CONJOINT ANALYSIS and Other Stats Methods

8/16/2019 CONJOINT ANALYSIS and Other Stats Methods

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CONJOINT ANALYSIS

Why Use Choice-Based Conjoint Analysis? Choice-based conjoint analysis,also known as discrete choice modeling or choice modeling, has severaladvantages that make it appropriate for use in market research.

1. First, it offers a more realistic shopping simulation -- participants are asked tochoose between carefully chosen sets of complete products. They are forced,unknowingly, to show the trade-offs they make between attributesfeatures.This is much more realistic than the most common forms of ratings-based orpreference-based conjoint analysis e!ercises. " paired comparisonsapproach #one form of preference-based conjoint$ has participants maketradeoffs between small subsets of all attributes and a full profile approachhas participants apply specific ratings or rankings to one product descriptionat a time.

%. &econd, it enables us to derive the importance participants must be

associating with different levels of the various product attributesfeaturesbased on the choices they make. 'esearch has shown that this approachproduces more realistic estimates of feature importance.

(. Third, research has shown that the importance of attributes such as pricetend to be understated in traditional, self-e!plicated importance ratinge!ercises that ask respondents to rate the importance of individual attributesin isolation from others rather than in combination with others #conjointly$ aswe do in the real marketplace.

). Fourth, choice-based conjoint analysis e!ercises allow participants toindicate that they would not purchase any of the alternatives presented. Thisfurther enhances the realism of the e!ercise because many productsservices

are not necessities, thus consumers can choose to own none of the availableitems. The relative value or utility of the *none of these* option can be usedas a threshold that must be met by any new productservice before that itemwill be accepted by consumers.

+. Finally, we use choice-based conjoint analysis, rather than older preference-based methods because it is more efficient in the number of judgements thatrespondents are reuired to make and more realistic in that it asks targetcustomers to make simulated purchase decisions rather than indications ofpreference.


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&/0T"T20

What is Maret Se!"entation? /arket segmentation describes the division ofa market into homogeneous groups which will respond differently to promotions,communications, advertising and other marketing mi! variables. ach group, or*segment*, can be targeted by a different marketing mi! because the segmentsare created to minimi3e inherent differences between respondents within eachsegment and ma!imi3e differences between each segment.

/arket segmentation was first described in the 14+56s, when productdifferentiation was the primary marketing strategy used. n the 14756s and14856s, market segmentation began to take off as a means of e!panding salesand obtaining competitive advantages. n the 14456s, target or direct marketersuse many sophisticated techniues, including market segmentation, to reachpotential buyers with the most customi3ed offering possible.

Why Use Maret Se!"entation? There are many good reasons for dividing a

market into smaller segments. The primary reasons9

1. Easier marketing. t is easier to address the needs of smaller groups ofcustomers, particularly if they have many characteristics in common #e.g.seek the same benefits, same age, gender, etc.$.

%. Find niches. dentify under-served or un-served markets. :sing *nichemarketing*, segmentation can allow a new company or new product to targetless contested buyers and help a mature product seek new buyers.

(. Efficient. /ore efficient use of marketing resources by focusing on the bestsegments for your offering - product, price, promotion, and place#distribution$. &egmentation can help you avoid sending the wrong message

or sending your message to the wrong people.When #o Yo$ Use Maret Se!"entation? "ny time you suspect there aresignificant, measurable differences in your market, you should consider marketsegmentation. dentified segments must be9

1. Big enough. /arket must be large enough to warrant segmenting. ;on6t try tosplit a market that is already very small.

%. Different. ;ifferences must e!ist between members of the market and thesedifferences must be measurable through traditional data collectionapproaches #i.e., surveys$.

(. Responsive. 2nce the market is segmented, you must be able to design

marketing communications that address the needs of the desired segments.f you can6t develop promotions and advertising that speak to each segment,there is little value in knowing that those segments e!ist.

). Reachable. ach segment must be reachable through one or more media.


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+. Interested in different benefits. &egments must not only differ on demographicand psychographic characteristics, they must also differ on the benefitssought from the product. f everyone ultimately wants the same things fromyour product, there is no reason to segment buyers. =owever, this is seldomthe case. ven commodities like sugar and paper plates can benefit fromsegmentation.

>. Profitable. The e!pected profits from e!panding your markets and moreeffectively reaching buyer segments must e!ceed the costs of developingmultiple marketing programs, re-designing e!isting products andor creatingnew products to reach those segments.

%o& #o Yo$ Se!"ent a Maret? There are two basic ways to segment amarket9

A priori. " priori segmentation involves dividing a market into segments withoutthe benefit of primary market research. /anager intuition, analysis of secondarydata sources, analysis of internal customer databases or other methods are

used to group people into various segments. ?revious *post hoc* segmentationstudies are considered to be *a priori* when applied to the same markets atsome point in the future. &ome e!amples of a priori segmentation schemes are9

• =eavy versus moderate and light users.

• /en versus women.

•


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• ;emographic variables - "ge, gender, income, ethnicity, marital status,education, occupation, household si3e, length of residence, type ofresidence, etc.

• eographic variables - City, state, 3ip code, census tract, county,region, metropolitan or rural location, population density, climate, etc.

• ?sychographic variables - "ttitudes, lifestyle, hobbies, risk aversion,personality traits, leadership traits, maga3ine read, televisionprograms watched, ?'B/ clusters, etc.

• ehavioral variables - rand loyalty, usage level, benefits sought,distribution channels used, reaction to marketing factors, etc.

Descriptor variables.

;escriptors are used to describe each segment and distinguish one group fromthe others. ;escriptor variables must be easily obtainable measures or linkableto easily obtainable measures that e!ist in or can be appended to customer files.

/any of the classification variables can be considered descriptor variables.=owever, only a small portion of those classificationdescriptor variables arereadily available from secondary sources. The trick is to identify descriptorvariables that effectively segment the market in the primary research effort whichare also available or can be appended to individual customer records incustomer databases. This permits us to operationali3e the market segmentationscheme developed in the primary research effort by applying it to e!istingcustomer and market information. ;&& utili3es a number of proprietaryprocedures to achieve this important linkage. =ere are some e!amples ofdescriptor variables9

• Census bureau demographic characteristics.

• Third-party classification variables like ?'B/ or @"A&.

• eographic characteristics or regions.

• ?anel data and scanner data on buying habits and usage levels.

• Customer data collected by companies for internal use.

What Analytical Techni($es Are Used to Se!"ent a Maret? /ostmultivariate analytical techniues can be used and probably have been used insome way to create post hoc market segments. There is no ideal methodologythat works with every segmentation study. ach methodology has advantages

and disadvantages. &egmentation studies generally reuire the use of two ormore methodologies to produce the best results. n nearly every case, multipletechniues should be tested before selecting the *best* solution. There are (categories of analytical techniues applied to market segmentation9 datapreparation, data analysis, and classification. The most common techniues foreach category are9

#ata )re*aration


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Factor analysis.Correspondence analysis.Conjoint analysis.

#ata Analysis

Cluster analysis.

Chi-suare "utomatic nteraction ;etection #C=";$ or Classification and'egression Trees #C"'T$.

"rtificial neural networks.Aatent class structure models.

Classi'ication

;iscriminant analysis./ultiple regression./ultivariate logit./ultidimensional scaling #/;&$.

ach of these analytical techniues, as well as other techniues not listed, can

be applied to survey data to produce market segments. elow, we brieflydescribe how they are often used in segmentation studies.

#ata )re*aration 0umerous techniues can be used to aide the segmentationprocess. Factor analysis can reduce the number of variables to a moremanageable si3e while also removing correlations between each variable. Thecoordinates produced by correspondence analysis, when calculated at theindividual or group level, can be clustered to produce market segments.Correspondence analysis can also be used to convert nominal data #like yesnoanswers$ to metric scales. :tilities from conjoint analyses can be used insegmentation because they represent the relative value individuals6 place on all

key attributes that define a product or service. n fact, conjoint utilities representthe most effective basis variables because they are derived from respondentpreferences between product options or from actual choices of preferredproducts.

#ata Analysis - Cl$ster Analysis Cluster analysis is the most freuently usedmethod of segmenting a market. The underlying definition of cluster analysisprocedures mimic the goals of market segmentation9 to identify groups ofrespondents in a manner that minimi3es differences between members of eachgroup while ma!imi3ing differences between members of a group and those in allother groups. =owever, there is one key difference between clustering andsegmenting respondents -- clusters produce groups of respondents who have

similar responses on key variables while segmentation finds groups ofrespondents who have similar behaviors when purchasing and seeking productsin the market. oth hierarchical and iterative cluster analysis procedures can beused, but hierarchical procedures are difficult to evaluate once you e!ceed 155or %55 survey respondents. "mong the various iterative cluster analysisprocedures, the D-/eans method is most often used. D-/eans cluster analysiscan be found in all of the most popular statistical programs #&"&, &?&&, /;?,&tatistica, &


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#ata Analysis - C%AI# and CA+T C="; and C"'T are known as*Classification Tree /ethods.* These methods divide respondents into groupsand then further divide each group into subgroups based on relationshipsbetween segmenting basis variables and some dependent variable. Thedependent variable is usually a key indicator such as usage level, purchaseintent, etc. These procedures create tree diagrams, starting at the top with allrespondents combined and then branching into % or more groups at each newlevel of the tree. &ubdivisions are determined by finding the survey variable thatproduces the greatest difference in the dependent variable among individualresponse categories or groups of response categories on that survey variable.C="; is the most commonly used classification tree method, but it can nothandle continuous dependent variables so a combination of C="; and C"'T issometimes used. oth C="; and C"'T have the ability to process non-metricand non-ordinal data. :nlike cluster analysis, classification tree methods createtrue segments when they divide respondents. =owever, these segments are onlybased on one dependent variable. 2ther methods, including cluster analysis,

divide respondents based on 156s or even 1556s of data elements.

#ata Analysis - Arti'icial Ne$ral Net&ors "rtificial 0eural 0etworks or "00soffer another means to segment respondents. The Dohonnen architecture is oneself-organi3ing "00 that can be used for segmentation. t is called self-organi3ing because, like cluster analysis, there is no dependent variablespecified in the model. The "00 attempts to group respondents based on theirsimilarities. t differs from cluster analysis in its ability to ignore noisy data.

"typical individuals have less impact on the segmenting calculations and eachsuccessive iteration makes ever smaller adjustments to the network weights sothe calculations uickly stabili3e, ignoring infreuent respondent characteristics.

The greater the variation or uncertainty in respondents6 answers, the better "00s perform compared to cluster analysis.

#ata Analysis - Latent Class Str$ct$res Aatent class analysis is oftendescribed as *factor analysis for categorical variables.* t is used to findunderlying constructs within sets of variables. =owever, latent class analysis canalso be used to cluster categorical variables into segments based on responsesacross a broad range of categorical variables. Aatent classes attempt to find theunderlying constructs which motivate people to buy a particular product or todesire certain features in that product.

Classi'ication Al!orith"s There are a number of classification algorithms oranalytical methods which can be applied to market segmentation. ;iscriminantanalysis can be used to classify respondents into predefined segments based ondescriptor variables like census data. The segmentation scheme determineswhich respondents belong in each market segment. The classification or scoringprogram then creates the means of identifying potential members of eachsegment based on limited information #usually data which can be obtained fromsecondary sources$. Ehen a limited set of information can be used to accuratelypredict which market segment each individual belongs, you have a successful


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classification algorithm. /ultiple regression and multinomial logit can be used inthe same manner to create classification schemes for your market segments.

%o& Many Se!"ents Sho$ld I %a,e? :nfortunately, there is no definitiveanswer. !perience, intuition, statistical results and common sense all must beapplied to decide on the number of segments to retain. f you have several verysmall segments, you may need to change the criteria for segmentation orremove some of these respondents as outliers. Too many segments can lead todeveloping many different marketing programs for small, very similar, markets.=ere a few rules of thumb for segmentation9

1. Large enough. /ajority of segments must be large enough to beeconomically feasible to target marketing and product design efforts.

%. Relevant. The segments must be relevant to your company6sproductsservices.

(. Reachable. &egments must be reachable through one or more marketing mi!variables #price, promotion, features or distribution$.

). Different. There must be clearly defined differences between marketsegments to make some segments more desirable than others. f many of thesegments want essentially the same features and intend to buy at the samefreuency or volume level, then these segments do not e!hibit meaningfuldifferences.

%o& M$ch #oes Maret Se!"entation Cost? ecause the amount ofinformation that must be collected and the detailed analyses that must beconducted to identify segments, market segmentation is one of the moree!pensive research projects you will consider. Telephone surveys of more than(5 minutes are common and multi-phase projects using combinations of

telephone and mail surveys are the norm when collecting data for marketsegmentation.

"lthough e!pensive, very few research projects can have the long term impactthat market segmentation can produce. This research methodology provides theinformation necessary to identify new markets, redesign marketing programs andincrease profitability. Few research projects can achieve more than one of thosegoals.


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)LACMNT O. /USTIONS

Is it 0etter to 0e 'irst or last? " common scenario in moving from onesatisfaction program to another involves the rearrangement of survey uestions.The same uestions may be asked, but their order is often changed. The moste!treme case of this involves moving a uestion from the very beginning to thevery end of a survey and vice-versa.

Ee used ) different customer satisfaction surveys to test the effects of movinguestions from the end to the beginning of the instrument. "ll ) surveys involvehealthcare companies.

• &tudy 1 -- 11 general satisfaction surveys and 1 overall satisfaction surveywere moved from the end of a survey #just before respondent demographics$to the beginning of the survey #immediately following introduction andscreener uestions$. ?atients who spent one or more nights in a hospitalwere surveyed.

• &tudy % -- The overall satisfaction uestions was moved from the beginningto the end of the survey. This study also involved patients who had spent atleast one night in a hospital.

• &tudy ( and &tudy ) -- The same as &tudy %, but involve different clientsand different respondent types #outpatientclinic visitors G&tudy (H, andemergency room visitors G&tudy )H$.

The Best )re'er 1oin! Last

• nterestingly, average ratings for the overall satisfaction uestions aresignificantly higher when asked at the end of a survey than they are at the

beginning of the survey, e!cept in one instance.• mergency room patients, which traditionally have much lower levels of

satisfaction than hospital inpatients or outpatients, recorded even lowerlevels of overall satisfaction when the uestions appear at the end of thesurvey.

• &pecific satisfaction items, as opposed to overall satisfaction uestions,show little difference whether they are asked at the beginning or end of thesurvey.

Concl$sions Ee believe the above observations are due to the *reinforcingeffect* the survey process has on respondents6 collective memories. ?ositive

e!periences are positively reinforced by the survey as respondents are asked toreflect on individual aspects of their e!perience which may have lost someimpact during the time that passed since their visit #respondents were usuallycontacted ) to > weeks after their visit$. Therefore, at the end of the survey, evenmore positive thoughts are generated about the e!perience than appeared at thebeginning of the survey. Conversely, negative e!periences are negativelyreinforced through the same process of reminding respondents of individual


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aspects that may have been forgotten. y the end of the survey, theserespondents have lowered their overall opinion of the e!perience.MA+2T +SA+C%

What is Maret +esearch? First, a brief description of what constitutes marketresearch. /arket research is the collection and analysis of data for the purposeof descision making. /arket research is used to describe e!isting marketconditions, e!plain certain market behaviors, and predict how consumers mightrespond to new products and changes in marketing mi!es.

Why Use Maret +esearch? The more important the decision or decisions tobe made, the greater the value of market research to help guide those decisions.elow is a list of reasons why market research can be invaluable9

• Ehen the costs of making a wrong decision far outweigh the costs of usingmarket research to confirm or dispel managers6 beliefs.

•


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• The research costs e!ceed the e!pected benefits. f there is little confidencein the value of the potential research or if the costs to enter a new market aresmall, there is no reason to research the decision.

• f you are not sure what information is needed or what research uestionsneed to be asked, don6t conduct any research until your objectives are clear.

• Can you effectively use the researchJ t might be interesting to know thepsychological makeup of left-handed golfers who bat right-handed, however,if you can6t use the information to sell more product or improve customerservice, its not worth spending money on.

Ee hope this sheds a little light on the pros and cons of market research.;espite our obvious biases, there are thousands of case studies and numeroussatisfied users of market research which can attest to the value of research.


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CONJOINT ANALYSIS #TAILS

What is Conjoint? Conjoint analysis is one of the terms used to describe abroad range of techniues for estimating the value people place on the attributesor features which define products and services. ;iscrete Choice, Choice/odeling, =ierarchical Choice, Card &orts, Tradeoff /atrices, ?reference asedConjoint and ?airwise Comparisons are some of the names used for variousforms of conjoint analysis.

The goal of any conjoint survey is to assign specific values to the range ofoptions buyers consider when making a purchase decision. "rmed with thisknowledge, marketers can focus on the most important features of products orservices and design messages most likely to strike a cord with target buyers.

Why Use Conjoint? 2k, so why should you pay for a conjoint analysis survey.For starters, conjoint analysis evaluates productservice attributes in a way thatno other method can. Traditional survey approaches ask respondents to

estimate how much value they place on each attribute. This is a very difficult taskfor any person to complete, much less someone who doesn6t spend everywaking moment thinking about the most important features of products such astoothpaste or wood deck treatments. Conjoint analysis, on the other hand,attempts to break the task into a series of choices or ratings. These choices orratings, when taken together, allow us to compute the relative importance ofeach of the attributes studied. nstead of *stated importance*, conjoint analysisuses *derived importance* values for each attribute or feature.

"nother of the advantages of conjoint analysis is the ability to use the results todevelop market simulation models that can be used well into the future. /arkets

continue to change as new competitors enter , new products are introduced,price wars erupt and marketers develop new advertising programs. Eithtraditional research approaches, every time a major change takes place in themarket, a new survey needs to be conducted to find out how people feel aboutthe changes and how it will affect their purchases. Eith conjoint analysis, thenew product or changes to e!isting products can be incorporated into thesimulation model to obtain predictions of how buyers will respond to thechanges. n most markets, these models can maintain their accuracy for two orthree years before you need to conduct a mini-version of the original study todetermine if any adjustments must be made to the model.

%o& #oes It Wor? ;epending upon the type of conjoint survey conducted,statistical methods like ordinary least suares regression, weighted leastsuares regression, and logit analysis are used to translate respondents6answers into importance values or utilities. 'egardless of the statisticalmethodologies used, conjoint analysis results have withstood intense scrutinyfrom both academics and professional researchers during the past %+ years.

The actual values obtained by these statistical methods are not important, onlythe relative values or relationships between each of the attributes are needed.


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The goal of these calculations is to evaluate respondents6 answers in a mannerthat reveals the underlying value they consciously or sub-consciously place oneach attribute. "ny rational person will prefer a K155 price over a K%55 price, ifall other things are eual #uality, features, etc.$. Ehat we do not know abouteach person is their level of sensitivity to the K155 price difference. &ome mightnever consider paying K%55 for the product, while others are nearly indifferent tothe price difference. " person who always chooses rand L over rand


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K>5,555. t would be just as bad to show only % price levels #K15,555 andK>5,555$ for those same car purchases because the price differential would beunrealistic for a typical respondent.

"ttribute levels must encompass all of the products that e!ist in your market oryou e!pect to e!ist within the near future. For continuous variables like price, (or ) price levels can cover the market from low price leader to premium product.For discontinuous attributes, ( to + levels are typically specified so somesacrifices may have to be made to eliminate the least desirable or leastimportant options.

The critical factor in specifying attributes and attribute levels is that a productcan not be accurately simulated if you can not define it reasonably well using theattribute levels chosen for the conjoint survey. f an option is not included in theconjoint survey and it does not fall within the boundaries of any two attributelevels which are specified, you will not have any information on how respondentsreact to that attribute level. Eithout this missing piece of information, the

importance of that attribute or attribute level relative to all of the other items inthe survey is unknown and can not be accounted for in the simulation model.

%o& #o I #eter"ine the O,erall 3al$e O' A )rod$ct? 2nce you have theutilities for each attribute level, you calculate a product6s value by summing theutilities across all the attributes which define that product. For each attribute, youselect the attribute level most closely associated with the product and note it6sutility. f a particular product falls between two levels of an attribute #e.g. yourproduct6s price is K1+5 and the price levels are K155 and K%55$, you interpolatethe specific utility for your product.


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survey instruments because respondents have difficulty stated what benefitsthey value most.

Which Conjoint Methodolo!y is Best? "s with most things related to marketresearch, the answer depends upon the circumstances. Choice-based Conjointor ;iscrete Choice /odeling has become the most popular methodology overthe last ) or + years, however, each method has advantages and disadvantages.

"dvantages of Choice-ased Conjoint;iscrete Choice /odelingChoice/odeling9

1. /ore closely resembles the decision process customers make in the marketplace where they look at all the alternatives available and pick the one theymost prefer. t is believed, though difficult to prove, that the more closely aresearch task mimics real behavior the more valid and reliable the results.

%. "llows respondent to choose *none of these.* n most purchase decisions,one of the alternatives is to walk away without buying anything. Choice-

based conjoint allows you to include this response in the model and accountfor it in the calculation of utilities.

(. /ore productservice profiles are seen by each survey respondent becausechoice-based conjoint typically presents ( or more alternatives in eachchoice set.

). asier to calculate attribute interactions like price and brand. ased onaggregate level analyses, attribute interactions can be included withoutdramatically increasing the comple!ity of the research design for choice-based conjoint e!ercises.

"dvantages of Traditional Conjoint?reference-based Conjoint'atings-based

Conjoint9

1. Can break large sets of attributes into smaller bundles for analysis. ?airwisecomparisons are one e!ample where respondents are only asked to indicatetheir preferences between sets of % to ) attributes. 2ne set of attribute levelsappears on the left and another set of levels for those same attributesappears on the right. This is much easier than evaluating 1+ or %5 attributessimultaneously.

%. Calculates utilities at the individual respondent level. "lthough newtechniues have recently been developed for calculating individual levelutilities for choice-based conjoint, this has always been done for traditionalconjoint. ndividual level utilities have a long history of success undertraditional conjoint approaches.

(. &traightforward e!perimental designs. Traditional conjoint studies need onlyto generate an orthogonal set of product profiles to complete the researchproject. Choice-based conjoint surveys must create the orthogonal productprofiles and then create additional profiles for all of the alternatives in eachchoice set in a manner that balances the relative desirability of eachalternative in a choice set and uses each attribute level uniformly throughoutthe survey.


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). ndividual level utilities make it easier to conduct market segmentation.+. asily used in hybrid methodologies. Traditional conjoint can be used to

focus strictly on product features, irrespective of price or brand name. Thisallows you to calculate utilities for each respondent based on specificfeatures. Choice based conjoint can then be used to focus on brand, priceand key bundles of features.

What A0o$t Brand ($ity and Brand I"a!e? Conjoint analysis is very good atmeasuring the value of brand names in relation to competing brands. :nlikemost other techniues for measuring brand euity, with conjoint analysis, youalso obtain information on how strong a brand is in comparison to specificproduct features and prices. =aving a dominate brand name may not be enoughif most of your market is so price sensitive or they desire a particular set offeatures enough to easily offset your investment in brand euity. Eith conjointanalysis, you can estimate how your market makes these tradeoffs betweenbrands, price and specific features. For more information, see=9N&andeepNE=NranduityN/ethodology.asp or

=9N&andeepNE=NranduityNunderstanding.asp.

What A0o$t Meas$rin! )rice Sensiti,ity? "s mentioned above, conjointanalysis can be used to measure individuals6 sensitivity to brand names, prices,and all other attributes in the research design. The utilities for each price leveloffer one measure of a market6s or market segment6s sensitivity to variations inprice. Ehen interactions between price and other attributes are calculated, youcan also measure how price sensitivity may vary with respect to brand name #astrong brand image usually has much less price sensitivity than an unknownbrand$ and other attributes. &imulations can be run at various price points toestimate changes in yours or your competitor6s prices on the marketplace.

Can Conjoint Be Used 'or Maret Se!"entation?


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SATIS.ACTION MO#LIN1

;&& has developed a standardi3ed approach to analy3ing customer satisfactionresults which helps managers convert &atisfaction results into "ctionableinformation. SatisAction5 Modelin! "s a way of making the results of thesestudies more actionable, we offer our proprietary &atis"ctionO statisticalmodeling system as a project option. &atis"ctionO results allow you to9

• dentify drivers of customer satisfaction and their relative importance.

SatisAction5 is Based On6 6 6

• !tensive &tatistical /odeling. The &atis"ctionO statistical modelingapproach uses regression, factor analysis and logit modeling to determinethe relative importance of different satisfaction items to customers.

• ?ositioning in /aps. The primary benefit of the &atis"ctionO modelingprocess is the ability to position different satisfaction items in uadrant maps.The maps and the interpretation of the maps are described in the followingsection.

SatisAction5 +es$lts The &atis"ctionO modeling process produces auadrant matri! used to classify individual satisfaction items #for e!ample,responsiveness of customer service reps$. ssentially, &atis"ctionO produces agraphical plot #called a classification matri!$ of your organi3ation6s perceivedperformance on attributes versus the derived importance of these attributes.Classification /atrices. 2ur ?2Ee' classification matrices are divided intouadrants. Ee interpret the uadrants as follows9

?ower. tems that are =igh in mportance to respondents and your organi3ation6s?erformance is =igh. #upper right uadrant$2pportunity. tems that are @ery =igh in mportance to respondents, but yourorgani3ation6s ?erformance is Aow. #lower right uadrant$Eait. tems that are =igh in mportance to respondents, but your organi3ation6s?erformance is Aow. #lower left uadrant$'etain. tems that are @ery =igh in mportance to respondents, but yourorgani3ation6s ?erformance is =igh. #upper left uadrant$

"ttention. tems mapped in the 2pportunity and Eait uadrants, whereperceived performance is Aow, demand the most immediate attention.;ifferentiation. tems appearing in the ?ower and 'etain uadrants can be used

as points of differentiation from your competitors. These are areas where yourperformance is highest.


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B+AN# /UITY

What is Brand ($ity? There are many different definitions of rand uity, butthey do have several factors in common9

Monetary 3al$e6 The amount of additional income e!pected from a brandedproduct over and above what might be e!pected from an identical, butunbranded product. For e!ample, grocery stores freuently sell unbrandedversions of name brand products. The branded and unbranded products areproduced by the same companies, but they carry a generic brand or store brandlabel like Droger6s or "lbertson6s. &tore brands sell for significantly less thantheir name brand counterparts, even when the contents are identical. This pricedifferential is the monetary value of the brand name.

Intan!i0le6 The intangible value associated with a product that can not beaccounted for by price or features. 0ike has created many intangible benefits for their athletic products by associating them with star athletes. Children and adults

want to wear 0ike6s products to feel some association with these star athletes#*be like /ike* - /ichael Pordan for non-sports fan$. t is not the physicalfeatures that drive demand for their products, but the marketing image that hasbeen created. uyers are willing to pay e!tremely high price premiums overlesser known brands which may offer the same, or better, product uality andfeatures.

)ercei,ed /$ality6 The overall perceptions of uality and image attributed to aproduct, independent of its physical features. /ercedes and /E haveestablished their brand names as synonymous with high-uality, lu!uriousautomobiles.


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to promote a new brand, and significantly lower awareness and trial rates fortheir new brand.

%o& #o I Use Brand ($ity to My Ad,anta!e? rand uity can providestrategic advantages to your company in many ways9

1. "llow you to charge a price premium compared to competitors with lessbrand euity.

%. &trong brand names simplify the decision process for low-cost and non-essential products.

(. rand name can give comfort to buyers unsure of their decision by reducingtheir perceived risk.

). /aintain higher awareness of your products.+. :se as leverage when introducing new products.>. 2ften interpreted as an indicator of uality.7. =igh rand uity makes sure your products are included in most consumers

consideration set.

8.


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Once We %a,e the Brand ($ity #ata7 What #o We #o With It? /arketsimulations are one means of putting rand uity information to use. :singestimated utilities, we can simulate market preferences for our products andthose of the competition. @arious scenarios can be created which involve theintroduction of new products or modifications to e!isting products to determinethe effects of these changes on market preferences. This information can beused to test9

• !tension of product lines with and without the use of an e!isting brandname.

• ntroduction of new products with and without brand name affiliation.

• stimate the monetary value of each brand. :sing the conjoint utilities, youcalculate the price differential that your brand could charge versus yourcompetitors if all companies offered the same features.

• ;etermine the effects of improving rand uity or reducing your investmentin a high-euity rand.

• stimate the impact of moving into new geographic areas where your brandname is unknown or has negative perceptions.

• stimate the effects of co-branding with a company who has more or lessrand uity than does your brand.

• Track rand uity over time for your company and your competitors. &topslides in your own rand uity before it can severely impact your companyand adjust your strategy to counteract changes in competitors6 rand uity.

• /easure effectiveness of your advertising and marketing campaigns to buildbrand image.

#oes Brand ($ity 3ary Across C$sto"ers? rand uity does vary across

individuals, as we would e!pect, and we can measure these differences. Thedata collected in rand uity studies can also be used to segment the marketinto various groups based on the benefits they seek. :sing the utility estimatesfrom the conjoint models, we can identify benefit segments in your market. Thesesegments can then be compared to each other to highlight differences in randuity between various types of product users, different levels of price-sensitivity, different levels of feature importance. ;emographic andpsychographic profiles of these benefit segments can ultimately be used totarget specific advertising messages to groups of potential purchasers based onthe desires of those groups. Coupons might be sent to those in the most price-sensitive segments, while detailed product literature might be sent to those who

place more value on specific features of your products.


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ONLIN +SA+C%

The 1ood 2nline survey research has several advantages over more traditionalforms of market research like telephone and mail surveys9

1. Aeast e!pensive research methodology. For a given sample si3e, onlinesurveys or nternet surveys can be e!ecuted for less costs than anytraditional forms of research. &ome reasons for the costs savings are9

0o outgoing or return postage costs which increase with sample si3e. 0o paper,staples, envelopes or other materials needed.

%. "nswering machines, caller ;, call blocking and no answers do not preventdelivery of the survey reuest.

(. &urveys can interact with users without the e!pense of computer assistedinterviewing or disk-by-mail approaches.

). "ccess to high-income, high-tech, professionals. These, and other businesspeople who are normally difficult to identify and reach via othermethodologies.

+. 'each early adopters of new products and new technologies. etting theopinions #and possibly approval$ of these valuable people can be veryhelpful in gauging the potential success of new products and services.

>. Faster turnarounds possible. nstantaneous electronic distribution of surveymaterials and electonic return of completed surveys give the fastest possible

opportunity for responses. Eith pre-recruited online panels, surveys can bedistributed and returned in hours, compared to days for telephone and weeksfor mail surveys.

7. nteractive surveys are now possible. Pava applets, Pava&cript, @&cript and "ctiveL technologies are making truely interactive surveys possible.:pcoming uestions can be constructed based on responses to previousuestions.

8. ?referred customer and intranet uses. 2nline surveys lend themselves toobtaining uick feedback from clients with whom you have a closerelationship and your own employees who might be scattered over manydepartments or throughout the world.

The Bad very survey methodology has its weaknesses, and online surveys areno e!ception.

• 0ot sure who is answering the survey. Aike mail studies, it is difficult to insurethe desired person actually answers the survey.


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• 0ot representative of :.&. population. Today, the nternet population ispredominantly high income males and young, computer-literateprofessionals.

• Aong surveys are more difficult. The personalities of today6s online usersmakes it difficult to coerce respondents into completing long surveys.

• 0ot a *mainstream* research methodology, yet. Eith more e!perience andcomparative analyses of online surveys versus mail and telephone results,online surveys should reach the same level of acceptance as othermethodologies.

• ;ifficult to pay incentives online. :ntil some form of *electronic-cash*becomes widely used, it will be difficult to directly credit respondents for theirparticipation. Currently, incentives have to be mailed to respondents or someform of lottery is used to dispense gifts.

The U!ly &ome problems with online surveys go beyond their

representativeness or their accuracy and reliability.

• mail surveys can be modified. mail surveys are sent as te!t files to befilled in by receipients. This allows receipients to change the wording touestions or responses and adddelete uestions as they desire. :nlike mailsurveys, where respondents have the same opportunities, changes to mailsurveys are very visible.

• mail Flames. 2nline users have developed a culture where unsolicitedmessages, regardless of the purpose, are loudly denounced. t is very easy,and uite common, for email receipients to fire off nasty responses #*flames*$to the surveyor or even post the surveyor6s address for others to flame as

well.• Aetter ombs. /ore proficient and disgruntled receipients may resort to letter

bombs or other tactics which can replicate or e!pand within the surveyors6mail server, causing the system to crash. The prevents any legitimate surveysfrom getting through until the letter bomb is removed and the email server isrestarted.

Concl$sions ;espite some definite problems, online research has many moreadvantages than disadvantages. Ehen surveying populations which mimic thedemographics of online users, online research can be a primary means ofinformation collection. Eith other populations, online research can be effectiveas a supplement to more traditional survey approaches or as a uick impression

of your customers #much like ualitative approaches are used$.

"s the reach of the internet e!pands to include a greater proportion of thegeneral population and many more small businesses, online surveys willbecome more representative of the population at large.

lectronic cash payments will open up many more opportunities for conductingonline surveys. 'esearchers will gladly compensate respondents for their time


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and energy if an easy, straight-forward system is established. t is cheaper topay respondents K15 or K1+ up front than attempt to recruit survey participantsvia mail or telephone and then complete a 1+ or %5 minute survey.


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/$estionnaire #esi!n

Basic Considerations To design a good uestionnaire, a number ofconsiderations must be kept in mind9 ;oes it provide the necessary decision-making information for management and does it consider the respondent.

1. Does It Provide the ecessar! Decision"#aking Information$ The primaryrole of any uestionnaire is to provide the reuired information formanagement decision making. "ny uestionnaire that fails to provideimportant insights for management or decision-making information should bediscarded or revised. This means that managers who will be using the datashould always approve the uestionnaire. y signing off on the uestionnairethe manager is implying, *


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an interview schedule for the adult purchaser would be worded in languagesuitable for the adult interviewee. 2ne of the most important tasks ofuestionnaire design is to *fit* the uestions to the prospective respondent.The uestionnaire designer must strip away the marketing jargon andbusiness terminology that may be misunderstood by the respondent. n fact,it is best to use simple, everyday language, as long as the result is notinsulting or demeaning to the respondent.

>. Remember ,hat a uestionnaire +erves #an! #asters. " uestionnaireserves many masters. First, it must accommodate all the research objectivesin sufficient depth and breadth to satisfy the information reuirements of themanager. 0e!t, it must *speak* to the respondent in understandablelanguage and at the appropriate intellectual level. t must be convenient forthe interviewer to administer, and it must allow the interviewer to uicklyrecord the respondent6s answers. "t the same time, it must be easy and fastto check for completeness. Finally, the uestionnaire must be translatableback into findings that respond to the manager6s original uestions.


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SAM)LIN1 ++O+

very survey contains some form of error. ven a complete census of all knownmembers of a population is subject to random error or potential measurementerror. There are % major forms of sampling error that might be encountered in asurvey9 'andom rror and &ystematic rror.

+ando" rror 'andom error is the difference between the sample results andthe true results. ven if all aspects of the sample are e!ecuted properly, theresults will still be subject to a certain amount of error #random error or randomsampling error$ because of chance variation. This error cannot be avoided, onlyreduced by increasing the sample si3e. t is possible to estimate the range ofrandom error at a particular level of confidence.

Syste"atic rror &ystematic error occurs when sample results consistently varyin one direction #consistently higher or lower$ from the true values for thatpopulation. &ystematic error includes all forms of error not directly attributable to

the sampling process. &ystematic error is made up of sample design error andmeasurement error.

Sa"*le #esi!n rror6 &ample design results may be biased for a number ofreasons9

1. Frame rror. The sampling frame is the list of population elements ormembers from which sample is selected. Frame error results when thesampling frame does not represent a true cross-section of the targetpopulation. For e!ample, using a listed telephone sample #like atelephone book$ as a sampling frame representing all households in aparticular market would produce frame error. "ll households with

unlisted telephone numbers would be systematically e!cluded from thesample. This leads to errors in the sample because previous studieshave shown that unlisted households are different and therforeprovided different responses to survey uestions. f the e!cludedhouseholds were not remarkably different from those householdsincluded in the sample, the sample design would still be flawed, butsampling error would not result from the designated sampling frame.

%. ?opulation specification error. 'esults from an incorrect definition ofthe universe or population from which the sample is to be selected. f aproduct was thought to only appeal to females, the populationspecification would only include females. f later it was determined thatboth males and females should have been specified in the population,population specification error results because all males were e!cludedfrom the population specification and they are likely to have differentopinions than the females surveyed.

(. &election error. &election error involves a systematic bias in themanner in which respondents are selected for participation in thesurvey. ven if the sampling frame is defined properly to include theappropriate population members, selection error can still occur.ncomplete or improper procedures for selecting participants will lead


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to selection error. f a sample list was sorted by 3ipcode andinterviewers selected survey participants by contacting names in order from the beginning of the list, selection error would occur becausethose members of the population appearing at the end of the list#larger 3ipcodes$ would never be contacted.

Meas$re"ent rror6 2ccurs when there is a variation between the informationbeing sought #true value$ and the information obtained by the measurementprocess. :nless the true value is known before conducting the survey #whichgenerally negates the need for a survey$, measurement error is the most difficultform of sampling error to identify. There are several types of error which canoccur during the measurement process9

1. &urrogate information error. 2ccurs when there is a discrepancybetween the information sought by the researcher and the informationreuired to answer a particular uestion. This can generally beattributed to improper definition of the problem during research design.

%. nterviewer error. "nytime interviewers are involved in the collection of

data, there is a potential for respondents6 to be influenced to giveinaccurate or untrue answers. ?hysical appearances of interviewers#age, gender, dress$, facial e!pressions and body language caninfluence respondents during personal interviews. Tone of voice andverbal cues can influence telephone respondents. ffective interviewer training and uality control monitoring are the best defenses againstinterviewer error.

(. /easurement instrument bias. ?oorly written uestionnaires whichhave leading uestions, easily mis-understood uestions, orcomplicated methods of recording information that lead to recordingerrors are all sources of measurement instrument bias.

). ?rocessing error. This type of error is the result of mistakes intransfering information given by respondents to computer data files.Deypunch errors by data entry operators or interviewers in front ofcomputers and problems with the setup and operation of scanners arethe principal forms of processing error.

+. 0onresponse bias. 0onresponse refers to the people who areselected to participate in a research study, but who fail to respond tothat survey for one of several reasons9 unavailable, not interested insubject, fearful of a sales pitch, or some other reason. f there is asystematic difference between those who responded and those whodid not respond to the survey, then the survey results are subject tononresponse bias.

>. 'esponse bias. 2ccurs when survey participants deliberately falsifyinformation or misrepresent information when they are not certain ofthe facts. 'espondents may falsify answers to give socially acceptableanswers, to avoid potential embarassment or to conceal personalinformation. /isrepresentations can occur when respondents provideanswers without knowledge of the true answers.


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3ALI#ITY 8 +LIABILITY

Definition of Terms First of all, we define the terms as follows9

'eliability. " measure is reliable to the e!tent that independent but comparablemeasures of the same trait or construct of a given object agree. 'eliability dependson how much of the variation in scores is attributable to random or chance errors.@alidity. " measure is valid when the differences in observed scores reflect truedifferences on the characteristic one is attempting to measure and nothing else.@alidity and 'eliability. f a measure is valid, it is reliable. 'eliability is a necessary,but not a sufficient condition for validity. 'eliability only provides negative evidenceof the validity of a measure. ecause it is so much more easily computed, reliabilityis often reported as evidence of validity. t is important to distinguish between thetwo concepts and to keep in mind that a measure may be reliable without beingvalid.

#e,elo*in! 3alid and +elia0le Meas$res :nfortunately, there are no shortcuts

or easy ways to develop measures that are both valid and reliable. "t ;&&, wefollow a seven step process to develop patient satisfaction measures.&pecify ;omain of the Construct. =ere the researcher must provide an e!actdefinition of what is included and what is e!cluded in the definition of the construct.For e!ample, in regard to patient satisfaction, what attributes of the service shouldbe measured to accurately assess satisfactionJ &hould we include reactions to timereuired to schedule an appointment, waiting time in the office, courtesy of thereceptionist, perceived thoroughness of the e!am, e!planations of medicalcondition, etc.Jenerate &ample of tems. The second step is to generate a list of items that webelieve capture the domain as specified in the previous step. Techniues that are

usually productive include e!ploratory research procedures such as focus groups,depth interviews and the like.Collect ;ata. &tep three involves collecting data about the concept from a relevantsample of the target population #e.g., those who have visited a physician within thelast (5 days$.?urify the measure. " number of procedures might or might not be used to purify themeasure.

;omain sampling model. "ccording to the domain sampling model, a primarysource of measurement error is the inadeuate sampling of the domain ofrelevant items. :nder this model we use a large correlation matri! showing allcorrelations among items in the domain. f all the items in a measure are drawnfrom the domain of a single construct, responses to those items should be highly

intercorrelated. Aow intercorrelations indicate that some items are not drawn fromthe appropriate domain and are producing error or unreliability.Coefficient alpha. 2ur recommended measure of the internal consistency of a setof items is provided by coefficient alpha which results directly from theassumptions of the domain sampling model. Coefficient alpha should be the firstmeasure calculated to asses the uality of an instrument. t is very importantbecause the suare root of coefficient alpha is the estimated correlation of the k-item test with the errorless true scores. Thus, a low coefficient alpha indicates the


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sample of items performs poorly in capturing the construct which motivateddevelopment of the measure. 2n the other hand, a large coefficient alphaindicates that the k-item test correlates well with the true scores. f coefficientalpha is low and the pool of items is sufficiently large, this indicates that someitems do not share eually in the common core and should be eliminated. Theeasiest way to find these items is to calculate the correlation of each item with thetotal score and plot these correlations by decreasing order of magnitude. temswith correlations near 3ero should be eliminated. tems which produce a suddendrop in the item-to-total correlations should also be dropped.Factor analysis. This techniue can be used to identify or suggest dimensions inthe underlying data. =owever, factor analysis should only be used after asatisfactory coefficient alpha is achieved.

"ssess reliability with new data. The major source of error within a test or measureis the sampling of items. f the sample is appropriate and the items *look right,* themeasure is said to have face or content validity. f you follow the previous steps youshould have a test or measure that has content validity.

Caveats. =owever, transient personal factors, ambiguous uestions or any of the

other e!traneous influences can cause the test or measurement to be unreliable.etween-test error . f the researcher wants a reliability coefficient which assessesthe between-test error, additional data must be collected. t is also advisable tocollect additional data to rule out the possibility that the previous findings are dueto chance.'eproducible. f the construct is more than a measurement artifact, it should bereproduced when the purified sample of items is submitted to a new sample ofsubjects.

"ssess Construct @alidity. "ll of the proceeding may not produce a measure whichhas construct validity. Construct validity, which lies at the very heart of the scientificprocess, is most directly related to the uestion of what the instrument is in fact

measuring -- what construct, trait or concept underlies a person6s score on ameasure. The preceding steps should produce an internally consistent or internallyhomogeneous set of items. Consistency is necessary but not sufficient for constructvalidity. To establish the construct validity of a test or measure, the researcher alsomust determine the e!tent to which the measure correlates with other measuresdesigned to measure the same thing and whether the measure behaves ase!pected.

Correlations with other measures. vidence of the convergent validity of a test ormeasure is provided by significant correlations with other approaches tomeasuring the same construct.;oes the measure behave as e!pectedJ =ere we are interested in determining ifthe measure behaves as e!pected in relation to other constructs. =ere we may tryto determine whether the test score can differentiate the positions of *nonegroups* or whether the scale correctly predicts some criterion measure #criterionvalidity$. ;oes a managed care plan member6s satisfaction as measured by thescale correlate with the member6s likelihood of uitting. t should, according towhat is known about dissatisfied members. f it does not, then one would uestionthe uality of the measure of the members satisfaction with the plan.'eproducible. f the construct is more than a measurement artifact, it should bereproduced when the purified sample of items is submitted to a new sample of


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subjects.;eveloping norms. Typically, a raw score produced by a measurement instrument isnot particularly informative about the position of a given individual or group ofindividuals on the characteristic being measured because the units in which thescale is e!pressed are unfamiliar and have no frame of reference.

For e!ample, what does a score of (+5 on a 155 item Aikert scale with 1 to +score imply about managed care plan member6s satisfaction with the planJEe might be tempted to conclude that because the neutral position is (, a (+5score with 155 statements implies a slightly positive attitude or level ofsatisfaction.=owever, we should be very cautious in making this interpretation. The (+5 scoremay represent the highest score ever achieved using this instrument or thelowest.

" better way of assessing the position of an individual or group of individuals onthe characteristic we are measuring is to compare the score with the scoresachieved by other people or groups.


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#eno"inator 9::: ;)rod$ct #esi!n Tool<

What is #o"inator 9:::? ;ominator %555 is a software tool for analysis andprediction of buyers6 response to changes in the products or services offered inthe marketplace. running at >>/h3 or higher and 1>/ of '"/.

http://h/Sandeep/WHG/Conjoint/conjoint.asphttp://h/Sandeep/WHG/Conjoint/conjoint.asp


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"ppro!iamtely (/ of disk space for the ;ominator %555 program.Conjoint "nalysis data.

Documents

CONJOINT ANALYSIS and Other Stats Methods