CHARLES E. GENGLERANDTHOMAS J. REYNOLDS

TfCHARLES E. GENGLERAssistant Professor

ot MarketingRutgers University

at Camden

THOMAS J. REYNOLDSPresident and

Chief Executive OfficerWirthiin-Reynoids

CONSUMERUNDERSTANDING ANDADVERTISING STRATEGY:ANALYSIS AND STRATEGICTRANSLATION OFLADDERING DATATwo major obstacles exist to the proliferation of laddering as a man-agement tooL First, the sheer magnitude of tedious work an analystmust perform to complete an analysis adds excessive costs to anystudy. Second, many who are familiar with the technique still havedifficulty bridging from data to strategy to executional design andimplications. This paper addresses both of those issues by describ-ing a newly available software support tool to make the data analy-sis a more reasonable task and by discussing the issue of strategydevelopment and implementation. An example within the productcategory of dog food data is used.

The authors would like to express theirgratitude for contributions by StevenWestberg and Jonathan Goldwater.

An important issue forboth industry and aca-demic consumer re-

searchers is the development ofan understanding of how con-sumers derive personally rele-vant meaning about products.This meaning is the basis con-sumers use to shape their deci-sion criteria among competitiveproducts and services. In thispaper, we discuss the process bywhich pragmatic analysis ofqualitative data on consumermeaning can be achieved andhow this analysis can be used toenhance creative copy develop-ment. All too often, the resultsof qualitative research couldhave been written before the re-search was performed, eitherbecause the final results aremerely the a priori opinion ofthe researcher involved, or be-cause the results are so obviousthat the research need neverhave been performed. The intenthere is to suggest a methodolog-ical process which will alleviateboth of these problems whengathering information on con-sumer meaning.

Numerous academic studieshave addressed this fundamentalissue of meaning from the tradi-tional product-attribute perspec-tive (Bass, Pessemier, and Leh-mann, 1972; Bass and Talarzyk,1972; Lehmann, 1971; McAlister,1982). Under the attribute per-spective, product meaning is theobservable physical characteris-tics of the product. This limitingperspective ignores any type ofpersonal meanings of the prod-uct attributes.

Recognizing this deficiency,product meaning has been ex-panded beyond merely attributesto include benefits those attrib-utes symbolize to the consumer.This orientation concentrates pri-marily on the direct results theproduct delivers to the consumerthrough product purchase or con-sumption (Haley, 1968, 1984; My-ers, 1976). More recently, the defi-nition of product meanings hasbeen expanded yet again to in-clude higher levels of abstraction(Gutman and Reynolds, 1979),namely, personal values (Homerand Kahle, 1988; MitcheU, 1983;Vinson, Scott, and Lamont, 1977).

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The ever-broadening focus onunderstanding the consumermeanings that underlie the deci-sion-making process, from attri-bute to benefit to personal-valueperspectives, is primarily drivenby the competitive forces in themarketplace. That is, the dra-matic increase in the number ofcompeting brands in most prod-uct categories forces marketersto look for positionings that aremore directly relevant to the de-cision-making criteria of the con-sumer. Clearly, understandingthe personally relevant meaningsthat consumers hold for a prod-uct, and the new positioningstrategies which may stem hornthese meanings, is invaluable tomarketing strategists.

Means-End Theory (Gutman,1982) presents an appealingframework to more comprehen-sively represent the consumermeanings that underlie product-positioning research. Ratherthan focus on a particular levelof meanings, the Means-Endframework incorporates all levelsinto a conceptual model, thatadditionally focuses on the asso-ciations (or derived meanings)between the levels. The associa-tions between concepts offer anexplanation of how consumersinterpret a product attribute assymbolizing associated benefits.Consumers translate productattributes into the benefits(termed consequences) they pro-duce, and benefits are ultimatelytranslated into the consumer'sdriving value orientation.

For example, a dog food mayhave an attribute of being "Dryand Crunchy." To a dog owner,"Dry and Crunchy" means thedog food will help deliver theconsequences (benefits) of"Cleaner Teeth" and "HealthierDog." In turn, these conse-quences help the dog owner tofulfill a personal value, to feel asense of "Responsibility as aGood Owner." Put simply, the

product, as defined by its dis-criminating perceptual attributes,is the means which satisfies themore personal ends, representedby values.

Importantly, the Means-Endframework adds a much richerunderstanding of how consum-ers derive meaning from prod-ucts. Within this framework,meanings reflect the linear pat-tern of concepts and associationsacross levels of meaning, which,taken together, serve to explainthe underlying reasons why theconsumer considers a given at-tribute to be salient. The cogni-tive perspective of Means-EndTheory can be seen to incorpo-rate attribute, consequence, andvalue research paradigms into aframework encompassing allthree models. It is the associa-tional aspect of the Means-Endmodel that provides a uniqueperspective on consumers' per-sonally relevant meaning.

Recently, several aspects ofMeans-End Theory are receivingincreased attention. Several arti-cles address research methodol-ogy (Reynolds and Gutman,1988; Valette-Florence and Ra-pacchi, 1991). Others have ad-dressed the application to posi-tioning strategy design (Olsonand Reynolds, 1983; Reynoldsand Craddock, 1988; Reynoldsand Gutman, 1984). Still othersapply Means-End theory as aconceptual framework for thestrategic assessment of advertis-ing (Gengler, 1990; Gengler andReynolds, 1993; Reynolds andGengler, 1991; Reynolds andRochon, 1991). This attentionhas resulted in the broader real-ization that significant potentiallies in using this consumer-based, strategically oriented re-search framework. However,three major practical problemsemerge: (1) the significant timeand cost of gathering individualin-depth, means-end (laddering)data; (2) the time and effort re-

quired to perform the contentanalysis of the qualitative re-sponses (steps in the ladders)and the quantitative summariesof the dominant pathways; and(3) the lack of any detailedframework or system to translatestrategic options as representedin the summary HVM into aworking format for the agencycreative staff.

Laddering Data Issues

Data Collection. Several re-searchers recently addressed thefirst of these problems: the issueof data collection. Gengler (1990)used an interactive computerprogram to assess strengths ofassociations between concepts.The concepts were derived a pri-ori in focus groups. Vallette-Flor-ence and Rapacchi (1990) used acard-sorting task to group con-cepts which were related. Bothof these techniques relied heavi-ly upon a priori definition ofconcepts to be associated andare in that respect inferior to theopen-ended format of ladderinginterviews. However, both tech-niques are quicker and easier toadminister than laddering inter-views. The main issue is wheth-er or not researchers feel theirconsumers are homogeneousand predictable enough to use apredetermined set of concepts.Walker and Olson (1991) used apaper-and-pencil technique ofdata collection. In their tech-nique, a questionnaire was ad-ministered to a group of individ-uals simultaneously. This tech-nique shows promise, butprecludes the insightful probingcharacteristic to laddering. Insum, a number of researchersare attempting to find more cost-effective and efficient methodsof data collection, but each ofthese has potential shortcomingswhen compared with the ladder-ing techruque advocated byReynolds and Gutman (1988).

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Indeed, it is difficult to justifyany small savings in time ormoney when compared with theenormous cost of inaccurate orincomplete results.

Analysis of Laddering Data.The second problem offers a bet-ter place to increase the effi-ciency of conducting a Means-End study: streamlining and im-proving the process of analysis.Analysis of laddering data is acumbersome task requiring sev-eral days of effort by highlyskilled analysts for even a me-dium-sized study. The basicanalysis steps can be summa-rized as (see Reynolds and Gut-man, 1988, for a detailed de-scription of these tasks):

A. Breaking the raw, conversa-tional data into separatephrases. These phrases arethe basic elements uponwhich subsequent analysis isbased. This involves review-ing the verbatim notes ortapes of the discussionprobes for the elements thatbest represent the conceptsexpressed by each individualsubject.

B. Content analysis of the ele-ments selected in Step A.

C. Summation of associationsbetween the content codes,resulting in a quantitativeassessment of all pairedrelationships, termedimplications.

D. Construction of a diagram tomeaningfully represent themain implications, termed aHierarchical Value Map(HVM).

Translation of Means-End Re-sults into Strategy and CreativeCopy. The third problem in-volves the lack of any detailedframework to translate strategicoptions from a laddering studyinto a working format for theagency creative staff. Althoughthe issue of divining strategy

from results is often discussed(Olson and Reynolds, 1983;Reynolds and Craddock, 1988),the focus generally concentrateson definition of the strategy andignores the issue of translatingthat strategy into creative, execu-tional concepts for advertise-ments. Discussing advertisingstrategy. Little (1979) states:"Good strategy requires imagi-nation and style and always will.At the same time, strategyemerges best from a foundationof reliable facts and sound analy-sis." Integration of creativity andMeans-End study results to de-velop a strategy and design cre-ative executions is an art. To be-come accomplished at any artrequires the development oftechnique. A key issue, then, isthe outlining of techniqueswhich can aid creative staff indeveloping executions fromstrategies.

Purpose of this Paper. Theraw Means-End data is the keybuilding block from which allsubsequent analysis is based.Although other approaches todata collection have been pro-posed and are being pursued,the costly interviewing processof laddering is often a necessity.The process is not subject tostreamlining without sacrificingquality of understanding of themeanings that drive consumerdecision-making. Hence, ratherthan focus on data collection,the dual purpose of this paper isto deal explicitly with the analy-sis and strategic implementationof laddering data. The followingsections of the paper deal with abrief background of means-endtheory and laddering, a discus-sion of improvements in ladder-ing analysis, and the use of lad-dering results to aid in develop-ing potent creative copy.

Specifically, this paper firstdetails the use of an interactivesoftware tool that expedites therather cumbersome and time-

consuming analytic steps out-lined above (Gengler and Reyn-olds, 1989). Content analysis is amajor portion of qualitative anal-ysis. Although labor intensiveand often tedious, great care andskill must be used in the contentanalysis, as the results are thebasis of all subsequent analysis.Often, content analysis is an it-erative task, where the analystsmay recode data several times,combining categories, splittingcategories, eliminating or creat-ing new categories, until theyfeel they have achieved the opti-mal solution. This stage of theanalysis process is drasticallyimproved by the use of interac-tive computer software, so thatthe content analysis can easilybe reviewed and modified.

In addition, automating theprocess allows analysts to de-velop several separate or aggre-gate analysis based upon demo-graphic segmentation. After afirst analysis, each codingchange done by hand can resultin many hours of recalculationfor the next analysis; whereas anautomated tool can help the ana-lyst reach the same point in amatter of seconds. Using thesystem we describe here, thesummation of associations be-tween content codes can be per-formed almost instantaneously,and analysts can experimentwith different HVMs resultingfrom these summations quicklyand easily. This experimentationwould require days of repetitiveeffort by hand. Essentially, theuse of an interactive softwaretool described here moves theanalysis of laddering responsesfrom being a rough, one-shotsubjective analysis to a thor-oughly reviewed and easily re-vised final analysis that a mar-keting manager can put confi-dence in. In this paper we shallgo through this process to dem-onstrate how laddering data canbest be analyzed.

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Secondly, the paper presents aconceptual approach to translat-ing laddering data into a formatfor creative ideation sessions.This translation bridges the gapbetween the abstract under-standing of meanings latent instrategy specification to the con-crete construction of creativemessages. To be useful to cre-ative executives, a ladderingstudy should deliver more thanjust a few vague terms specify-ing an overall positioning, itshould deliver information onhow consumers relate differentmeanings and a basis for ideaand message development—nota dogmatic restriction of exactlywhat an advertisement shouldsay. It is information to feed thecreative process rather than re-strictions to suffocate it. Meth-ods for the effective usage ofladdering data are discussed inthe final section of the paper.

Background

Personal values are theorizedto be a basis of attitude andpreference (Rosenberg, 1956;Vinson, Scott, and Lamont,1977; Howard, 1977). Means-endtheory (Gutman, 1982) is basedupon a personal values orienta-tion, in which personal valuesare the motivating "end-states ofexistence" which individualsstrive for in their lives (Rokeach,1973). Personal values, then,represent individuals' internal,self-relevant, goal states, whileproducts are often representedas a bundle of physical productattributes. Means-end theorysimply posits that the way inwhich these physical attributesof products are linked to per-sonal values of individuals de-fines how products gain per-sonal relevance and meaning.Thus, a physical attribute of aproduct is important if that at-tribute, during product con-

sumption, produces a desirablebenefit or consequence to theconsumer. In turn, the perceivedconsequence of product pur-chase and/or consumption, de-rives its importance through theextent that this consequence islinked to another higher levelconsequence and ultimately intoan individual's personal valuesystem. A fundamental problemfacing consumer researchers ishow to ascertain this means-endcognitive structure (attributes toconsequences to values) for anyparticular market.

Laddering (Gutman and Reyn-olds, 1979; Reynolds and Gut-man, 1988) is the standardmethod for assessing cognitivestructure consistent with themeans-end paradigm. The lad-dering process is performedthrough a series of one-on-one,in-depth, personal interviews. Inthe process of laddering, sub-jects are asked to perform achoice or sorting task in order touncover a preference-based dis-tinction which they use tochoose between brands in themarket. The interviewer thencontinues to ask the respondenta series of probing questions touncover the structural relation-ships between this distinctionand the respondent's personalvalue system. In other words,the interviewer is trying to elicitthe cognitive relationships thatgive personal relevance to theproduct preference distinction.These questions are designed ina manner that will not lead thesubject to respond in any spe-cific answer but will promptthem to give an answer that re-flects, in their own words, theirown particular perspective andmeaning. Typically, these ques-tions will be short and of a formsimilar to "Why is this importantto you?" A thorough discussionof the laddering interviewingtechnique can be found in Reyn-olds and Gutman (1988).

Stages of ComputerAnalysis ofMeans-End Data

Analysis of the responsesgathered through laddering in-terviews involves several steps.To illustrate this process, datagathered on consumer choices offood for their canines is used asan example. This data set con-tains responses from 67 ladder-ing interviews, with one to fourladders produced from each sub-ject. The discussion of analysiswill be presented in terms of thestages the analyst must gothrough, the important consider-ations at each stage, and howthese stages can be facilitatedusing Gengler and Reynolds'(1989) decision-support tool,LADDERMAP.*

Stage A: Specifying Elementsof a Means-End Chain. First ofall, the conversational nature ofthe raw data from ladderingforces the analyst to separate theladder responses of each individ-ual into "chunks" of meaning.These chunks correspond to thedistinct levels of product mean-ing identified by the analystwithin the ladder. Two impor-tant decisions must be made bythe analyst at this stage. First,since the interviews are open-ended and conversational in na-ture, response not germane tothe topic must be eliminated.Secondly, specifying what com-poses a "chunk" of meaning isextremely important since theseunits are the basis of all furtherstages of analysis. For example,an extract from a typical ladder-ing interview may go somethinglike "I prefer brand J (of dogfood) because it is dry and

*LADDERMAP is a registered trademarkof Means-End Software. LADDERMAPsoftware can be obtained throughMeans-End Software, P.O. Box 3364,Camden, New Jersey 08101, ph. (609)858-6781.

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Figure 1

Ladder Entry Screen from LadderMap Software

LADDER EDIT SCREEN

Subject ID = 007 Ladder # = 4

> freer to do more things for meVALUE Synonym:

> saves me timeCONSEQUENCE Synonym:

> less mess, convenientCONSEQUENCE Synonym:

> dry textureATTRIBUTE Synonym:

Screen ID = LE-2

data name = dog food

accomplishment

save time

convenience

dry

Enter F10 to exit, FI for help

crunchy and it cleans my dog'steeth when she eats it." In thisstatement, the subject is actuallystating two different "chunks"of means-end information, spe-cifically, the attribute "dry andcrunchy" and the consequence"cleans my dog's teeth."

The LADDERMAP software isdesigned to assist in this stage.An interactive data entry featureis provided. Under this feature,analysts can enter multiple lad-ders per interview subject andup to ten "chunks" of meaningper ladder. As ladders are en-tered for each respondent, theanalyst is prompted for the firststage of content analysis whichis classifying each "chunk" aseither an attribute, a conse-quence, or a value. This classifi-cation underlies the theoreticalbasis of the analysis and aids theanalyst in discerning what areand are not relevant "chunks" toinclude from the verbatim re-sponses. An example of the lad-der entry screen is shown in Fig-ure 1, with a ladder from thedog food data.

Stage B: Content Analysis ofMeans-End Data. Next, the"chunks" of meaning must becontent analyzed in order to ag-

gregate and generalize acrosssubjects. This process involvestwo steps. The first step is todefine a dictionary of contentcodes into which classificationscan be made. This involves apreliminary review of the dataand the development of a com-prehensive {and exhaustive) setof categories into which to clas-sify all of the "chunks." The sec-ond step is the actual assign-ment of each verbatim to thesecodes. In a well-defined productcategory, where analysts typi-cally have strong insights into

consumers' perceptions and mo-tivations, many of the categorycodes may be defined a priori.More often, however, in ladder-ing data analysis the steps ofcode definition and classificationprocess are interwoven and thecodes essentially evolve duringthe classification.

To facilitate this highly labor-intensive and recursive task,which inherently requires inten-sive human judgment and deci-sion-making, the software allowsinteractive coding in an easy-to-use format. Actual content fromthe interviews is shown on thescreen, grouped under the codesit has been assigned to. An ex-ample of the screen is shown inFigure 2a, where verbatim re-sponses have been coded underthe categories of "Flavor" and"Taste." If, upon inspection, it isfound that some concepts areassigned incorrectly, they can beeasily corrected and assigned tothe proper content code. Also, ifthe initial coding scheme is veryspecific, many content codesmay have relatively few actualconcepts assigned to each. Forexample, the codes of "Flavor"and "Taste" above could becombined under flavor.

Similar codes can then be eas-ily grouped hierarchically under

Figure 2a

Performing Content Anaiysis Interactively

>flavortastes good, like meatgood beefy flavortastes like real meatvariety of flavorsmeaty flavor

tastetastes better than other brandstastes goodmy dog likes the taste

F2- EDIT SYN F3- Change ACV F4- Track Code

n = 5n = 1n = 1n = 1n = 1n = 1n = 3n = 1n = 1n = 1

F10 EXIT

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a larger code, making reassign-ment an easy task. For example,in Figure 2b all of the items un-der "Taste" are easily movedunder the major heading of "Fla-vor" by simply assigning"Taste" under "Flavor" in thecoding scheme. This enables theanalysts to split, combine, orredefine categories quickly andeasily on-line. The content anal-ysis task is truly the heart of lad-dering analysis. It is the stepwhere qualitative data (the raw,verbatim responses from the lad-dering interviews) are convertedinto nominal codes which can bequantified. Because codes can beeasily combined hierarchicallywithin each other when usingthe software, we recommend alarge number of very specificcodes when first analyzing thedata and gradually combiningand grouping similar meaningsuntil a manageable number ofapproximately 50 remain. How-ever, if you are performing anal-ysis by hand, we recommendattempting to reduce the numberof codes to approximately 50 im-mediately. Although this mayresult in a slightly higher mis-classification, the combination ofcategories by hand would be re-strictively difficult and tedious.

besides being error prone. Thelexical listing reports from thesoftware, which report what ver-batim responses are categorizedunder each content code, can bereferenced at any future point tosee what exactly was collapsedinto the final content codes. Anyfurther analysis of the data isonly as good as the content anal-ysis. Hence, this task should notbe underrated in importance.

Stage C: Defining Connec-tions between Content Codes.Once the laddering data is classi-fied into codes, it can be quanti-tatively analyzed to produce adiagrammatic representation ofthe meaning structure. The endproduct of a laddering data anal-ysis is a graphical representationof means-end structures aggre-gated across all subjects, the Hi-erarchical Value Map (HVM). AnHVM consists of the differentcontent codes derived from con-tent analysis arranged on a mapand connected with lines. Theselines show the common path-ways of meanings, representinghow product attributes are re-lated to personal values. Themain goal of analysis is the con-struction of the HVM, which isthe framework for assessing stra-tegic positionings in the market-

Figure 2b

Combining Two Categories during Content Analysis

>flavortastes good, like meatgood beefy flavortastes like real meatvariety of flavorsmeaty flavortaste

tastes better than other brandstastes goodmy dog likes the taste

F2~ EDIT SYN F3- Change ACV F4- Track Code

n - 5n = ln - 1n ^ in = 1n - 1n = 3n = 1n = 1n=«1

F10EXIT

place. This involves two stages:determining what connectionsshould be represented on theHVM, or directed graph, andconstructing the HVM in a fash-ion which is easily readable.

Reynolds and Gutman (1988)present a straightforward deci-sion rule for determining whatassociations should be illustratedin an HVM. Each association iscompared with a cutoff level. Ifan association has a strengthgreater than or equal to thatvalue, then the association orconnecting linkage is illustratedon the HVM. The LADDERMAPsoftware implements this deci-sion rule in its HVM construc-tion algorithm.

The algorithm consists of threesteps. First, we construct an ag-gregate implication matrix,which contains the sums of all ofthe instances where conceptswere linked in the laddering in-terviews. These associations canbe counted in two ways—onlythe direct associations are in-cluded, or all the direct plus in-direct associations are counted.To illustrate what direct and in-direct associations mean, con-sider a means-end chain ofA ^ B —> C. Here, we have di-rect association, from A to B andfrom B to C and an indirect as-sociation from A to C. The sumof direct and indirect associa-tions is an indicator of thestrength of a given association.Often, multiple ladders will begathered from each individual inthe course of an interview. Thenumber of ladders elicited fromeach individual respondent isusually dependent upon the re-spondent's depth of knowledgeor involvement in the productcategory. Also, a respondentmay name an association be-tween two concepts severaltimes in different ladders elic-ited. In this case the associationis only counted once per respon-

24 Journal of ADVERTISING RESEARCH-^ULY/AUGUST 1995

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dent when constructing the impli-cation matrix in order to preventbias in the aggregate results.

After the implication matrix isconstructed, a cutoff value is se-lected by the analyst to deter-mine which connections shouldbe represented on the HVM. Toassist the analyst in making thisdecision, a bar chart is providedon screen by the software toshow how much variance wouldbe explained by different levelsof cutoff values (see Figure 3).Typically, this cutoff value iscompared against the aggregateof direct plus indirect associa-tions in each cell of the implica-tion matrix. A binary matrix isformed which contains a (1) ineach cell for which the corre-sponding element of the implica-tion matrix is greater than orequal to the cutoff value and a(0) otherwise. These binary flagsindicate which associations orconnecting linkages should beillustrated on an HVM. How-ever, in the interest of construct-ing a meaningful unclutteredHVM, not all of the marked as-sociations are actually drawn asindividual lines. Some of theconnections indicated in the bi-nary matrix are considered re-dundant and, therefore, do notneed to be illustrated. If, for in-stance, the matrix indicatesX ^ Y, X -* Z, and Y ^ Z, thenthe direct connection X ^ Z isredundant since it is captured inthe X ~* Y and Y ̂ Z relation-ships. Although some may ar-gue that this conceals the X ^ Zconnection, the map wouldquickly degenerate into an un-readable state if all redundantconnections were included. Afterall of the redundant, "passthrough" relationships are elimi-nated, the binary matrix canthen be used to draw the finalHVM.

The process of summing theimplication matrix and determin-

ing the connections to be madeis a relatively simple task andtakes only a few seconds on amicrocomputer, whereas it is anextremely long and laborioustask by hand. This allows theanalyst to go back and makecontent-analysis-coding changesand subsequent alternate analy-sis without concern since thequantitative steps can be repro-duced rapidly and effortlessly.Furthermore, automation of thetask allows the analyst moreflexibility and control over theprocess. This allows for experi-mentation with different levelsof cutoff values and differentcoding strategies. Interestingly,the total number of Is accountedfor in the HVM which is re-ported can be considered ameasure of the representative-ness of the solution. This per-centage index serves as a usefulsummary measure that shouldbe reported in all ladderingresearch.

Stage D: Drawing the Hierar-chical Value Map. After the datais analyzed to determine exactlywhich associations should beillustrated as connections on theHVM, the final stage of produc-ing an HVM must be performed.Two requirements are imposedon the analyst at this stage.First, the finished HVM mustrepresent a significant number ofthe associations derived from theraw laddering data. From experi-ence in conducting over 100studies, the minimum thresholdvalue should never be less than70 percent with an average num-ber typically in the 75 to 85 per-cent range. To represent anysmaller percentage can causevaluable insights to be lost. Sec-ondly, and perhaps more impor-tantly in many business environ-ments, the final HVM must beeasily interpretable by manage-ment if it is to be a viable tool.Again, this stage involves quali-

tative judgments made by askilled analyst to produce anHVM which is both accurate andaesthetically pleasing, hence atradeoff between validity andparsimony.

The algorithm discussed inStage C only determines whatconnections should be made, butdoes not actually indicate wherenodes should be placed to drawan intelligible HVM. Genglerand Reynolds (1989) presented aheuristic-based algorithm andinteractive editing softwarewhich can aid analysts in draw-ing an HVM derived from thebinary matrix. This facility al-lows the analyst to quickly andeasily view several HVMs basedupon different cutoff points anddifferent coding judgments.Since an aesthetic component ofreadability affects the interpret-ability of the HVM, the softwareprovides several interactive capa-bilities to adjust the map andenhance its readability, such asmoving nodes about, cuttingand redrawing lines, or renam-ing nodes to be more representa-tive of the data.

In sum, the construction of anHVM from raw laddering datainvolves several stages of bothquantifiable and nonquantifiableanalysis. The major task ofbridging from qualitative toquantitative analysis lies in con-tent analysis of the subjects' ver-batim responses. Inherently, thisis a human judgment task butcan be greatly facilitated by theuse of interactive computer soft-ware. Furthermore, the use ofsoftware at this stage facilitatesthe subsequent quantitativestages of analysis, in particular,summarizing the frequency ofthe codes by subgroups withinthe total sample. Although thisfinal stage can be (and is) auto-mated to a great degree by theLADDERMAP software, this isnot a task which can be entirely

Journal ot ADVERTISING RESEARCH^IULY/AUGUST 1995 25

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Figure 3HVM for Canine Cuisine

FINANCIALRESPONSIBILITY

get the most from lifemore money for the family

LOVEfeel acceptedfeel worthwhile

COMPANIONSHIPman's best friendcamaraderieavoid loneliness

BELONGINGsecurityfriendship

GET JOB DONEcompletes work dog tasks

ENERGETICplayfulactive

FULFILLRESPONSIBILITY

SOCIALLYACCEPTABLE

people will like mecan join dog clubs

good ownerdoing the rightthing

GOODDISPOSITION

GOODAPPEARANCE

shiny coatbright eyes

PROLONGLIFE

more years with petless pain from toss

HEALTHY DOG

SAVES MONEYthriftydon't waste money

LESS WASTEuse all of iteasier storage

dog is healthyavoid health problems

NUTRITIONbalanced dieteats healthier food

EATS BETTEReats alt of his fooddoesn't leave any

DOG ENJOYSlikes his foodtasles better

CLEAN TEETHno bad breathfewer dental problems

DRY DIETARY

HIGH QUALITY superior productproduct quality

BRAND NAME MOIST FUVOR VARIETY

Variance accounted for: 83%

26 Journal of ADVERTISING RESEARCH—JULY/AUGUST 1995

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put through a black box to pro-duce useful results. Analystjudgment and decision-makingat all stages of the process isa critical component of theanalysis.

Interpretation andStrategic Use ofHierarchical Value Maps

The resultant HVM from ananalysis of the dog food categoryis shown in Figure 3, with ver-batim examples nested undereach code. {This HVM accountedfor 83 percent of all of the con-nections or associations in theraw laddering data, which werefer to as a measure of vari-ance.) Again, the HVM repre-sents the patterns of meaning bywhich individuals give personalrelevance to product distinc-tions. The thickness of the linesconnecting the concept nodessimilarly represents the varyingfrequencies of association.

The HVM can be divided intothree fairly distinct levels corre-sponding to the a/c/v codes. Theproduct attributes {Dry, Dietary,Name Brand, Moist, and FlavorVariety) are located at the lowerpart of the map. The conse-quences which are basically oftwo types, functional and psy-chosocial, represent the immedi-ate outcomes that the consumerperceives to result from the cor-responding attributes. In otherwords, the desired consequencesor outcomes are the immediate,tangible reasons a consumer at-taches importance to the attri-butes. The Values (Love, Belong-ing, Fulfill Responsibility, andFinancial Responsibility) placedat the top of the map representpersonally relevant goals or ob-jectives achieved by the lowerlevel consequences.

The connections between thenodes represent personal mean-ings. These links are actually the

key to understanding and usingan HVM. This is true for tworeasons. First, being able toidentify the connections betweenconcepts in the mind of the con-sumer is essential to understand-ing the perceptual basis for deci-sion-making. This represents thecardinal insights offered by anin-depth understanding of theconsumer. Second, once a posi-tioning strategy is determined,the creative task essentially in-volves developing words, im-ages, and/or symbols that willcreate the desired connections inthe mind of the consumer. Thus,focusing on the connections be-tween concepts is central to bothunderstanding and using ladder-ing research.

A common method for inter-preting laddering data (Reynoldsand Gutman, 1988) is to considerthe unique pathways of meaningfrom the attribute to the valuelevel as perceptual orientationsor perceptual segments. This isuseful, but also has its shortcom-ing. As a segmentation method,this approach is useful only ifthe analyst takes into accountthe method in which the HVMis constructed, namely, that allthe concept nodes in a pathwayneed not be included in the per-ceptual orientation. This is dueto the fact that the HVM is con-structed to include related "passthrough" nodes through theelimination of redundant con-nections, minimizing the num-ber of connecting lines required.To avoid this problem, one mustcheck the implication matrix tomake sure the unique pathwaysactually represent key definingelements which are significantlyinterconnected. This method ofdrawing an HVM assumes thatthose reading the map will natu-rally understand that a link fromconcept A to concept B and fromconcept B to concept C implies alink from concept A to conceptC, even if it is not expiidtiy

drawn. In most cases, drawingin these implicit connections willrender a map unreadable due toits complexity and multitude ofcrossing lines. If one is dealingwith a very simplistic knowledgestructure or coding scheme, or ifinterviewers failed to elicit fullladders from subjects, a mapincluding all connections maythen be feasible.

Alternatively, the implicationmatrix can be converted to a tri-angular distance matrix andused as input to a hierarchicalclustering algorithm (see Kle-nosky, Gengler, and Mulvey,1993 for an example). Differentattributes, consequences, andvalues are grouped together bythe analysis. The LADDERMAPprogram creates a file for thispurpose, which can be easilyused with any standard statisti-cal applications package. Each ofthese clusters could be viewedas a perceptual orientation and abasis for a psychographic seg-ment. An issue, then, is to as-sess which of these is the appro-priate target market for a givenbrand.

Making PositioningDecisions Based onMeans-End Pathways

Each of the perceptual orienta-tions discussed as segmentsshould be evaluated as a poten-tial product positioning. T'his isaccomplished by benchmarkingthe strengths and weaknesses ofthe respective products, using acombination of traditional atti-tude data and subjective judg-ment. The objective data pro-vides a sound basis for assessingthe lower attribute and func-tional consequence levels. Themore personal psychosocial con-sequences and value levels re-lated to the competing brands'positionings can usually be accu-rately assessed from their adver-tising communications.

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Combining the segmentationand the competitive positioninganalyses results in the strategicframework from which position-ing options can be developed.Basically, four options emerge.The first, and least likely, is dis-covering a significant yet un-tapped orientation, one that isnot currently being used in thecompetitive environment. Giventhe sophistication of today'smarketer, this is becoming in-creasingly less likely.

Option two involves ground-ing a positioning by establishingownership of a meaning, essen-tially creating a stronger link be-tween what is at present a rela-tively weak association. For ex-ample, in the HVM in Figure 3,the linkage between "dry" and"clean teeth" is seen to be weak,therefore, one positioning optionwould be to build a strong asso-ciation here, in the context of"healthy dog." "Healthy dog"would then need to be definedin terms of another, higher ordermeaning, like "prolong life."The net result would be a strate-gic positioning that communi-cates to the consumer that themeaning of "dry" —> "cleanteeth" is a discriminating charac-teristic to satisfy the higher or-der needs they have with re-spect to their dog.

Option three involves devel-oping new meanings, essentiallyforming a meaningful connectionbetween two as yet unrelatedconcepts. Again, using the HVMin Figure 3, one example wouldbe to connect "flavor variety" to"high quality," thereby tappinginto the strong (tightly con-nected) higher order meaningsthat stem from "high quality." Asimple example of this would beto create "flavor varieties" (atleast named as such) or descrip-tors that would commonly beconsidered or associated with asuperior cut of meat by humanstandards (i.e., filet, choice, or

tournedos). Of course, thehigher level association from"healthy dog" to the most ap-propriate values (given the com-petitive environment) must alsobe specified in the positioning.

The fourth option involvescreating a new meaning by add-ing a new attribute descriptor tothe consumer lexicon. One ex-ample of this type of positioningdevelopment would be to definea new attribute that couldreadily be associated with "nu-trition," given the central role itplays in the HVM. A possiblealternative would be a "medi-cally grounded supplement"such as a unique combination ofneeded vitamins and minerals,which could be easily linked tosuperior "nutrition" and ulti-mately reinforcing to more per-sonal value drivers at the higherlevels. An approach like thiscould offer significant potential ifthe specifics of canine nutritioncould be defined with a uniquecontrast to human dietary re-quirements, essentially creatinga new knowledge framework theconsumer could use to groundthe rational component of his orher decision-making.

As is apparent, the HVM of-fers more than consumer in-sight. It is a framework to con-trast current positionings and todevelop "what if" scenarioswhich ultimately can becomestrategic options. Similar to theskill required to construct a rep-resentative HVM, the develop-

. . . the HVM offers morethan consumer insight. It is

a framework to contrastcurrent positionings and todevelop ''what if" scenarioswhich ultimately can become

strategic options.

ment of strategy cannot be doneby a black box algorithm: it re-quires clear and oftentimes cre-ative thinking.

Strategy Translation

The specification of position-ing strategy based in a Means-End framework using the Model(see Figure 4) is well docu-mented in the academic litera-ture (Olson and Reynolds, 1983;Reynolds and Craddock, 1988;Reynolds and Gutman, 1984).However, to date, no specificshave been forthcoming on howto translate the specification intoa framework that creative staffcan use to develop executionalideas. The abstract nature of thecontent codes and the HVM,though grounded in consumermeanings, appears more like alogical set of connections be-tween rather simple, lifeless de-scriptors. The primary reasonunderlying this surface interpre-tation lies in the failure to ade-quately explain in detail the rele-vance of the concept of mean-ing. The first of two illustrationsof this inadequacy in both expla-nation and understanding aremade in the prior section, wheremeanings, defined as the con-nection between two conceptnodes, served as the basis forthe development of strategic op-tions. Understanding the criticalassociative aspect of meaningoffers significant potential tosolve the strategy-to-creativetranslation problem that cur-rently exists.

To illustrate, consider the "Su-per Premium" potential strategythat appears in Figure 5, whichcreates a new linkage between"flavor variety" and "high qual-ity." Note that the specificationhere is repeated on the far leftand the key strategic elementsare presented in the center sec-tion. This form of strategy speci-fication offered by the two left-

28 Journal ot ADVERTISING RESEARCH^JULY/AUGUST 1995

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Figure 4

MECCAS—Means-End Conceptualization of Components forAdvertising Strategy

DRIVING FORCEThe value orientation of the strategy;the end-level to be focused on in theadvertising.

LEVERAGE POINTThe manner by which the advertisingwill tap "into," reach, or activate thevalue or end-level of focus; the spe-cific key way in which the value islinked to the specific features in theadvertising.

CONSUMER BENEFITThe major positive consequences forthe consumer that are to be explicitlycommunicated, verbally or visually, inthe advertising.

EXECUTIONAL FRAMEWORKThe overall scenario or action plot,plus the details of the advertisingexecution. The executional frame-wof1< provides the "vehicle" by whichthe value orientation is to be commu-nicated, especially the Gestalt of theadvertisement; its overall tone andstyle.

MESSAGE ELEMENTSThe specific attributes, conse-quences, or features about the prod-uct that are communicated verbally orvisually.

Source: Reynolds, Thomas J., and Jonathan Gutman. "Advertising Is Image Management."Journal of Advertising Research 24,1(1984): 27-34.

most sections appears less thanbland to the insightful creative.What is missing, again, is theconcept of meaning; for it is thecreative goai to create meaningsthat will make the product per-sonally relevant to the consumer.

The simplicity and brevity ofstrategy specification in thismanner, though apparently lim-iting, actually has the potentialto serve the creative process ex-ceptionally well. Not only is itunrestrictive, it also provides aunique structure for ideation.

The focal point of this ideation isthe associative aspect of meaningbetween any two given concepts.

To develop meanings, onemust focus on the connectinglines between the concepts andexplore the possibilities thatmaximize the probability that thedesired meanings (connections)will result. This task directlyfeeds the creative process. Whatis required, then, is to developexecutional ideas, scenarios,symbols, and/or feelings thatwill cause the association of the

two concepts in the mind of theconsumer. Generation of ideasin this way can initially be ac-complished by answering thequestion: "What will cause theconnection to be made?" Onceideas are developed for each ofthe three key strategic connec-tions (see the right-most sectionof Figure 5 for rough examples),the blending of these ideas cantake place by creating specificscenes that serve to deliver thedesired meanings, or an overallexecutional action plot that em-bodies all of the key meanings.

The initial form of strategytranslation seen in Figure 5 rep-resents the basic underpinningsthat would create the desiredconnection. The goal is to gener-ate specific ideas thereby ex-panding the creative concept.For example, the "productbridge" linking the Message Ele-ments to the Consumer Benefitcould be enhanced by consider-ing product names that infer the"high quality" and the "flavorvariety" meaning. Using humanmeat labels such as choice orfilet might accomplish this. Inaddition, combining the productname with the visual of the petreally enjoying the special, andthereby superior, meal may cre-ate both of the desired sets ofconnections.

The "personal relevance"bridge between the ConsumerBenefit and the Leverage Pointcan be exemplified by demon-strating the good disposition ofthe pet, such as showing it play-ing with kids or being well disci-plined. Tying this idea into theexecution, either before and/orafter feeding, offers another ex-ample of how the strategic con-cept can be brought to life. Forthe "value bridge," connectingthe Leverage Point to the DrivingForce, a visual demonstration ofthe affection latent in the bondingof the pet and its owner seemslike an obvious executional idea.

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Figure 5

"Super Premium"

MECCAS

DRIVINGFORCE

STRATEGY

LOVE

COMPANIONSHIP ,

r ^GOODDISPOSITION

\ ^ HEALTHY DOG J

EATS BETTER

DOG ENJOYS

MESSAGE IELEMENTS I

HIGH QUALITY

FLAVOR VARIETY

MOIST

SPECIFICATIONOF MEANINGS(CONNECTIONS)

Value Bridge

Because he's good with others, youcan keep him with you. He's a betterfriend to you and he rewards you withhis selfless love.

Personal Relevance Bridge

Your dog eats better, so he's happyand friendly... and that means he'sgood with others, like playing withkids, grandparents, and other dogs.

Product Bridge

Your dog likes variety and freshnessjust like you do. For him, that's high-quatity, super premium dog food. . .fresh, moist, and meaty.

Clearly, the sample creativeideas presented are merely ex-amples limited by the lack oftime spent and real creative in-sight. However, these ideasserve to demonstrate how thecreative process can bring to lifethe strategy elements providedin a specification. The creativecontribution is obviously the ul-timate payoff—the tangible re-sult of the positioning strategyand has to be worked every bitas rigorously as the developmentof strategic options. It is abun-dantly clear, however, that thecreative output is intended tocommunicate meaning. Thinkingspecifically in these terms offerssignificant potential to focus thecreative process.

Figure 6 demonstrates how

another potential positioning,"Special Needs," can be devel-oped from the HVM. In this ex-ample, a new attribute is cre-ated, one that gives the con-sumer a rational reason forgrounding their decision-making(Reynolds, Cockle, and Rochon,1990). The strategic goal, then, isto provide the consumer with arational basis to believe the foodis more nutritious, and thus su-perior to the competition, whichis accomplished by building theappropriate meaning.

Continuing with this line ofreasoning, the absence of spe-cific dog-nutrition descriptors inthe HVM offers the possibility ofdefining what dog nutrition isand how it differs from humannutrition. The meaning of nutri-

tion, as defined by whatever"medically grounded supple-ments" can be delivered by theproduct, can then serve to posi-tively differentiate the brand.Once grounded, the scenariosneeded to convey the higher levelconnections can be developedsimilarly to the previous example.

As the two examples illustrate,understanding the HVM is thekey to both specifying strategicoptions and to translating theoptions into grist for creativedevelopment. The central tenetand primary contribution of thepoint of view offered here canbe summarized as this: ihe suc-cessful implementation of theMeans-End approach to strategy isthe realization that meaning is ev-erything. Positioning is aboutmeaning. Analysis of consumerperceptions of the reasons thatdrive decision-making behaviorshould be framed as a study ofmeaning. Therefore, the devel-opment of strategic communica-tions involves understandinghow visual and verbal elementscontribute to generate the de-sired meanings in the mind ofthe target consumer.

SummaryLaddering is one of the most

useful qualitative research tech-niques available to advertisingresearchers. It provides an op-portunity for consumers to re-spond to choice situations intheir own words and expresstheir own feelings, yet providesenough structure to keep theconversation focused exactly onwhat the consumer thinks aboutthe product category. The analy-sis of laddering data can be alaborious task, fraught with myr-iad classification decisions. Theanalytic tool described in thispaper provides a methodologyfor analyzing laddering data in amore-organized, less error-prone, and less opinionatedfashion. This is done without

30 Journal of ADVERTISING RESEARCH-JULY/AUGUST 1995

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Figure 6

'Special Needs"

MECCAS STRATEGY

DRIVINGFORCE

FULFILLRESPONSIBILITY

LEVERAGEPOINT

PROLONG LIFE

HEALTHY DOG

CONSUMERBENEFIT

NUTRITION

MESSAGEELEMENTS

SPECIFICATIONOF MEANINGS(CONNECTIONS)

Value Bridge

Your dog relies on you; hehas a special trust that youwill take good care of him;you deserve his trustbecause you give him a longand rewarding life.

Personal Relevance Bridge

Superior nutrition and aperfectly balanced meal helpkeep your dog healthy and fit,even when he's getting old.He can live a long, active lifewith you.

HIGH QUALITY

MEDICALLY-GROUNDEDSUPPLEMENT

Product Bridge

Your dog's needs are differentfrom yours. He needs thespecial, vital ingredients thatgo into high-quality dog foodfor complete, good nutrition.

suppressing the amount of infor-mation communicated to thosewho will eventually use the re-sults of the analysis. The resultsof qualitative research shouldnot be the biased opinion of theresearcher. They should not be aselected amusing vignette or twofrom the best communicators inthe sample interviewed. Theyshould represent all of the per-spectives of all of the individualsinterviewed. Only through acareful analysis process such aswe have discussed here can thisbe achieved.

Yet, even if an analysis hasyielded valuable insights intoconsumer or industrial buyerpsyche, these insights are worth-less if they are not put into ac-tion. The strategic statements

and positionings derived from aladdering study must be commu-nicated to creative staffs devel-oping advertising for the prod-uct. Furthermore, they must becommunicated in a frameworkthat stimulates creative ideationaround the chosen positioningrather than restricting the cre-ative staff to an overly specified

. . . even if an analysis hasyielded valuable insights intoconsumer or industrial buyer

psyche, these insights areworthless if they are not put

into action.

message content. Such an over-restriction can be a fantastic for-mula for dry, unexciting adver-tising. For this reason, we haveoutlined how strategies derivedfrom a laddering study can besuccessfully used as a source ofideas for creative staffs. Findingnew ways to translate a prod-uct's tangible features into cus-tomers' key benefits, or to trans-late benefits into personally rele-vant feelings and values, is vitalto creating advertising which isexciting and cohesive with abrand's chosen positioning. Thefocus of any communication withcustomers must be on the lastingproduct/brand related meaningsformed in the customer's mem-ory. This focus will not onlyhelp to build messages that con-tribute to a stronger brand imageand positioning but will alsohelp to preempt the creation ofmessages which, in isolation,may be "good ads" but in a ho-listic perspective dilute the posi-tioning of the brand and confusethe brandimage. •

CHARLES E. GENGLER is assistant profes-sor of marketing at Rutgers University inCamden, NJ His research focuses on im-provements in laddering data collection,analysis, and testing ot advertising accord-ing to the Means-End paradigm He hasperformed consulting work and given execu-tive development seminars on marketingand positioning strategies in the banking,pharmaceutical, casino, and resort indus-tries Recently, he completed a stay withGrey Advertising in New York through theAdvertising Education Foundation s VisitingProfessor Program

THOMAS J. REYNOLDS is president andchief executive officer of Wirthiin-Reynoids,a marketing and communications strategyconsulting company He is also currently aparfner and chief marketing strategist forRichmont Corporation, a private merchantbank. He has served as a professor in theGraduate School of Management and asDirector of Marketing Studies at the Univer-sity of Texas at Dallas where he was re-cently named Professor Emeritus He haspublished more than 50 articles on the de-velopment and implementation of values-based strategy. As a consultant he hasworked with over 40 corporations in over 20countries operating on marketing strategyproblems including such companies asProcter & Gamble. Coca-Cola, AT&T, Intel,and Mary Kay Cosmetics.

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