botschen thelen pieters - using means-end structures for benefit segmentation

EuropeanJournal ofMarketing33,1/2

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Using means-end structuresfor benefit segmentation

An application to servicesGünther Botschen

Marketing and Law Group, Aston University, Birmingham, U KEva M. Thelen

Department of Marketing, University of Innsbruck, Austr ia, andRik Pieters

Department of Business Administration, T ilburg University,The Netherlands

Keywords Benefit segmentation, Consumer behaviour, Image, Market segmentation, Product attr ibutes, Services marketing

Abstract A lthough the basic idea of benefit segmentation l ies in using causa l, as opposed todescr iptive, factors as segmentation cr iter ia , most of the empir ical studies do not differentiatebetween product attr ibutes and the benefit sought by consumers. The objectives of this article areto clar ify the distinction between attr ibutes and benefits sought, and to apply a modified ladder ingtechnique, based on means-end theory to use the el icited benefits to form benefit segments. Acompar ison wi th a t tr ibute-based segments demonstra tes tha t means-end cha ins provide apowerful tool for “true” benefit segmentation.

IntroductionHaley (1968) and Wind (1973) proposed the segmentation of markets on thebasis of benefits sought by identifiable groups of consumers. Whilepsychographic and general attitudinal approaches to segmentation may workwell statistically they are less helpful when it comes to deriving effectivemarketing strategies (Young et al., 1978). Therefore, benefit segmentation hasbecome the preferred technique for successful product positioning, new productintroduction, pricing, advertising, and distribution (Wind, 1978).

Most of the published segmentation studies follow a similar approach: afteranalyzing secondary data and/or conducting one-on-one in-depth or focusgroup interviews to identify relevant attributes and benefits a measure ofimportance of attributes/benefits is developed (Haley, 1968; Moriatory andReibstein, 1986; Mühlbacher and Botschen, 1988). This instrument is pre-testedand the data collection starts. Generally, responses are given on a scalerepresenting low to high importance and/or variability. Data analysis beginswith data treatment (e.g. normalization, standardization) and frequentlycontinues with factor and cluster analysis to identify benefit segments.

European Journal of Marketing,Vol. 33 No. 1/2, 1999, pp. 38-58,© MCB University Press, 0309-0566

Received August 1996Revised August 1997

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Although the basic idea of benefit segmentation lies in using causal, asopposed to descriptive, factors as segmentation criteria, most of the empiricalstudies do not differentiate between product attributes and the benefits soughtby consumers (Haley, 1968; Mathews and Watt, 1986; O’Connor and Sullivan,1995). According to the means-end chain theory of cognitive structures,however, consumer behavior is driven by the “true” benefits sought, whichcause the desire or preference for certain attributes. Drawing direct conclusionsfrom preferred attributes on future purchase behavior without clearlydistinguishing them from the underlying benefits seems, therefore, problematic.Indeed, means-end chain theory has been proposed as ideally suitable for thedevelopment of segments (Aurifeille and Valette-Florence, 1992; Reynolds andGutman, 1988). To the best of our knowledge, published studies applyingmeans-end chain theory for benefit segmentation are absent, though. In thispaper we will elaborate on the distinction between product attributes andbenefits sought, and we will show that it is important to segment markets onthe benefit-level.

A second criticism concerning the standard benefit segmentationapproaches deals with the way the list of attributes/benefits is used. In mostcases, not all attributes/benefits apply to all respondents. Wilkie and Weinreich(1972) concluded in their investigation that results improve when differences inthe number and type of attributes/benefits used are allowed. They suggestedthe use of “determinant attributes” criteria (Myers and Alpert, 1968) to selectattributes/benefits (O’Connor and Sullivan, 1995). Although such an approachis an improvement, we argue that individual free elicitation of attributes and ofthe underlying benefits sought offers a better basis to develop benefit segments.These types of benefit segments should be able to more easily forecastpurchasing behavior.

The objectives of our study are to clarify the distinction between attributesand benefits sought, to apply a modified laddering technique, based on means-end theory which allows free elicitation of attributes and underlying benefits,and to use the elicited benefits to form benefit segments.

Theoretical backgroundThe belief underlying benefit segmentation is that the benefits which people areseeking in consuming a given product are the basic reasons for the existence of“true” market segments (Haley, 1968). Benefit segmentation can, therefore, beregarded as an approach to market segmentation which identifies marketsegments by causal factors rather than descriptive factors.

Although there is strong agreement that benefit segmentation may betterexplain purchasing behavior than traditional methods of segmentation (Kotler,1991) we perceive a lack of clarity as to what should be considered a benefit. Aslong as benefits equate to the product’s or service’s more abstract qualities(Haley, 1968; Johnson, 1989) the concluded relationship to purchasing behaviorcontains a certain risk. If the focus is on the level of preferred attributes we donot identify underlying benefits sought by customers. Means-end chain theory


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may give us some guidelines to further elaborate on the distinction betweenattributes and benefits sought and to determine the appropriate level ofsegmentation.

Means-end theoryBasis to distinguish between attr ibutes and benefitsMeans-end theory (Gutman, 1982; Olson and Reynolds, 1983) offers a practicalmethaphor to assess consumers’ product, service or behavior knowledge andmeaning structures. The representation of cognitive structures in memoryadvocated by the theory is based on the acknowledgement that product, service,or behavior may be linked to self. The central tenet of the theory is that product,service or behavior meaning structures stored in memory consist of a chain ofhierarchically-related elements. The chain starts with the product, service orbehavior components (attributes) and establishes a sequence of links with theself concept (personal values) through the perceived consequences or benefitsproduced by certain attributes of the product, service or behavior. This forms a“means-end chain” in that attributes are the means by which the product,service or behavior provides the desired consequences or values, i.e. the ends.Values are the ultimate source of choice criteria that drive buying behavior(Claeys et a l., 1995). This exemplifies a basic assumption of the means-endchain approach (Peter and Olson, 1987) and of the marketing concept in general(Kotler, 1991), that products, services, or behavior are bought for what they dofor the consumer.

For a finer-grained analysis of the mental representations each basic level ofabstraction can be divided into sub-levels, leading to distinct categories ofabstraction: concrete attributes, abstract attributes, functional consequences,psychological consequences, instrumental values, and terminal values (Olsonand Reynolds, 1983).

Young and Feigin (1975) present a benefit chain analysis which linksemotional or psychological benefits to product claims or product attributes. Abenefit chain begins with a product description including a specific attribute.The consumer provides two benefits that are derived from this attribute andtwo benefits derived from each of the two initial benefits. The process isrepeated once more, yielding 14 benefits in all. The whole process is categorizedinto the following steps:

Step 1: product specific attributeleads to

Step 2: functional benefitleads to

Step 3: practical benefitleads to

Step 4: emotional pay-off.

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Gutman (1982) defines functional and practical benefits and emotional pay-offas types of consequence. If we consider the two approaches and followGutman’s (1982) definition of benefits and consequences we propose thefollowing distinction between attributes and benefits sought by consumers.Attributes are characteristics of products, services or behavior which may bepreferred or sought by consumers. These attributes do not explain per se forwhat reasons the product, service or behavior is or might be bought. Accordingto means-end theory we can distinguish between two levels of attributes:concrete and abstract attributes, e.g. “price” and “good quality”. In both caseswe receive additional information about the product, the service or the behavioritself but we do not discover any underlying reasons why the product is chosenand/or bought. Most of the companies prefer to work with attribute-basedsegmentation which they consider more operational and more easy to act onthan on benefits sought.

Benefits which people are seeking in consuming a given product, service orbehavior offering various attributes explain why people are looking for certainattributes. Again, according to means-end theory we can differentiate betweenconsequences on the functional level, e.g. “I can easily handle it”, andconsequences on the psychosocial level, e.g. “others regard me as being special”(Olson, 1989; Olson and Reynolds, 1983; Peter and Olson, 1987). Both are linkedto attributes sought and personal values, which are divided into instrumentaland terminal values. From the customer’s point of view it is not the product’sattributes which count, but the problem solution – the benefit sought – whichthey derive from a certain combination of attributes.

We will use this definition of benefits when doing benefit segmentation. Webelieve that much of the conducted empirical studies to determine benefitsegments do not focus on benefits as described above. Instead, they focus onconcrete and abstract attributes and sometimes on a combination of attributesand benefits or consequences, respectively.

Let us give some examples. If during the purchasing process of dataterminals “reliability” (abstract attribute) is the sixth important attribute(Moriatory and Reibstein, 1986) this does not equal the benefit sought by thecustomer. Underlying benefits may be: “no disturbance of data processing”,“our investment pays back better”, “optimal customer support”, or “employeesare seldom annoyed”. If customers of analgesic brands consider package size(concrete attribute) important (O’Connor and Sullivan, 1995), the benefitsderived from different package size can range from “fits perfectly in my pocket”to “easy to carry with me”, and “lasts for more than one month”. If customers ofholiday destinations perceive “sunny weather” (concrete attribute) as animportant attribute (Mühlbacher and Botschen, 1988) they may desire “sunnyweather” because they are seeking benefits like “get a wonderful suntan”,“allows us to do open air sports”, or “children may play games the whole dayoutside”. Even concerning the example in Haley’s (1968) landmark article“Benefit segmentation: a decision-oriented research tool”, we would claim that


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“flavor and product appearance” as well as “price” are representing concreteattributes and not benefits sought by the customers.

In summary, benefit segmentation studies frequently treat attributes asbenefits. As a result, benefits as the reason for attribute preferences are oftenoverlooked because the same attribute may lead to different benefits, a case ofmultifinality (Pieters, 1993), and because a single benefit may be based onmultiple attributes, a case of equifinality (Pieters, 1993), segments based onattributes may differ from segments based on benefits sought, and benefitsegmentation which is actually based on attributes instead of on actual benefitssoughts may be grossly misleading.

In this study we will examine whether in fact the same attribute can lead todifferent benefits sought and whether a single benefit can be based on multipleattributes and what the consequences are for segmentation.

Segmentation on means-end chainsIn response to the identified problems and opportunities for findingsegmentation variables which determine the behavior of consumers moreaccurately, market segments could be based on the specific meanings and/orlinkages between meanings contained in means-end chains. Essentially, this issimilar to other approaches to segmentation based on consumers’ perceptualstructures, except that the means-end chain analysis is not restricted to a singlelevel of meaning or linkages.

Figure 1 gives an overview about segmentation types according to differentlevels within means-end chains.

Segments of type A can be built on the attribute level consisting of concreteand/or abstract attributes, on the benefit-level consisting of functional and/or

Figure 1.Segmentation typesbased on means-endchains

Self-esteem

Levels ofMeans-

EndChains

ConcreteAttributes

AbstractAttributes

FunctionalConsequences

Psycho-socialConsequences

InstrumentalValues

TerminalValues

ExampleMotor-Cycle

TriumphSpeed Triple

900

Price

98 H.P.

GoodQuality

Powerfulengine

Can easilyhandle it

Can perfectlyaccelerate

Others see meas special

Feel verystrong

Being centerof attention

Typeof

Segmen-tation

Attribute based “True” benefit based Value based

Linkages based

Linkages based

Entire means-end chain(s) based

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psychological consequences, and on the value level consisting of instrumentaland terminal values. Type B of a segmentation approach focuses on linkagesbetween attributes and consequences, linkages between consequences andvalues, and linkages between attributes and values. This type of segmentationwould cluster customers according to the similarity of parts of their perceptualstructure. For instance, one could group together all consumers for whom sugarin a candy-case leads to energy which provides a strong feeling, and allconsumers for whom sugar provides a good taste. Finally, type C wouldsegment on the basis of the entire means-end chain(s). Consumers who shareone or several means-end chain(s) might be grouped together and treated as asegment (Olson, 1989).

The selection of the appropriate segmentation type will depend on the basicmotivations of the investigation, e.g. understanding markets, productpositioning or new product introduction (Wind, 1978), on the product, service orbehavior under consideration and, of course, on the requirements for effectivesegmentation such as measurability, substantiality, accessibility, actionability,and the relevance of variables for selection and purchase intention (Kotler,1991).

Since consequences/benefits are the central reason why consumers choose aproduct or service (Gutman, 1991), segmentation on the “true” benefit-levelseems to be very promising. Therefore, we will focus on this level ofsegmentation.

Conducting tailor-made interviewsTraditionally, benefit segmentation procedures – analyzing secondary data,identification of attributes/benefits via in-depth interviews or focus groups,generation of item-pool, pretest, data collection via rating scale representinghigh importance – bear the following risks:

• several attributes/benefits may not be relevant for the respondent, butdue to the fact that all of them are presented he/she is forced to evaluatethem all;

• respondents tend to rate any attribute/benefit sought relatively high onthe corresponding rating scale even those which are not relevant;

• some important attributes/benefits might be overlooked in the in-depthor focus-groups interviews;

• depending on the amount of items respondents tend to looseconcentration.

As a consequence segments derived from traditional rating data will onlyslightly differ with respect to preferred attributes due to the fact thatrespondents tend to rate most of the items as important. We expect that clustersolutions using laddering data with free elicitation of attributes and benefits arebetter than solutions based on rating data.


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By applying the laddering technique, the most common method to establishmeans-end chains, some of the mentioned problems can be reduced or avoided.Laddering refers to an in-depth, one-on-one interviewing technique used todevelop an understanding of how consumers translate the attributes ofproducts, service or behavior into meaningful associations with respect to self,following means-end theory (Reynolds and Gutman, 1988). This is achieved bydirect probing with the typical question: “Why is that important to you?” withthe objective of finding connections between a range of attributes,consequences, and values.

Common criticism of the means-end chain approach claims that by askingthe “why?” question artificial abstract levels may occur because respondentsthink in a more strategic way and are trying harder to find arguments for theirbuying behavior as they usually do (Grunert and Grunert, 1995; Grunert et al.,1995). To avoid this potential problem we have chosen an efficient paper andpencil laddering technique with the added advantages of free elicitation ofimportant attributes (Walker and Olson, 1991) and the lack of interviewer bias.

Empirical studyObject of the studyObjects of the study were the expectations of customers toward the quality ofsales-personnel in speciality shops for women’s and men’s clothing. Empiricalstudies on service quality emphasize the importance of high quality sales-personnel in the encounter situation (Berry and Parasuraman, 1991; Crosby,1991; Payne, 1993). The behavior of the sales personnel offers a possibility tosuccessfully differentiate from competitors and to increase customersatisfaction (Bateson, 1989; Hensel, 1990, Mairamhof, 1996; Meyer andMattmüller, 1987). The detailed knowledge of customers’ expectations andunderlying motives concerning sales personnel behavior may support effectiverecruiting and training of personnel (Heskett et al., 1990). Speciality clothingshops were chosen because most of the consumers are familiar with them andthere the quality of sales personnel is extremely important.

SampleThe population for the study was the 250 graduate students who specialized inmarketing or retailing. Students were asked to complete a questionnaire inclass.

QuestionnaireFirst, respondents were asked to describe in detail one of their recent clothingpurchases. They indicated when they had bought this clothing item, what theclothing item was, why, for whom, and where they bought it, how often theypreviously purchased at this shop, how much money and time they spent on itand if they had clear expectations concerning the type of clothing item.Respondents were asked to keep this clothing purchase in mind whenanswering the specific laddering and rating questions.

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Then the questionnaire contained a paper-and-pencil laddering interviewthat had been applied in previous research (Pieters et a l., 1995; Walker andOlson, 1991). In the paper-and-pencil version used, laddering was accomplishedthrough a structured questionnaire in which subjects were asked to indicate“What characteristic features should sales personnel have and how should theybehave?” Subjects could give up to four characteristics and behavioralexpectations that were important to them in the specified buying situation, andfor each mentioned attribute they were asked three times why the given answerwas important to them. In the questionnaire there were four sequences of fourboxes connected by arrows. The first box in each sequence was labeled “I wantthe sales personnel to be … or to behave …” (2, 3 or 4, as applicable), andthe consecutive columns of boxes were labeled “… this is important to me,because …” (2 or 3, as applicable). Subjects were instructed that in case theycould not think of a reason why something was important to them beyond whatthey had already indicated, they could leave the respective column empty(Pieters et al., 1994).

After completing the paper-and-pencil laddering interview the questionnairecontained 17 items expressing desired characteristics and kinds of behavior ofsales personnel, like “I want salespeople to be honest” or “I want salespeople’sadvice to be more than just information about materials and design”. Theseitems had to be rated on a five-point scale ranging from “I expect very much” to“I would never expect”. The scale was developed according to the traditionalbenefit segmentation approach: analysis of secondary data, in-depth interviewswith graduate students, generation and formulation of item-pool, pretest andmodification if necessary (Mühlbacher and Botschen, 1988; 1990). Finally,subjects provided some socio-demographic information.

ResultsA total of 231 responses to the laddering interview were content-analyzed bythree independent judges and grouped into 22 meaning categories. Interjudgeagreement was 70 percent, which corresponds to a PRL reliability (proportionreduction in loss) of 0.95 (Rust and Cooil, 1994). Disagreements were resolvedthrough discussion so that all responses were classified.

Two corrections to the data were made. If a person gave two consecutiveresponses that belonged to the same meaning category, only the first responsewas coded. Also, if a person returned to the same meaning category after oneintermediate response to another category, the last response was not coded. Inboth cases, the sequence of responses express circularity (Pieters et al., 1994).

Three independent judges grouped the 22 meanings according to thedeveloped definition of attributes and benefits into eight attributes, 13 benefitsand one value. Interjudge agreement was 100 percent concerning the attributes,93 percent concerning the benefits. Two judges agreed identifying 13 benefitsand one value, the third judge classified one of the 13 benefits as a value, afterdiscussion it was coded as a benefit. Figure 2 shows the eight attributes, 13benefits and the one value.


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Following Reynolds and Gutman (1988), a 22 × 22 asymmetric dominancematrix was constructed, in which the attributes, benefits/consequences, andvalues act as the row and column elements, and in which the cells contain thefrequency in which a particular column element is mentioned after a particularrow element, aggregated across subjects and ladders. Only direct linkagesbetween meanings are entered, and the diagonal is empty, as a particular rowelement cannot be mentioned after itself.

Next, the means-end structure was constructed by depicting all connectionsbetween meanings that form active cells at a selected cut-off level of ten (Pieterset al., 1994). Figure 3 shows the aggregate hierarchical value map.

The overall HVM shows at the bottom line the eight identified attributes, thelinkages to the 13 identified benefits, and the linkages to the final value “goodfeeling” at the top of the map.

Figure 2.Identified attributes,benefits/consequencesand values

Attributes Benefits/consequences Values

Friendly Overview Buying urge Respect Feeling goodCompetent Fun in shopping Customer loyalty Efficient shoppingHonest Sufficient time Quality of the Reduces uncertainty

productAdvising Own decision Control contact Right clothingDistant momentHelpful HelpsPolite decisionEmpathetic

Figure 3.HVM of the wholesample

Feeling good

Customerloyalty

Buyingurge

Respect

Fun inshopping

Helpsdecision

Quality ofthe product

Rightclothing

Reducesuncertainty

Advising

CompetentFriendly

Honest Helpful

Distant

Empathetic

Polite

Overview

Sufficienttime

Controlcontactmoment

Efficientshopping

Owndecision

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Hierarchical cluster analysis was used to identify market segments. It was firstapplied to the attribute rating scale. Squared Euclidean distances were used asproximity measure and Ward’s procedure for calculation of the similaritybetween objects and clusters. Berg’s study (1981) showed that compared toother methods Ward’s procedure finds good partitions and assigns cases“correctly”. Two clusters described in Table I could be found.

From both clusters all attributes were rated as more important or lessimportant. Cluster 2 rates most of the items significantly lower than cluster 1,which means cluster 2 has higher expectations concerning characteristics andbehavior of sales-personnel than cluster 1. These results could be due toindividual answering tendencies, therefore, the analysis was redone withstandardized data and the two clusters could be found again. To prove theselectivity of the identified clusters discriminant analysis was conducted. Thehighest standardized canonical discriminant function coefficients were 0.68 forthe attribute “gives expert advice”, 0.55 for the attribute “endeavoured” and0.50 for the attribute “empathetic”.

The sample was randomly split in half. One half of the sample was used tocalculate the discriminant function, and the other half was only used forclassification.

Cluster 1 Cluster 269 cases 154 cases

Attributes Means Means Significance

Friendly even when store is crowded 1.90 1.55 ***

Friendly although customer is indecisive 1.51 1.24 ***

Friendly although customer is leaving 1.32 1.25without buying

Friendly regardless of customer’s appearance 1.42 1.16 ***

Endeavoured 2.68 1.51 ***

Service available when needed 2.72 2.05 ***

Reliable 1.77 1.50 ***

Expert advice 3.09 1.88 ***

Customer-oriented 1.55 1.21 ***

Respectful 1.74 1.43 ***

Honest 2.43 1.44 ***

Knows sizes 2.87 2.80Knows material 2.23 2.30Fashionable outfit 2.67 2.73Distant 1.58 1.65Regards customer’s taste 1.87 1.53 ***

Empathetic 2.07 1.42 ***

Notes: * = < 0.1; ** = < 0.05; *** = < 0.01

Table I.Market segments based

on attribute ratings


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The results of discriminant analysis indicate a clear cluster solution. A total of94 percent of the cases used in the analysis and 88 percent of the cases not usedin the analysis could be correctly classified. To check the stability of theidentified clusters the analysis was repeated using k-means clustering. Twocorresponding clusters could be identified. Cross-tabulation of the two clustersolutions using different methods showed that in 75 percent of the cases clustermembership corresponds.

To sum up the analysis of attribute ratings suggests that there are twodifferent market segments. The first has generally lower expectationsconcerning sales personnel than the second. They do not differ much in respectof the priority of the attributes. Comparing the rank orders of attributes basedon the means a Spearman correlation coefficient of 0.78 (significance 0.000)indicates a strong relation. This supports our expectation that segmentsderived from traditional rating data will only slightly differ with respect topreferred attributes due to the fact that respondents tend to rate most of theitems as important. If identified market segments do not differ in the kind ofexpectations, but only in their intensity segmentation results may not be veryhelpful for marketing management.

Better results may be obtained using free elicited attributes derived fromladdering data. As described above content analysis of the laddering data led toeight attributes which were used in hierarchical cluster analysis.

All variables from the laddering interviews were coded as 1 (a particularmeaning was elicited by a respondent) or 0 (meaning not elicited). Next, binarysquared Euclidean distances were used as proximity measures and Ward’smethod was applied. A total of four clusters could be identified which aredescribed in Table II. Chi-square-test was applied to compare differencesbetween the four clusters. Of the eight attributes, seven differ significantlybetween the groups. Members of cluster 1 want distant and friendly personnel,most of them also value expert advice. For members of cluster 2 only distantpersonnel and expert advice count. Members of cluster 3 expect friendly,competent personnel which is able to give expert advice. Cluster 4 wants to havehelpful and friendly personnel.

To prove stability of the results, the analysis was repeated using k-meansclustering. Corresponding clusters could be identified. Cross-tabulation of thetwo cluster solutions showed corresponding cluster membership in 83 percentof the cases.

Discriminant analysis was used to test selectivity of the identified segments.The sample was randomly split in half. One half was used to calculate thediscriminant functions, the other half was only used for classification.

A total of 91 percent of cases used in the analysis and 86 percent of cases notused in the analysis were correctly classified indicating that the selectivity isgood. The attributes “friendly” “distant” and “helpful” contribute most todiscrimination. This supports that cluster solutions using laddering data withfree elicitation of attributes and benefits improve in comparison to solutionsbased on rating data.

So far, we analysed the attribute level. As discussed, attributes do not explainper se for what reasons a product or service is bought. Perceived consequences(benefits) explain why people are looking for certain attributes. To produce“true” benefit segments the consequences of attributes from laddering datawere used in hierarchical cluster analyses in the next step. Binary squaredEuclidean distances measure and Ward’s method were used. Four clusters couldbe identified, which are described in Table III.

Members of the four benefit segments differ significantly accordingto their benefits sought when shopping for clothing. Members of cluster 1want to have sufficient time to make their own decision and be treatedrespectfully. Members of cluster 2 want sales personnel to reduce theiruncertainty so as to get the right clothing. Members of cluster 3 mainly seekfun in shopping, members of cluster 4 seek mainly buying urge. K-meanscluster analysis to test the stability of the cluster solution came up withcorresponding clusters.

The selectivity of the clusters was tested using discriminant analysis. Thesample was randomly split half. One half was used to calculate the discriminantfunction, the other half was only used for classification.

The results of discriminant analysis indicate a clear cluster solution. A totalof 85 percent of the cases used in the analysis and 77 percent of the cases notused in the analysis could be correctly classified. The benefits “respect”, “fun inshopping”, “efficient shopping” and “reduce uncertainty” contribute most todiscrimination.

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Table II.Market segments

identified by clusteringattributes derived from

laddering analysis

Cluster 1 Cluster 2 Cluster 3 Cluster 482 cases 43 cases 59 cases 47 cases

Percentage of Percentage of Percentage of Percentage ofcluster cluster cluster cluster

Attributes members members members members Significance

Friendly 92 0 90 66 ***

Competent 27 26 51 38 **

Expert advice 56 67 73 45 **

Distant 100 84 9 21 ***

Helpful 28 12 7 96 ***

Empathetic 15 21 27 17

Polite 7 28 12 40 ***

Honest 27 33 48 13 ***

Notes: * = < 0.1; ** = < 0.05; *** = < 0.01; = > 50 percent; = > 75 percent

To compare the different three cluster solutions based on attributes derivedfrom rating and laddering data and consequences derived from laddering data,chi-square-tests were applied. No significant relation could be found. Membersof the various benefit segments do not concentrate in certain attribute segmentsand also members of the various attribute segments are spread over all benefitsegments (Table IV and V). These results show that as expected segmentsbased on the attribute level will significantly differ from segments built on thebenefit level.

To gain improved insight, the importance of attributes of sales personnelwas compared between the benefit segments (Table VI).

Significant differences between the clusters could be found for only four ofthe eight attributes. These occur because for those attributes one of the benefitsegments does not value the certain attribute as much as the other segments.


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Cluster 1 Cluster 2 Cluster 3 Cluster 458 cases 56 cases 57 cases 60 cases


Consequences members members members members Significance

Sufficient time 53 36 28 25 ***

Right clothing 47 68 47 27 ***

Reduces uncertainty 24 64 28 12 ***

Overview 21 55 19 12 ***

Control contact

moment 10 29 9 23 **

Fun in shopping 41 7 91 12 ***

Quality of the

product 38 27 12 12 ***

Helps decision 10 46 26 5 ***

Own decision 67 48 12 38 ***

Buying urge 22 21 30 50 ***

Efficient shopping 16 13 33 8 ***

Respect 66 2 20 2 ***

Customer loyalty 17 25 12 3 ***


Table III.Benefit segments based on meanings derived from laddering analysis

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For all benefit segments there are two or three attributes that are desired bymost of their members.

These results support that the same attribute can lead to different benefitssought and that a single benefit can be based on multiple attributes.

Benefit segments should be accessible to be useful for management. Themore variability segmentation shows in demographic or behavioral variablesthe easier the segments can be accessed. To test the variability of “true”benefit segments compared to attribute segments derived by laddering andby attribute ratings, we related the three cluster solutions to two demographicvariables (sex and age) and five behavioral variables (the amount of formerpurchases in the same shop, the amount of different shops visited for thepurchase, the amount of time and money spent for the purchase, existence ofspecial ideas before purchase). In Tables VII, VIII and IX significantdifferences within the three cluster solutions are extracted. The “true” benefitsegments show more variability in some important variables than theothers.

The comparison of the two attribute clusters based on rating data revealedonly one significant difference in respect of the behavioral variable “amount oftime spent for the purchase”.

Attribute segments based on laddering dataCluster 1 Cluster 2 Cluster 3 Cluster 4 Row total

Benefits segmentCluster 1

Count 24 11 12 11 58Row percentage 41 19 21 19 25Col. percentage 29 26 20 23

Cluster 2Count 20 11 11 14 56Row percentage 36 20 19 25 24Col. percentage 24 26 19 30



Column 82 43 59 47 231Total 36 19 25 20 100

Table IV.Comparison of segments

based on attributesderived from ladderingand benefit segments


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Attribute segments based on ratingsCluster 1 Cluster 2 Row total

Benefits segmentCluster 1

Count 20 35 55Row percentage 36 64 25Col. percentage 29 23

Cluster 2Count 15 40 55Row percentage 27 73 25Col. percentage 22 26



Column 69 154 223Total 31 69 100

Table V.Comparison of segmentsbased on attribute ratings and benefit segments

Table VI.Market segmentsidentified by clusteringattributes derived fromladdering analysis

Benefits segmentCluster 1 Cluster 2 Cluster 3 Cluster 458 cases 56 cases 57 cases 60 cases


Attributes members members members members Significance

Friendly 74 64 74 63

Competent 40 38 44 20 **

Expert advice 64 64 70 43 **

Distant 62 63 40 65 **

Helpful 36 39 35 23

Empathetic 21 23 18 17

Polite 24 7 19 25 **

Honest 31 32 30 23


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The four-cluster solution at the attribute level derived from laddering datashows two significant differences in the behavioral variables “existence ofspecial ideas before purchase” and “money spent for the purchase”.

At the “true” benefit level the variability in important variables increases.The two-behavioral variables “the amount of former purchases in the sameshop”, “the amount of time spent for the purchase” and the demographicvariable “age” significantly differ between the four clusters.

Cluster 1 Cluster 2Basis: 64 cases Basis: 150 cases

Less demanding Demandingcustomers customers

Variables Mean Mean Significance

Time spent 2.60 2.97 ***

Note: *** = < 0.1

Table VII.Comparison of behavior

and demographicvariables between

attribute clusters derivedfrom ratings

Cluster 1 Cluster 2 Cluster 3 Cluster 4Basis: 74 cases Basis: 41 cases Basis: 53 cases Basis: 46 cases

Variables Mean Mean Mean Mean Significance

(1 <–> 4)***

Special 2.48 2.27 2.23 1.79 (2 <–> 4)*

ideas (3 <–> 4)*

Money 1,358 1,454 1,873 1,339 (1 <–> 3)**

spent

Notes: * = < 0.1;** = < 0.05; *** = < 0.01

Table VIII.Comparison of behavior


attribute clusters derivedfrom laddering

Cluster 1 Cluster 2 Cluster 3 Cluster 4Control/relax Correct clothing Fun Purchasing

Basis: 54 cases Basis: 52 cases Basis: 54 cases Basis: 54 casesVariables Mean Mean Mean Mean Significance

Former purchases 3.18 3.67 3.31 3.56 (1 <–> 2)**

Time spent 2.89 3.09 2.82 2.65 (2 <–> 3)**

Age 22.09 22.95 23.26 22.52 (1 <–> 4)***

Notes: * = < 0.1;** = < 0.05; *** = < 0.01

Table IX.Comparison of behavior


benefit clusters derivedfrom laddering


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Discussion and managerial implicationsMost of segmentation studies conducted for companies are still based ontraditional segmentation criteria. The benefit-segmentation approachintroduced by Haley (1968) tries to avoid weaknesses of the traditionalapproaches insofar as it proposes to focus on the benefits sought by thecustomer. However, most of the published studies tend to identify attributesinstead of benefits sought.

The purpose of this paper has been to show that means-end chains can beused to develop “true” benefit segments. Therefore, we further elaborated onthe distinction between attributes and benefits sought. Means-end chains seemto be ideally suited to segment markets according to different levels, onattributes, benefits or values, and on the linkages between the meanings. Amodified laddering technique based on means-end chains which allows freeelicitation of attributes and benefits sought was applied to create marketsegments. A comparison of attribute-based segments with “true” benefitsegments showed no correlation. Therefore, we suggest as long as the objectiveof segmentation is to better explain why consumers prefer products, services ora certain behavior to focus on the “true” benefits and to use desired attributes tofurther describe the identified “true” benefit segments.

We showed that the following disadvantages of the standard benefitsegmentation approach:

• all identified attributes/benefits have to be rated by respondents, evenwhen they are not relevant for the respondent;

• respondents tend to rate most of the items as important which makes itmore difficult to develop significant different segments;

can be avoided by the means-end approach.Means-end chains seem to be a powerful tool for effective market

segmentation. As far as the standard benefit segmentation procedure isconcerned a simplified laddering technique or a benefit chain analysis can beused during the exploratory qualitative phase. This would help to identifyattributes and benefits sought and to build “true” benefit segments.

As far as practicality and cost issues are concerned benefit segmentation onladdering data might result in higher costs and complexity. In the soft ladderingversion well-trained interviewers are needed for the data collection and in softand hard laddering content analysis of the data is time consuming and bears theadditional potential of pitfalls.

From a managerial perspective benefit segmentation based on means-endchains should allow a deeper understanding of why people look for certainattributes, in our study the reasons why customers look for certaincharacteristics and behavior of sales personnel. To focus on the underlyingbenefits sought permits sales personnel to adapt their behavior more closely tocustomers’ underlying expectations and helps to improve customer satisfaction.Especially in service encounter situations the sales employees play a crucialrole to successfully differentiate from other services companies.

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Managers can use this information when training and recruiting theirpersonnel. A basic tenet of any sales training seminar is understanding thecustomer. This is because salespeople must manage dynamic communicationprocesses. Rather than reading a script that remains constant for all, asalesperson must tailor an approach to each customer, listening to those whoneed to be heard and explaining details to customers who need information(Gengler et al., 1995; Spiro and Weitz, 1990). Indeed, Weitz (1978) found that asalesperson’s perceptual ability, the ability to understand a customer and thecustomer’s decision making process, is related to sales performance. The usageof information of identified “true” benefit segments in training could improveefficiency and effectiveness. Under an innovative perspective, sales employeesmight identify additional characteristics and kinds of behavior to satisfyidentified benefits sought by different segments.

However, the proposed benefit segmentation approach could be applied tomany other aspects of services industry, e.g. how do customers differ accordingto their benefits sought in respect to atmosphere, the core service, andadditional services?

Limitations and future researchOne limitation of our study concerns the paper-and-pencil laddering (hardladdering). The primary advantage of this method is the relative efficiency withwhich data can be collected. However, little is known about the validity andreliability of the procedure and the comparability of results obtained fromtraditional laddering interview (soft laddering) and paper-and-pencil laddering.For example, the many empty boxes that appear in the questionnaire may putstrong demands on subjects to list consequences and goals that are of littleimportance to them (Pieters et al., 1994). It would be interesting to see researchcomparing the results of hard and soft laddering. Should a test of convergentvalidity establish that both techniques lead to similar results, one could safelyconclude that hard laddering is a preferable technique, since it is easier toadminister and less costly. Differing results would call for an investigation ofpredictive validity in a larger context (Grunert et al., 1995).

Another limitation concerns the free elicitation technique. So far we do notknow if the application of other techniques, e.g. triadic sorting, free sorting orattribute selection, would produce similar results. This raises two questions:

(1) Will the set of attributes finally selected as the starting point of theladders differ depending on which elicitation method is used?

(2) If yes: which set of attributes is the “right” one?

Although in this paper we tried to further elaborate the distinction betweenattributes and benefits sought in practice, many borderline cases may still turnup. Categorization in a uniform way is heavily dependent on the availability ofcontext information. Hard laddering usually provides very little contextinformation.


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As far as segmentation is concerned we demonstrated that means-end chainsprovide a powerful tool for “true” benefit segmentation. The identified benefitsegments can be described more in detail, connecting them with attributes. Thecombination of segments with buying situations and additional behavioral anddemographical variables would further allow predicting buying behavior tostart, additionally actionability and accessibility would be improved. As thestudent sample can be regarded as a rather homogeneous group in respect ofdemographic and behavioral variables, we expect the variability of “true”benefit segments in these variables to improve studying more heterogeneousgroups.

A further step, as already mentioned in the paper, would be segmentingcustomers according to similar linkages of their perceptual structure or on thebasis of their entire means-end chains. To our knowledge only one study(Roehrich and Valette-Florence, 1991) used links between categories as the basisfor clustering. The units of analysis were ladders and not respondents. Everyrespondent may then be a member of more than one cluster, which does notseem to be a right solution. However, it should be possible to conduct a similarprocedure with respondents instead of ladders as the unit of analysis (Grunertet al., 1995).

In spite of the shortcomings of our study, the approach taken in this paper touse means-end chains to identify “true” benefits seems promising. Although theidea to use means-end for segmentation purposes has been often proposed toour knowledge this is the first paper where it is applied in an empirical studywithin the services industry.

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