14

ArchG. WoodsideRobertLMMseaFred WritetsGakD.MuHer

Preference Segmentation ofHealth Care Services: TheOld-Fashioneds, ValueConscious, Affluents, andProfessional Want-It-Alls

The results of a national segmentationstudy are reported. The findings extendthe empirical work of Finn and Lamb andthe benefit-seeking conjectures by Kotlerand Clarke that consumers with prefer-ences toward specific hospitals can besegmented into a few distinct groups. Thegroups described in the findings are iden-tified as the value conscious, the afflu-ents, the old-fashioneds. and the profes-sional want-it-alls. Each segment has aunique demographic profile. The substan-tial importance of doctors' recommenda-tions in influencing hospital choice is sup-ported for all four consumer segments.Suggestions for additional research andhospital marketing strategies are pro-vided.

ArchG. Woodside(Ph.D, Pennsylvania State University) is the MalcolmS Woldenberg Professor of Marketing, FreemanSchool of Business, Tulane University, New OrleansHe IS a past president of Division 23, Consumer Psy-chology, Amencan Marketing Association.

Robert L.(MA, University of Nebraska) is a Vice President ofSFII Gallup and SRI Research Center, Inc, and Gen-eral Manager of the California office. Mr Nielsen isa health care marketing research consultant.

Fred MWtws(M.BA, Tulane University) is Marketing SerwcesManager at Barq's, Inc., in New Orleans. Mr WaltersIS responsi)b for marketing planning and evaluation

Gale D. Mutter(Ph.D, University of Nebraska) is a Vice President ofSRI Research Center, Inc, and SRI Gallup Dr Mulleris also a Senior Research Analyst with those organi-zations.

The managerial purpose of market seg-mentation is normally to develop alimited set of well-defined potentialcustomer groups, some of which arelikely to be responsive to an organiza-tion's product and service offerings.Faced with heterogeneous markets, anorganization following a market seg-mentation targeting strategy usually canincrease the expected profitability of itsmarketing activities through productofferings, pricing, advertising, and dis-tribution. This provides a major theo-retical rationale for the segmentationconcept (Frank, Massy, and Wind 1972;Wind 1978).

Market segmentation studies are beingused by some hospitals for their stra-tegic marketing plan. The findingsguide selection of specific market seg-ments for the long-term planning cycle(5 years), as well as selection of a mis-sion statement and an advertising themethat are distinctive from those of com-petitors, of interest to targeted mar-kets, and believable because the hos-pital does deliver the service claimed(cf. Clarke and Shyavitz 1981; Finn andLamb 1986; Mindak and Bybee 1971).

In the past, a common fallacy amonghealth care organizations has been thenotion that they are distinctive becauseof the high quality of their medical orclinical care. Virtually all missionstatements claimed to deliver the"highest quality" of medical (clinical)care regardless of age, income, and soon. This claim is clearly not true. Mostorganizations deliver reasonable qualitycare, but precious few are in the van-guard. In addition, if the vast majorityof health care organizations are claim-ing to deliver the highest quality med-ical care, the claim does not differen-tiate them firom all the other health careorganizations making the same claim.It is a rare health care organization thattruly derives its distinctiveness from thehigh quality of its medical care (Kotlerand Clarke 1987, p. 45).

What benefits do consumers seek fromhealth care organizations.'' Can con-sumers be segmented into benefitgroup>s? If so, do benefit groups havedistinctive demographic profiles andother identifiable characteristics thatmake them easier to reach? Tlie pri-mary purp>ose of the empirical study re-ported here was to answer these ques-tions.

Finn and Lamb (1986) have noted that,according to Haley (1968, p. 31), ben-efits sought by consumers may indicatetheir behavior much more accurately

than do demographic characteristics orvolume of consumption. Using a smallsample of former acute care inpatientsof one hospital, Finn and Lamb wereable to meaningfully cluster 111 re-spondents, of 182 who had returnedusable questionnaires, into four dis-tinct benefit segments. The segments

. . . did not diEfer meaningfully orstatistically on demographic or usagecharacteristics. Therefore, we doubt thata demographic segmentation study wouldhave produced meaningful results" (p.30). However, as Finn and Lamb noted,their results are limited to the formerpatients of only one hospital and can begeneralized only to the acute care pa-tients of that institution during the 5-month period included in the sampleframe. They suggested that a cross-sec-tional study across institutions is neededon hospital benefit segmentation.

Kotler and Clarke (1987) suggest thatmost consumers of health care servicescan be grouped into one of the follow-ing four core benefit-seeking segments.

Quality buyen seek out the bestproduct and are not concernedwith the cost. A quality seekerin the hospital market mightconsider only teaching hospi-tals or the best medical center.Service buyers look for the bestpersonal and nursing care andassume that all medical care isadequate. A service seekermight choose a communityhospital with nice amenities andan empathetic nursing staff.Value buyers look for the bestvalue for the money and expectthe service to match the price.A value buyer might go to adentist who is reasonably pricedand who has a reasonably goodreputation.Economy buyers are primarilyinterested in minimizing costand favor the least expensivemarket offer. Many retail den-tal chains, offering dentalcleaning for $9, are app>ealing

to this segment (Kotler andClarke 1987, p. 244).

Kotler and Clarke emphasize that forbenefit segmentation to work best,consumers segmented by benefits soughtshould differ substantially by demo-graphic or other identifiable segmen-tation variables that would make thebenefit segments easier to reach effi-ciently (p. 247).

Heretofore, though specific empiricalmodels of consumer preferences for al-ternative designs of health care serviceshave been tested (e.g., Berkowitz andFlexner 1980; Carroll and Gagon 1983;Hauser and Urban 1977; Wind andSpitz 1976), empirical evidence of ben-efit segmentation applied to large-scalepopulations of consumers has beenlacking. Kotler and Clarke do not citeany empirical studies in support of theirfour-group benefit segment proposi-tion.

We report the results of a national healthservices benefit segmentation study. Thefindings support the basic propositionthat most Americans (of those who dohave a hospital preference) can begrouped into one of four benefit seg-ments by their preferences for hospitalservices. Each of the four segments hasa unique demographic profile that fa-cilitates reaching each benefit segmentefficiently.

RESEARCH DESIGN

A telephone questionnaire was de-signed to elicit the factors that mightaffect consumers' preferences for hos-pitals for medical service requiring atleast an overnight stay in a hospital (i.e.,inpatient care). A total of 18 possiblereasons for reported hospital preferencewere included in the survey. They wereselected ftom 220 items developjed frompretests and hospital preference studiescompleted in 22 U.S. metropolitan areasin 15 states during 1984 and 1985 bythe SRI/Gallup Hospital Monitor Staff,

The OU^tMoneik, Value Cormaous,

Lincoln, Nebraska. Each of the 18 itemswas found to load substantially (factorscores >.75) in factor analyses wherethe principal rotated factors had eigen-values greater than 1.0 and 5% or moreof the total variance was explained byeach factor. No more than two itemsthat loaded heavily on any one factorwere included in the study.

Question items similar to the descrip-tions of the four core benefit segmentsidentified by Kotler and Clarke (1987)were used in the study, as well as ad-ditional questions thought to relate toother benefits sought by hospital p>a-tients. The additional questions weredeveloped from statements mentionedoften in open-ended responses duringpretests and from previous hospitalstudies completed by SRI ResearchCenter, Inc.

Questions related to a quality prefer-ence included the following items.

The hospital has a reputationfor having the best and latestequipment.

The hospital is a teaching hos-pital or affiliated with a med-ical school.

The hospital has the best doc-tors in the area.

Questions related to a service prefer-ence included the following items.

The hospital has a reputationfor being caring and concernedabout its p>atients.

The hospital has warm andfriendly staff members.

You know the hospital has thebest nurses.

Items believed to relate to preferencesfor value received from the hospital in-cluded:

The hospital has a good repu-tation for treating your condi-tion or illness.

You or someone in your house-hold has been to the hospital

16

before as a patient and had agood experience.

These question items may not matchclosely Kotler and Clarke's (1987) def-inition of value benefits because theconcept of reasonable price for the ser-vice received is not included.

One item among the 18 questions re-lated to a preference for economy:

The hospital charges lowerprices than other hospitals dofor the same service.

Other items were used to learn the im-portance of recommendations of familydoctors, location convenience, wellnessprograms, emergency care reputation,religious affiliation, and physical facil-ities (new and modern building) as rea-sons for preferences toward hospitals.

Respondents were asked to indicatewhether each of the 18 preference fac-tors was very, somewhat, or not im-portant in describing why they preferthe hospital named. The respondentsalso could indicate that they were un-certain about the importance they wouldassign to the preference factor.

The following demographic informa-tion was collected in the study: sex, age,race, state residence, income, educa-tion, and occupation. 'The respondentswere asked to identify their age ac-cording to one of five groupings: 18 -24, 25-44, 45-54, and 65 + . Four racecategories were used: white, black,Hisp>anic, and Asian/native residents.Five income levels were used in $10,000increments up to $40,000 + , with thefive categories including "do not know"and "no answer." Education includedsix levels, from less than 12th grade topostgraduate. Ten occupation cate-gories were used (e.g., professional/managerial, secretarial/clerical, house-wife, military, student).

Before being asked the hospital pref-erence questions, the respondents wereasked:

If you had a medical condition that re-quired at least an overnight stay in a hos-pital, which hospital in your area wouldyou prefer to be in?

Only adult respondents who couldidentify a specific hospital by name wereasked to respond to the hospital pref-erence questions.

Sample and Procedure

A quota sampling design was used toensure that 2000 respondents wouldparticipate and that the respondentswould match the demographic andgeographic profile of Americans livingin the contiguous 48 states and theDistrict of Columbia. Five to 10 met-ropolitan areas were included from eachstate to ensure that the majority of eachstate's residents had a possibility of beingincluded in the sample. Respondentsand geographic areas were selected forthe study in proportion to representa-tive shares of state population and de-mographic characteristics. Similarly, thenumber of telephone numbers (by pre-fix) was selected to be representative ofthe working numbers using the tele-phone exchange in proportion to otherprefix listings in the areas sampled.

Telephone three-digit prefixes were se-lected in proportion to their use in lo-cal areas. Random digit dialing of theremaining four digits was used to en-sure that unlisted numbers were in-cluded in the sample. Up to three call-backs were placed in an attempt to reachnonrespondents.

Twelve professional, full-time tele-phone interviewers placed all telephonecalls for the study. The calls were madefrom SRI Research Center, Inc.'s Lin-coln, Nebraska, offices during Junethrough August 1985.

Response

The cooperation rate in completing thesurvey was 92% among the householdscontacted. TTie profiles of the respon-

dents were very similar to the demo-graphic and geographic (region andstate) distributions of U.S. residents,with the exception of race. Lower pro-portions of black and His|>anic p>ersonswere included among the sampled re-spondents (7% and 2%, respectively)than are present among U.S. residents.

Data Analysis

Cluster analysis was used to classify therespondents who preferred hospitals forinpatient care. 'The groups were basedon the reasons given for their hospitalpreferences. The demographic profilesof the grouped respondents were com-p>ared to leam whether several of thegroups could be identified efficiently.Though some theoretical basis has beensuggested for using a four-group clas-sification, several marketing scholars(see, e.g., Punj and Stewart 1983; Wells1975) point out that the clustering ofail observations into a few groups maynot be a good practice. "Rather theidentification and elimination of out-liers or the use of a decision rule to stopclustering short of the inclusion of allobservations is probably advantageous"(Punj and Stewart 1983, p. 143-4).

Cluster analysis is a hraily of empiricalmethods (see Punj and Stewart 1983for a review of clustering methods) in-volving no prior assumptions aboutimportant differences within a popu-lation. However, theoretical and "real-world" rationales have been developedfor grouping market segments via clus-tering routines (see, e.g., Schaninger,Lessig, and Panton 1980; Wind 1978).We adopted the practical viewpoint ex-pressed by Wells (1975) that clusteranalysis of respondents should result invery different, homogeneous groups.

Quick Cluster (SPSS 1986) was usedfor grouping the respondents by theiranswers to the iKispital preference items.Quick Cluster is a minimum varianceclustering method; the squared Euclid-ean distances between all clusteringvariables are minimized. Quick Cluster

2 (Jme W8U

nwas selected because the method is de-signed for efficient clustering of a largenumber of cases (n > 1000) by findingcluster centers based on the values ofthe cluster variables and by assigningcases to the centers that are nearest.

Because individuals may perceive scalesof imponance differently, each individ-ual's rating was normalized. For eachindividual, preference responses wereaveraged across all 18 items. Then eachitem rating was subtracted from thismean and a constant was added to thedifference. This data transformationcorrected for the fact that some respon-dents rated most preference items as veryimportant (especially low income re-spondents) and others (high income re-spondents, in particular) assigned thevery important rating to only a fewpreference items. However, normali-zation of the data changed the clusterassignment for only a few respondentswhen the clusters based on the nor-malized data were compared with thosebased on the raw dataa finding re-ported ofren in the clustering literature(Punj and Stewart 1983).

Missing values were less than 3% of thedata for the preference questions. Theaverage value for the total respondentswas used for the missing values. Beforethe data were normalized, two scoringmethods were examined. In the firstmethod, "very important" was scored3, "somewhat imponant" was scored2, "not important" was scored 1, and"uncertain" responses were convertedto the average score of the respondentswho did give a degree of importancerespK)nse. In the second method, the"uncertain" responses were scored as 1.5to reflect the idea that uncertainty ofthe importance of a particular reasonindicates an importance level between"not" and "somewhat" important.

The resulting cluster assignments fortlK two methods were very similar. Inthe following section we report the av-ctage responses for the preference itemswith "uncertainty" scored as 1.5 before

normalizing. Cross-tabulations of therespondents grouped by cluster analysisand their raw scores on the preferenceitems also were run and are reported forsome of the preference items in the fol-lowing section.

FINDINGS

A total of 68% of the 2000 respon-dents identified a hospital by name asbeing the one preferred for overnightinpatient care. A total of 91% of therespondents naming a preferred hospi-tal responded to all 18 questions aboutthe reasons for their preferences.

Naming a preferred hospital for inpa-tient care varies by sex and region ofthe country. Seventy-three percent ofthe female and 63% of the male re-spondents reported a preferred hospi-tal. Greater proportions of respondentsliving in the Northeast and NorthCentral regions named a preferred hos-pital in comparison with respondentsliving in the West. However, the ma-jority of residents in each region of thecountry surveyed did name a preferredhospital. Women may be more in-volved in making medical decisions forthe entire family than are men. Thehigher average age of U.S. residents inthe Northeast and North Central re-gions may be associated with moremedical experiences and formation ofhospital preferences in these regions thanin the West.

The proportions of respondents naminga preferred hospital do not vary by in-come, occupation, education level, orrace. However, these demographicvariables are associated substantially withthe respondents clustered by reasons forhospital preferences. Sex and region arenot associated with the respondentgroupings.

Quster analyses specifying 3, 4, 8, 12,and 16 groups were run on the pref-erence dam. A total of 89% (n = 1114)of the respondents reporting reasons fortheir hospital preferences were assigned

to four groups within the 8-group solu-tion. Instead of forcing all the respon-dents into one of four groups, we lim-ited our examination of the preferencesand demographic variables describingthe respondents to the four substaintialgroups among the 8-cluster solution.In real life, some individuals are un-likely to fit a profile that is similar toenough to that of other individuals toform a meaningful market segment.Punj and Stewart (1983) suggest thatin performing cluster analysis research-ers should not attempt to include allrespondents in a few substantial groups.Such a procedure of not including allrespondents in benefit segments wasfollowed by Finn and Lamb (1986).

The cross-validation procedure for clus-ter analysis recommended by Punj andStewart (1983) was run twice on twohalves of the total sample. The respon-dents were split into two equal groups.Cluster analysis, with an 8-group so-lution, was carried out on one of thetwo groups. The respondents in theholdout sample were assigned to one ofthe eight clusters on the basis of thesmallest Euclidean distance to a clustercentroid vector. The same method wasused in reverse; cluster analysis was runon the original holdout sample and theother half of the respondents were as-signed on the basis of the similarity oftheir responses to the cluster centroidvectors. The results of both cross-vali-dations classified most (more than 86%)respondents into one of four groups. Thecoefficients of agreement (kapp>a) weresubstantial and both solutiotis werejudged to be stable. The four largestclusters were very similar in size andresponse patterns (distributions of itemresponses) when the two solutions werecompared. Hence the two data sets werecombined and the findings reported arebased on the total sample of respon-dents.

The Four Hospital Preference Segments

The four groups differ substantially bytheir distributions and average re-

The OUfaMonads, Value Camm

sponses to the reasons for their hospitalpreferences, as would be expected fromcluster analysis. 'The substantive find-ings are that four major clusters wereformed and the descriptions of theclusters may be useful for hospital mar-keting managers.

Names felt to reflect the demographiccharacteristics and reasons for hospitalpreferences were assigned for the fourgroups: "old-fashioned," "affluent,""value conscious," and "professionalwant-it-alls." The four groups are 8,35, 22, and 24%, respectively, of the1246 persons in the sample who hadhospital preferences and responded tothe 18 preference items.

Though the groupings differ signifi-cantly (by cross-tabs and F-tests) on allhospital preference items, the degreesof association (omega squared, O) , Hays1985) differ substantially among thepreference items. Omega squared is ameasure of association between twoconstructs that is calculated after anal-ysis of variance. Analogous to r , o) isthe level of variance in a dependentvariable explained by an independentvariable (Hays 1985; Sawyer and Peter1983).

The Value Conscious Segment

The value conscious group includes asubstantial share of the respondents whoanswered the preference items (22%).This group is the only one in which asubstantial majority (70%) felt it wasvery important for their preferred hos-pital to charge lower prices than otherhospitals do for the same services. Thispreference question was associated (o)= .26) with group membership morethan any other reason for hospital pref-erence. Both cross-tab and ANOVA re-sults are reported in Table 1. Note thatonly 22% of the affluents and profes-sional want-it-alls and 50% of the old-fashioned felt that this "lower price forthe same service" was very important.Clearly, one group in particular is con-

TABLE 1The Hospital Charges Lower PricesDo for the Same Services

Segment

Old fashionedAffluentValue consciousProfessional

want-it-alls

Total

SegmentOld fiashionedAffluentValue consciousProfessional

want-it-alls

Total

VeryImportant

(%)

502270

22

36

X' = 247.

X

2.72.33.2

2.3

2.6

SomewhatImportant

(%)

173016

2524

91, 9 d . f , p

Than Other Hospitals

' = 324

2.0 .66

(35%). The affluents are most similarto the professional want-it-alls in theirreasons for preferring sp)ecific hospitalsand in their demographic characteris-tics.

A greater share (68%) of the affluentsthan of the other three segments re-ported that a very impyortant reason fortheir hospital preference was "the hos-pital is a wellness and health center,not just a place to go when you aresick." The association of wellness andhealth center with the four hospital

preference segments is moderate (co^ =.08; see Table 6).

A total of 92% of the affluents reportedthat a very important reason for theirhospital preference was that their pre-ferred hospital "has a reputation of beingcaring and concerned about its pa-tients." In comparison, only 71% ofthe old-fashioneds reported this reasonas being very important, but about thesame percentage of the value conscious(91%) and professional want-it-alls(89%) considered it very important.

TABLE 5Four Hospital

Segment

Old fashionedAffluentValue consciousProfessional

want-it-alls

Total

Preference

18-24(%)

11616

9

13

Segments

25-34 35-44(%)

172925

23

25

= 36.

(%)181618

16

17

J4, 15 d.f., p

by Age

45-54(%)

131515

16

15

< .0016

55-64(%)

211211

17

14

65 +(%)

241215

19

16

SampleNumber

95438277

304

1114

The afHuents least often (13%) re-ported that a teaching hospital or ahospital affiliated with a medical schoolwas a very important reason for theirpreferences. A substantial share (44%)of old-^hioneds cited this reason as veryimportant (see Table 7).

Few affluents (22%) reported lowerprices for the same services as a veryimportant reason for their hospitalpreferences. Their distribution of re-sponses and mean response to this itemare similar to those of the professionalwant-it-alls (see Table 1).

Demographic profile of the affluent seg-ment. A greater share (29%) of the af-fluents than of the other three seg-ments reported household income above$40,000 (see Table 3). Affluents morecommonly have education beyond highschool than do the old-fashioneds andthe value conscious. The distributionsof the affluents and professional want-it-alls by education are very similar. 'Theresults for education are reported inTable 8.

Note in Table 5 that the affluents areyoungest, with 45% younger than 35years and only 24% older than 55 years.The affluents have the lowest share(18%) of retired/disabled p)ersons as anoccup>ational category and the same shareof professionals/managers (27%) as theprofessional want-it-alls (see Table 2).The affluent segment particularly canbe described as young and upscale inincome, education, and occupationalstatus.

The Professional Want-lt-All Segment

The professional want-it-alls are thesecond largest segment of respondentswith hospital preferences (24%). As thename implies, this segment identifiedmore reasons as being very importantfor their hospital preference than anyother group. More than 70% of theprofessional want-it-alls reported thefollowing reasons for their hospitalpreferences as very important.

JMCK VoL 8, No. 2Uime 1988}

21

I TABLE 6The Hospital Is a WellnessPlace to Go When You Are

Segment

Old fashionedAffluentValue consciousProfessional

want-it-alls

Total

Segment

Old fashionedAffluentValue consciousProfessional

want-it-alls

Total

and Health Center,Sick

Very SomewhatImporrant Important

(%)

556843

41

53

X' = 10961

X

2.83.22.8

2 7

2.9

(%)202535

32

29

9 d.f.. P atient and had agood experience.You know the hospital has thebest nurses.

The hospital has a reputationof being caring and concernedabout its patients.

The hospital has a reputationof having the best and latestequipment.

TABLE 7The Hospital IsMedical School

Segment

Old ^hionedAffluentValue consciousProfessional

want-it-alls

Total

S^ment

Old fashionedAffluentValue consciousProfessional

want-it-alls

Total

a Teaching Hospital

VeryImportant

(%)

41334

27

25

X" = 121

X

2 62 22.6

2.4

2.4

SomewhatImportant

(%)

163431

2930

55, 9 d.f., p o NotI Know

22

TABLE 8Four Hospital

Segment

Old fashionedAffluentValue consciousProfessional

want-it-alls

Total

Preference Segments by

Less Than12th Grade

(%)

309

12

11

12

12thGrade

{%)

463640

3738

Education

Trade/BusinessSchool

(%)

055

4

4

K' = 49.24, 15 d.f., p a-tient medical care is likely to affect thefuture well-being of the patient, thequality of the service received may bea very important concern for nearly allconsumers. A market concerned exclu-sively with economy therefore may notmeet the substantiality requirement forhospital market segmentation (Kotlerand Clarke 1987, p. 251). Some em-pirical evidence is needed to examinethese issues.

The two segments likely to be mostprofitable for designing new hospitalmedical products (services) are the af-fluents and the professional want-it-alls,given that members of both segmentstend to be upscale in income and lessconcerned about lower prices than theother segments. The affluents, in par-ticular, are most likely to be influencedby services designed to maintain andimprove their physical well-being (e.g.,offering wellness and health centers).The professional want-it-alls are mostlikely to be influenced by satellitemedical clinics located close to theirhomes and sponsored by a hospital with

The

24

the best reputation for quality of doc-tors, nurses, and equipment. AltonOchsner Medical Foundation, a leadinghospital in the New Orleans, LA, met-ropolitan area, started using this strat-egy in 1986 to reach households inupscale neighborhoods that were 10 to20 miles away.

Given the substantial importance ofdoctors' recommendations in the hos-pital selection decision found for all fourmarket segments in the study, an in-dustrial marketing analogue may bemore appropriate for designing hospitalmarketing strategies than a packagedgoods analogue, especially for smallhospitals with limited marketing bud-gets. An industrial marketing analogueincludes the recommendation of em-phasizing the development of relation-ships with distributors and other firmsin the relevant marketing channel (e.g.,doctors) and not embracing packagedgood advertising techniques (Mindak1986).

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