)whether both the husband and wife work. A childbelow the age of six (who therefore does not attendschool) requires large time inputs from his or hermother. Further, when the housewife does go shop-ping, she often may need to hire a babysitter, to useone of her other children to take care of the child,or to take the child with her. The result should beincreased transaction costs of shopping. Specifically,the household will have higher transaction costs acrossall stores which result in less frequent shopping trips,more units purchased per trip, and purchasing on dealonly if a deal is available during the trip. Once thechild reaches the age of six, and begins school, theamount of time the housewife must spend with thechild is decreased and a given shopping trip becomesless costly. Thus, households with at least one childbelow six should be less deal prone than householdswith no children below six.

The other factor affecting the amount of timeavailable for shopping is whether both husband andwife are employed. Additional demands placed on ahousehold's time because of the wife's employmentshould lead to a reduction in the time available forshopping. Thus, for such households, transaction costsshould increase for all stores, and less deal pronenessshould be observed.

Summary

In summary, the households most likely to be dealprone are (1) homeowners, (2) car owners, (3) house-holds with no children under six, and (4) householdswithout working wives. In the next section consumerpanel data are used to test these predictions.

EMPIRICAL RESULTSThe data used to analyze deal proneness were the

Chicago Tribune Panel puchase data and associateddemographic variables. Consumers classified intothree segments defined by Blattberg et al. [3]thenational brand loyal deal, the national brand switcherdeal, and deal-orientedare defined here as beingdeal-oriented. All consumers classified into one ofthe other stable pattern categories (i.e., not includingthe changing pattern or last purchase loyal pattern)constituted the non-deal-prone population.^ Fiveproduct categories were studied: aluminum foil, waxedpaper, headache remedies, liquid detergent, and facialtissue. The data were gathered from 1958 to 1966,depending on the category." The household variablesstudied are those described in the previous section.

Blattberg et al. [3] classified each household'spurchase patterns into segments. Deal proneness wasbased on membership in one of the three segmentsand was a dichotomous variable: deal prone or notdeal prone. Because the effect of the independentvariables on deal proneness may be nonlinear, theauthors decided to use cross-classification analysisinstead of regression [see 6, p. 126-9]. A majorproblem in doing cross-classification analysis is thatsample sizes may become small for certain cells whentwo or three sets of independent variables are analyzedsimultaneously along with the dependent variable. Thisproblem is particularly vexing here because mostdemographic and household resource variables areintercorrelated. For example, high income householdsthat rented and did not own a car are a very smallpercentage of all high income households. Initially,the data are analyzed individually for each explanatoryvariable. Then certain combinations of variables areconsidered together. In future studies, if larger samplesare available, interrelationships between more sets ofexplanatory variables should be studied.

Household Resource VariablesThe first two variables analyzed are home ownership

and car ownership. Tables 1 and 2 give the resultsfor each of the five product categories. The table

The nonstable buying patterns include some deal prone house-holds which for certain periods of the data were deal prone andfor other periods were not. They are excluded because they weredifficult to categorize. The size of this group is never more than20% of the total consumers and is usually much smaller.

* Aluminum foil (1962-66), waxed paper (1963-66), liquid detergent(1959-61), facial tissue (1958-61), and headache remedies (1959-61).

374 JOURNAL OF MARKETING RESEARCH, AUGUST 1978

Table 1HOME OWNERSHIP

Homeownership

RentOwn

Aluminumfoil

30.9%' (SI)"37.5 (120)

Waxedpaper

12.5% (88)29.5 (132)

Product category

Headacheremedies

22.7% (97)29.8 (151)

Liquiddetergent

29.6% (98)38.9 (229)

Facialtissue

23.4% (137)28.7 (230)

'The table entry is the percentage of aluminum foil buyers who rent a home and are deal prone.''Numbers in parentheses are total within-cell sample sizes on which each percentage is based.

Table 2CAR OWNERSHIP

Carownership

No carCar

Aluminumfoil

24.0%' (50)"38.4 (151)

Waxedpaper

17.1% (70)25.3 (112)

Product category

Headacheremedies

19.4% (67)3.0 (180)

Liquiddetergent

26.9% (67)38.8 (258)

Facialtissue

25.0% (88)27.3 (278)

"The table entry is the percentage of aluminum foil buyers who do not own a car and are deal prone."Numbers in parentheses are total within-cell sample sizes on which each percentage is based.

entries are the percentages deal prone. For example,in the case of waxed paper, 29.5% of the householdsthat owned a home were deal prone and 12.5% ofthe households that did not own a home were dealprone.

The results in Tables 1 and 2 suggest that owninga car or a home makes a household much more dealprone. For every product category this result holds.These results are consistent with the predictions thathome ownership and car ownership should be asso-ciated with greater deal proneness.

One problem with analyzing the variables separatelyis that if one owns a car, one is also more hkelyto own a home. Thus, the observed effect may bedue to one of the two variables and not the other.Table 3 shows the effect of the two variables jointly.Except for facial tissue, owning both a home anda car results in the highest probability of being dealprone. The percentage deal prone is always higherwhen a household owned a car and a home than whenit owned a car and rented. It is higher for four ofthe five products when a household owned both acar and a home than when the household owned ahome, but not a car. Thus, the effect does not appearto be due to only one of the two variables.

To get some idea of the magnitude of the effectthat owning both a car and a home had on dealproneness, the following model was estimated.

(6)

where:

1=1,...,4

01 = average deal orientation,3, = the effect of consumer characteristic / on deal

orientation,y = the effect of product category j on deal orienta-

tion, ande,, = the disturbance term.

Because product categories with very low deal prone-ness may have a lower absolute difference in dealproneness for a given household characteristic thanproduct categories with high deal proneness, a multi-plicative model was used. If one takes logarithms ofboth sides of equation 6, the effects (3, and 7 ) canbe measured by using estimates calculated from stan-dard analysis of variance formulas.' The constraintsare that

The model is similar to the log-linear models describedby Green et al. [7] and Bishop et al. [2]. The exactestimates used are given in [9, p. 327-34].

Estimates of the model's p, parameters are presentedin Table 4. The results show that owning a car anda home yielded a 3 of 1.366, whereas not owninga car and renting yielded a 3 of 0.821. Averagingacross all the products so that the grand mean repre-sents average deal responsiveness, one sees that own-ing a car and a home increased deal responsivenessfrom 20.5 to 34.4%, a 67.9% increase. Owning eithera car or a home, but not the other, increased deal

D,j = percentage of deal-oriented consumers for prod-uct category j and consumer characteristic i.

that the data contain some households that are commonto more than one product category.

'This constraint is the same as requiring Fip, = HT^ = 1.

IDENTIFYING THE DEAL PRONE SEGMENT 375

Table 3CAR AND HOME OWNERSHIP

Home ownershipand car ownership

No car and rentsNo car and owns

homeOwns car and rentsOwns car and home

Aluminumfoil

21.2%" (33)"

29.4 (17)37.5 (48)38.8 (103)

Waxedpaper

14.0 (43)

22.2 (27)11.1 (45)31.4 (103)

Product category

Headacheremedies

22.9% (48)

10.5 (19)22.4 (49)32.8 (131)

Liquiddetergent

28.6% (35)

25.0 (32)30.2 (63)41.5 (195)

Facialtissue

18.3% (60)

39.3 (28)27.3 (77)27.4 (201)

'The table entry is the percentage of aluminum foil buyers who do not own a car and who rent a home and are deal prone."Numbers in parentheses are total within-cell sample sizes on which each percentage is based.

Table 4CAR AND HOME OWNERSHIP

(Grand Mean = 24.9%)

Category Response

No car andrents

No car andowns home

Owns car andrents

Owns car andhome

.821

.932

.957

1.366

responsiveness from 20.5 to 26.2%. It is clear, there-fore, that owning both a car and a home greatlyincreases deal responsiveness, in comparison withnot owning either or owning only a car or only ahome.

Income

On theoretical grounds, household income levelshould be correlated negatively with deal proneness.Empirically, the opposite relationship may be observedbecause of the effects of confounding variables. Table5 gives the results of income for three income levelslow ($0-5,999), medium ($6,000-8,999), and high ($9,-000 or more). The income categories are based onroughly 33% groupings. The results indicate that,contrary to theoretical predictions, high incomehouseholds are more deal prone than low income

households. For every product category except facialtissue, a higher percentage of high income householdsare deal prone than low income households. The effectis not large in most categories, but it is persistent.

To isolate the effects of confounding variables,income was analyzed by adjusting first for homeownership and then for car ownership. (Because cellsizes became very small, it was impossible to analyzeincome simultaneously with home and car ownership).Table 6 and 7 present the results. Table 6 indicatesthat, except for liquid detergent, high income house-holds are not generally more deal prone than lowincome households. If anything, low income house-holds that own homes tend to be the most deal prone.Similar results are observed for car ownership. Table7 indicates that if one considers car owners only,high income households are not uniformly more dealprone than low income households.

Thus, when suitable adjustments are made for carand home ownership, higher income is not associatedwith increased deal proneness. Without these adjust-ments, the opposite conclusion would have beenreached. If the effects of car and home ownershipcould be simultaneously partialed out, one wouldexpect to find even stronger evidence that lowerincome households are more deal prone than higherincome households.

Time-Related ResourcesThe results for the two variables related to time

are presented in Tables 8 and 9. Table 8 shows that

Table 5INCOME

Incomelevel

$ 0-5,999$6,000-8,999$>9,000

Aluminumfoil

31.0% (84)"39.1 (64)35.8 (53)

Waxedpaper

19.6% (97)23.9 (71)26.9 (52)

Product category

Headacheremedies

28.0% (100)23.0 (87)31.1 (61)

Liquiddetergent

28.1% (114)36.8 (114)44.4 (99)

Facialtissue

31.4% (137)19.2 (125)29.5 (105)

"The table entry is the percentage of aluminum foil buyers whose income level is $0-5,999 and are deal prone."Numbers in parentheses are total within-cell sample sizes on which each percentage is based.

376 JOURNAL OF MARKETING RESEARCH, AUGUST 1978

Table 6HOME OWNERSHIP AND INCOME

Income level andhome ownership"

$0-5,999 andowns home

$0-5,999 and rents$6,000-8,999 and

owns home$6,000-8,999 and

rents$9,000 or more and

owns home

Aluminumfoil

37.1%" (35)"26.5 (49)

45.0 (40)

29.2 (24)

31.1 (45)

Waxedpaper

28.9% (38)13.6 (59)

30.2 (53)

5.6 (18)

29.3 (41)

Product category

Headacheremedies

40.9% (44)17.9 (56)

22.0 (59)

25.0 (28)

29.2 (48)

Liquiddetergent

31.8% (66)22.9 (48)

36.1 (83)

38.7 (31)

47.5 (80)

Facialtissue

33.9% (62)29.3 (75)

23.5 (85)

10.0 (40)

30.1 (83)

"Certain categories were omitted because the sample sizes were too small."The table entry is the percentage of aluminum foil buyers whose income level is $0-5,999 and who own a home and are deal prone.^Numbers in parentheses are total within-cell sample sizes on which each percentage is based.

Table 7CAR OWNERSHIP AND INCOME

Income leveland car

ownership"

$0-5,999 and nocar

$0-5,999 andowns car

$6,000-8,999and owns car

$9,000 or moreand owns car

A luminumfoil

21.6%" (37)'

38.3 (47)

37.5 (56)

39.6 (48)

Waxedpaper

17.6% (51)

21.7 (36)

23.3 (60)

27.3 (44)

Product category

Headacheremedies

20.0% (45)

35.2 (54)

25.0 (72)

31.5 (54)

Liquiddetergent

21.1% (38)

32.0 (75)

37.5 (96)

46.0 (87)

Facialtissue

25.9% (58)

35.4 (79)

19.0 (105)

29.8 (94)

"Certain categories were omitted because the sample sizes were too small."The table entry is the percentage of aluminum foil buyers whose income level is $0-5,999 and who do not own a car and are deal

prone."Numbers in parentheses are total within-cell sample sizes on which each percentage is based.

Table 8AGE OF CHILDREN

Age ofchildren

Childrenunder six

No childrenunder six

Aluminumfoil

41.5%" (41)"

36.0 (89)

Waxedpaper

21.7% (60)

25.3 (95)

Product category

Headacheremedies

26.6% (64)

33.7 (98)

Liquiddetergent

39.8% (83)

34.0 (141)

Facialtissue

24.0% (75)

28.1 (160)

"The table entry is the percentage of aluminum foil buyers who have children under six and are deal prone."Numbers in parentheses are total within-call sample sizes on which each percentage is based.

there is a small increase in deal proneness when thereare no children under six for three of the productcategories, waxed paper, headache remedies, andfacial tissue. For the other two categories, aluminumfoil and liquid detergent, the predictions are notsupported.

For the other time-related variable, housewife'semployment status, the prediction was that working

women should be less deal prone than nonworkingwomen. The data in Table 9 show that for all fiveproduct categories, working women are less deal pronethan nonworking women. A problem in analyzing theeffect of working women on deal proneness is thatincome interacts with the housewife's employmentstatus. For families in which the head has a low income,there is a higher likelihood of the housewife being

IDENTIFYING THE DEAL PRONE SEGMENT 377

Table 9HOUSEWIFE'S EMPLOYMENT STATUS

Employmentstatus

EmployedUnemployed

A luminumfoil

29.0%" (62)"38.3% (128)

Waxedpaper

17.7% (62)23.5% (153)

Product category

Headacheremedies

25.0% (76)28.6% (161)

Liquiddetergent

32.6% (92)37.4% (214)

Facialtissue

22.4% (116)29.2% (226)

"The table entry is the percentage of aluminum foil buyers who are employed housewives and are deal prone."Numbers in parentheses are total within-call sample sizes on which each percentage is based.

employed. Also, for larger families, the housewifemay need to work. Unfortunately, the sample sizeswere too small to analyze the effect of working womenwith income and family size held constant.

SUMMARY AND CONCLUSIONSA specific model of household purchasing behavior

is used to identify variables which should affect dealproneness. The essence of the model is that householdsand firms make the same kinds of inventory decisions.The variables that affect the household's purchasingbehavior are holding costs, stockout costs, transactioncosts, purchase price, and usage rates. Householdcharacteristics were linked to these cost variables,and predictions were made about which types ofhouseholds should be deal prone. The predictions thenwere tested empirically.

The empirical results showed that the householdresource variables, car and home ownership, werestrong predictors of deal proneness. Of the householdsthat owned a far and home, 34.4% were deal prone.In contrast, only 20.5% of the households that didnot own either a car or a home were deal prone.Time-related variables, age of youngest child andhousewife's employment status, also affected dealproneness but not as strongly as household resourcevariables. The effects of income also were analyzedand the results showed that upper income householdswere more deal prone. However, when income wasadjusted for household resources, this effect becamenegligible. The results are based on the analysis ofpurchasing data for five different frequently purchasedproducts. Therefore, it seems reasonable to concludethat the results can be generalized to a wide varietyof frequently purchased goods.

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