189872695 Choosing the Right Eggs

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Inte rnationa l Journal of M arket Research Vol 54 Issue 5

Choosing the right baskets for your eggs

Deriving actionable customer segments using

supervised genetic algorithms

Sam DavisT

In the context of key d river analysis in applied customer satisfaction research, the

assumption of sample homog eneity (that single models perform adequately over

the entirety of a survey sample) can be shown to restrict the value of the insights

derived. While latent class regression has been used as a method of circumventing

some of these issues, it is proposed that there are major barriers to both uptake

and successful practical usage of the techn ique. Several of these issues are common

to any multivariate techn ique, w hile o thers are specific to latent class regression.

Following an exam ination of these issues, we introduce an alternative technique

for deriving discrete latent classes, using a combination of genetic algorithms

and (bivariate) correlations. This paper concludes that the proposed approachoutperforms latent class regression in its ability to deliver action-orientated

insights, and is better placed to assist marketers facing real-world research

questions and datasets.

ntroduction and theoretical perspectives

A common issue facing market researchers is determining the importanceof a particular variable, or set of variables, in influencing another:

W hat drives satisfaction? or How do I improve my brand image? are

particularly common questions. The analytic techniques used to answerthese questions can be grouped under the umbrella term of key driveranalysis (hereafter, KDA).

KDA can range from simply asking people directly what they think is

important, to a range of more complex statistical m ethods. D irect methods

are generally not commonplace in contemporary market research largely




because of respondents propensity to focus on more obvious areas suchas price. It is also commonplace for respondents to claim that everythingis important (among others. Bacon 2003).

There is almost continual debate over the best indirect analytictechniques to answer these sorts of questions, but most arguments do notconsider that different people tend to be motivated by different things -or, more formally, they take the view that the sample is homogenous. Thisassumption of sample homogeneity can be limiting. A simple yet powerfulillustration is given below.

Imagine that we have been asked to determine the factors that most

influence the purchasing of digital cameras. We may develop a set ofscales designed to measure performance on areas that are thought toinfluence purchasing, and then attempt to determine their influencestatistically.

Three areas are tested: (1) price; (2) user-friendliness; and (3) the abilityto customise camera components. We could then use multiple regression(hereafter, MR) to determine both the combined effect of these three areason purchasing as well as the relative importance of each area. With sucha methodology we may well find that we achieve only a poor ability to

predict purchasing and that each of the three areas is a relatively weakdriver.

It is not uncommon to report these results as-is, and a fairly literaltranslation of this for the end-user of the research would be that they canafford to under-deliver in these areas. Outside of the narrow context ofthese statistics this is highly unlikely to be the case; the result may havecome about at least partly because of a violation of the assumption ofsample homogeneity. In reality, it is likely that there are several underlyinggroups with differing drivers of purchase. Grouping these together as asingle analysis sample cancels out these effects, and weakens the analysisand the conclusions we may draw.

Latent class regression (hereafter, LCR) is a variation of MR that doesnot assume sample homogeneity. It works by defining segments accordingto respondents motivations, and runs regression models for each of thesegroups separately (Pinnell 2003).

In the presence of underlying sample heterogeneity, LCR offersadvantages over single-sample models, yet seems not to have come into

widespread use. This paper asserts that this is likely to be a result of severalbarriers:



Intern ationa l Journa l of Ma rket Research Vol 54 Issue 5

• relatively low awareness of these techniques compared to single-samplemethods

• LCR tends to perform poorly in delivering a practically usefulframework

• a disconnect between the textbook data we often see in theoreticalliterature on this topic and the realities of live survey data.

While we argue that tbe objective of LCR is indeed conceptually imperativein many instances, we also contend tbat the application of LCR raisessome concerns.

This paper examines tbe issues associated with applied usage of LCR,and sbows how it may potentially lead to sub-optimal conclusions.Two further assertions are made, as follows.

1. Subjectivity is critical in this area - the expertise of the researchercounts a great deal.

2. In tbis field, multivariate analysis methods themselves may not be bestsuited to the research problem and/or data.

Once the case for these claims has been established, an alternative methodtbat upbolds the principles of LCR yet seems to offer more actionableresults will be introduced.

Before continuing, it is important to note that this paper is not intendedas a statistical discussion of this area, but is focused on pplied us ge forresearch practitioners and users. A basic understanding of the principles ofcorrelation and regression-based metbods is assumed.

Multiple regression in customer satisfaction research

he du l positioning of mu ltiple regression

Tbe objective of MR is to understand bow well a set of predictorsinfluences a single variable usually one that is linked to a criticalcommercial outcome), and in particular how important tbese predictorsare in relation to one another.

Any reader of introductory, or not so introductory, texts discussing

analytics in market research would assume tbat MR is not only amongour most powerful tools, but is also used with a great deal of frequency.



hoosing the righ t baskets for your eggs

research problems and, in our experience, its use is on the decline as a

result. Consequently it has given way to a range of more sophisticated

techniques, most of which remain related to MR, however.

We shall now turn to a brief discussion of these shortcomings and

describe how commonly applied statistical remedies to counteract them fail

to do so because these are not truly statistical in nature. Instead, these tend

to be a result of the divide between textbook statistics and the real-world

properties of customer satisfaction survey data. Grapentine (1997) notes

that textbook examples in the area tend to offer very little in the way of a

meaningful discussion of applied issues, leaving practitioners ill-equipped

to handle these.

ulticollinearity and the particular d ngers of spurious multicollinearity

Multicollinearity is defined as the scenario where the variables we are

using to predict an outcome are, themselves, related to each other (among

man y othe rs, Tabachnik Fidell 20 01 ). Let's imagine we we re testing

the combined effect of age and length of time in tertiary education on

intelligence test performance among university students.

MR will not work adequately here if our objective is to understandwhether age or duration is the more important factor. This is simply

because these are so interrelated that the influence of each cannot be

accurately divided between them.

This specific example is in contrast to many of the multicollinearity

problems we face in applied customer satisfaction research because the

relationship between these predictors is logically necessary, albeit imperfect.

ecessary relationships seldom exist in customer satisfaction data: it is

typical to observe very high levels of multicollinearity even where there is

not a logical relationship. This is often the result of the 'halo effect' of a

product or brand - i.e. these are generally just good or bad across a wide

range of areas we are measuring. This issue is compounded by research

users' need for a level of granularity that a survey respondent struggles

to provide. This paper will refer to this as spurious multicollinearity and

contends that this is a special (applied) case of the problem that statistical

interventions cannot solve.

The example in Table 1 shows a correlation matrix between variables

from a real-life study exam ining custom er satisfaction w ith an o rgan isation'swebsite.



Intern ationa l Journ al of Ma rket Research Vol. 54 Issue 5

Table 1 I l lustrat ion of spurious mu lt icol l inearity webs ite sat isfaction: Pearson correlat ion

Ease of nav igating website

Functionality of website

Speed of w ebsite

Reliability of website

Ease of navigatingwebsite

1

0.77

0.72

0.48

Functionalityof website

0.77

1

0.72

0.59

Speed ofwebsite

0.72

0 72

1

0.67

Reliability ofwebsite

0 48

0 59

0 67

1

four variables, and that all we have really been able to measure is that a

website is perceived by respondents as either good or bad. This does not

mean, of course, tbat these factors are unimportant in defining wbat goodor bad means.

We must also note here that, wbile tbese relationships make sense, they

are certainly not necessary Re liability as it is defined here is inte nd ed

to mean that the website does not crash. Clearly tbis has no necessary

relationship with tbe ease of navigating the website in the way that the

research user was intending - essentially tbat various components of the

website were easy to find while the website was functioning It follows,

tben, tbat tbe end user will require a separate understanding of these issues.

When we correlate these individual variables against an overallsatisfaction metric we would expect to find little to choose between them,

as is indeed tbe case (Table 2).

Table 2 Flat correlat ions em erging from mult icol l inear predictors: Pearson correlat ion

Ease of naviga ting website

Functionality of website

Speed of w ebsiteReliability of w ebsite

Overall company satisfaction m easure

0 40

0 44

0 530 42

These correlations suggest tbat all tbe variables in tbe analysis have a

moderate influence on satisfaction. Of course it would be a mistake to

presume a causal relationsbip.

A regression analysis paints a very different picture. Tbe standardised

regression coefficients are given in Table 3.

It is clear from tbe re gression coefficients tba t Speed of Website is far and

away tbe most important factor. We know, however, that tbis extremityis not the case if we simply consider the correlations. In addition, a very




Table Multiple regression analysis of factors influencing Vkiebsite satisfaction: standardised

regression coefficients

Constant)

Ease of navig atin g v>/ebsite

Fun ctionality of v\/ebsite

Speed of website

Reliability of website

Standardised regression coefficients

0.03

0.12

0.40

0.10

The presence of a negative sign when we know the bivariate relationshipis positive is a well-established symptom of a regression model that

is affected by multicollinearity (Zhang c Mahmud 2005). It is alsoimportant to note that similarly suffering models do not always exhibitsuch obvious warning signs.

These regression results do seem problematic, as to report these literallywould yield conclusions that are misleading (Speed of Website is not theonly important factor) and of hmited use to an end-user or client.

Various statistical solutions have attem pted to address this. Kruskal s

Relative Importance (Kruskal 1987) and Shapley-Value Regression havereceived a great deal of press in recent years, and are seemingly being

afforded a level of holy gra il status. A full trea tment of these techniquesis beyond the scope of this paper, but in essence they attempt to partition

importance scores more accurately across the predictors in the presenceof multicollinearity. This does, in itself beg the question as to how accurately can be defined in this instance.

When using these techniques, the variable with the highest correlationwith the criterion variable tends to remain inflated - so in essence thesemethods do not offer an improvement over MR from this perspective.

Although coming at this from a slightly different perspective, Grapentine(2010) offers a critique of these methods concluding they have become an easy op tion and are fundamentally ill-considered.

There may, arguably, be a temptation for some clients (and agency-sideresearchers) to prefer inflated importance scores as these provide moreinteresting results. This paper contends that this can be problematic,

especially when these inflated factors are privileged to the detrimentof others that remain clearly important in bivariate terms (and withface-validity).

Many traditional texts (even those with an applied stance) haverecommended the removal of one or more variables to reduce



Intern ation al Journal o f Mark et Research Vol 54 Issue 5

While there are obviously good statistical reasons for removing

variables, this tends to cause concern for clients and end-users. It isdifficult to recommend this approach as clients simply need a method thatallows them a view of all the areas included in the questionnaire.

Another frequently proposed remedy is to use factor analysis to reducethe predictors into a smaller number of independent factors. This willsolve the multicollinearity problem statistically but does not fare anybetter as clients tend not to be able to do much with 'factors'. This isparticularly true in the event of spurious multicollinearity as we will begrouping together areas with very little intuitive or practical connection.

Again, we should note that the same applied text (TuU Hawkins 1990)is one of many that advocate this approach.

Given the frequency of detecting spurious multicollinearity in customersatisfaction studies, it is clear that practical solutions are required. Itseems that proposed solutions are either not sufficiently cognisant of therealities of the practical needs of clients or simply do not offer a superioralternative to conventional MR.

ystematically missin data

further barrier to the use of MR is that multivariate analysis requiresthat all cases (usually respondents) contain usable data for all the variablesused in a model. This is seldom the case in customer satisfaction studies. Acommon study design will ask a set of satisfaction or performance questions(generally in the form of Likert scales) across a wide range of respondents'interactions with a product or brand. A single study, for example, may askrespondents to comment on their levels of satisfaction with both the taste

of a breakfast cereal as well as how satisfied they were with the customerhelpline in the event they needed to raise a complaint.

Most customers will have tasted the cereal but few will have calledthe helpline. As a result we do not have the complete dataset required toperform the analysis. It is notable that, when the helpline has been called,it is likely to have been in order to complain.

Again, many proposed solutions do not fully reflect the reality ofapplied research. Predictably, perhaps, one of these is the removal ofvariables that contain the most missing values in order to reduce the

amount of cases/respondents this affects (Hair et al 2010). This minimisesthe amount of sample that needs to be excluded from the analysis in order





Intern ationa l Journa l of Ma rket Research Vol 54 Issue 5

multivariate techniques grow in usage, their status becomes increasinglydifficult to challenge. While it is undoubtedly true that multivariate analysisoffers a wealth of power that bivariate techniques cannot hope to emulatein certain settings, this is certainly not true in all applications. Grapentine 1997) argues that researchers should exercise caution when using bivariatemethods to assess the importance of variables in KDA because of theadvantage of MR holding additional predictors constant when determiningimportance. This paper has already shown that there are other practicalconcerns to consider when using MR that may outweigh the advantages itoffers in some circumstances.

Late nt class regression

LCR works by finding a set of regression models that maximises theexplanatory power of the relationships between the predictors and thecriterion variable by finding groups with differing regression coefficients or importance scores). The end result is that the analysis produces anotherwise conventional M R analysis on these groups. In effect, then, it is acombination of regression modelling and segmentation analysis Vermunt

C Magidson 2002; Pinnell 2003) with the result that we are left withgroups that are motivated in different ways.

Generally we find that each of the latent classes/segments identifiedachieves substantially improved model fit compared to a single-samplemodel. This is generally considered an indication that the analysis has beensuccessful in uncovering and accounting for heterogeneous samples.

Support of this basic premise of LCR has already been noted, but it s nowimportant to discuss potential weaknesses that emerge in the course of theshift from theory to practice. Given that LCR is a multivariate technique,the problems relating to spurious multicollinearity and systematic missingdata already raise some serious challenges. In addition two further, morespecific, issues raise further questions over the usefulness of the method.These are discussed in more detail in the following sections.

h dangers of over fitting

In the context of MR the concept of over-fitting usually refers to a scenario

where the explanatory power of a model is inflated because of a highnumber of predictor variables. This is particularly the case with a high




Using 12 random predictor variables in a single-sample MR yielded lowlevels of model fit (as we would expect). We tben performed LCR usingthe same variables and were able to detect four latent classes witb verystrong model fit:

Class 1:R2 = 0.83Class 2: K = 0.84Class 3: Ä^ = 0.87Class 4: R^ 0.84

In effect, we are able to use LCR to create a sense of order andpredictability where none can exist in reality. The fact is tha t we simply willfind statistically differentiated groups (witb high R} statistics) because tbeanalysis cannot fail to do so. To expect anytbing different would be akinto asking a computer to produce a sbape otber than a triangle out of threeadjoined sides.

If a similar result occurred in practice it is highly likely that tbe LCRapproach is modelling as much noise as it is signal in its effort to increasethe explanatory power achieved by a single-sample model. In addition,

while excellent levels of explanatory power may be acbieved in tbe singledataset in question, it is almost unfeasible tbat sucb an over-fitted modelcould be repeated in another sample.

Surely, then, tbe underlying reality of the scenario we may infer fromthese results can be questioned. With substantial marketing and productdevelopment budgets on tbe line, how much of the researcher s powershould really be surrendered to tbe results produced by tbe analytic software?

We sball return to tbis in more detail wben looking at a practicalexample below.

ifferentiation of raw input v ri bles cross latent cl sses

Wben profiling the segments detected using LCR we tend to find very littledifferentiation between classes wben looking at tbe raw r spons s to tbevariables used in the model (as opposed to a different pattern of regressioncoefficients). In otber words, tbere is often limited ability to describe theresulting segments using criteria external to tbe regression model itself

It is accepted tbat not providing for differentiation in the original modelvariables is not a self-evident disadvantage of LCR, given tbis is not an



Intern ationa l Journal of M arket Research Vol 54 Issue 5

the technique as a segmentation tool for that very reason. Analysts can(correctly, from a statistical perspective) explain the lack of differentiationin the original variables, but it takes an unusually statistics-driven client toaccept a solution with these characteristics.

So while we concede that achieving differentiation on additionalprofiling variables or the raw inputs themselves is not top priority, wewould certainly favour a technique that would enable us to achieve thisto some degree.

Offering an alternative supervised methods using genetic

algorithms

Up to this point we have raised several issues regarding the use of LCRin practice. We will now show that a technique using supervise geneticalgorithms can account for sample heterogeneity while achieving morepractically useful results.

The following section will briefly introduce the theory behind geneticalgorithms before describing how these can be brought to bear on theproblem. Finally, it will be demonstrated, through comparative analysis

between methods, that this approach offers some tangible practicaladvantages over LCR.

brief introduction to genetic algorithms

Genetic algorithms (hereafter, GAs) were proposed (Holland 1975) as amethod of solving optimisation problems based on the principles of naturalselection. The GA process is outlined more fully in other texts, (amongothers Steiner Hruschka 2003), but a basic overview is given here.

A set (or 'population', in GA terminology) of potential solutions to agiven problem is generated, and the performance of each is assessed with

respect to how well the problem is solved. Following a series of trials (thenumber of which is specified by the researcher) the 'fittest' solutions areselected to remain in the solution population. These potential solutionsthen give rise to a new generation of solutions by passing on characteristics

that are useful in solving the problem. The process is then repeated untila satisfactory level of performance is reached. In addition to this process

of 'adaptation' and 'breeding', solutions may also be allowed to randomlymutate. This ensures that the algorithm does not evolve along a single




he role of supervision in the process

A useful characteristic of GAs is tbat a solution can be aimed not only

at improving a particular objective, but constraints can also be placed

on specific parameters of the solution. The importance of tbis will be

elaborated on more fully below, but it sbould be clear tbat it is tbis ability

to constrain tbe results tbat adds a great deal of power to tbe analysis,

given tbe concerns raised witb regard to over-fitting in LCR.

We should consider a potential objection tbat may stem from tbis

point: tbat a researcber should be in the business of finding answers, not

co-creating tbem. In tbe experience of tbe author, there are two types of

niarket research client: tbe first views statistical analysis as a tool to ssistin answering a business question, and the second believes in the objective

trutb of 'clean statistics' - or statistical truths that emerge from tbe data

that sbould not be tinkered witb at any cost.

Tbis paper argues that a balance between tbese two poles is optimal. We

sbould neither blindly accept the results generated by analytic software, nor

sbould we take so much of a lead role in shaping tbe analysis tbat the results

take on an overly 'manufactured' form, for botb practical and ethical reasons.

Co ntem po rary literature on tbe topic of ma rket segmentation s particularly

sensitive to this spectrum (Bottomley C Nairn 2004; Stewart-HunterBergentbal 2004). In tbis field, tbere is a sbift towards allowing a researcber

to generate tbe most useful results by employing a degree of subjectivity.

Otber applications of statistics in market researcb (KDA included)

seem to have fallen bebind a little and remain more witbin an 'emergent

truth' school of thought. We believe that allowing the researcher to take

a supervisory role minimises tbe risk of over-fitted results tbat we have

already demonstrated and allows for more usable results.

pplying genetic algorithms to accoun t for sample heterogeneity in key

driver analysis

We bave set up several experiments to assess bow we can combine

correlation analysis witb GAs to allow us to identify discrete 'latent classes'

while overcoming the issues illustrated above. Due to constraints of space,

bowever, we are able to discuss only one example in detail (given further

below in tbis paper).

Tbe key characteristics of the approacb are as follows:






Tbe model fit yielded by a multiple regression analysis conducted across

tbe total sample is weak {R ̂ 0.14).

A comparison between tbe model fit scores achieved by tbe LCR and tbe

GA metbods for defining latent classes are given in Table 4. Note that four

classes were used for tbis analysis.

Both metbods acbieve groups witb substantial improvement in model

fit. Tbis is particularly true for tbe LCR approacb, but recall our earlier

argument that a particularly strong model fit may indicate over-fitting.

A comparison of tbe two approaches with respect to the correlations

between the predictor and criterion variables is given in Table 5.

Th e LCR m etho d yields some results that are imm ediately counter-intuitive.Class 1 in par ticula r exh ibits stro ng negative corre lations for Sufficient

Parking Spaces and Ease of Paying. One may plausibly understand tbe latter

as a lack of value for money caused by having to pay at all, but we would

argue tbat a researcber would have a difficult task convincing tbe client that

there is a grou p of people w ho w ould prefer to have fewer parking spaces to

choose from. LaLonde (1996) has also detected (and raised concerns over)

the presence of strong negative importance scores in bis treatment of LCR.

In our view, the strong negative correlation indicates that tbe LCR approach

may be over-fitted and seems to offer less actionable groups as a result.Class 3 of the LCR app roac h has similarities to Class 3 of the GA app roa cb

and is not seen as weak (detailed below), altbougb tbere are some moderate

negative correlations. Class 2 tends to bave higber importance scores tban

other classes almost across tbe board, wbile Class 4 is moderately positive

on environmental factors: cleanliness, lighting and security in addition to

ease of paying.

Tbe GA approacb has been constrained so as not to tolerate an

un rea listic level of nega tive drivers , so we d o no t see a scen ario similar to

Class 1 of the LCR approach.

Class 1 (GA) may be understood as interested in being able to

park, and while availability of spaces and ease of finding a space are

Table 4 Model fit comparison of latent class regression versus the genetic algorithmapproach

Class model f it (R )

Total sample

12

Latent class regression

0.14

0 700.94

Genetic algorithm method

0 14

0 420 42



Intern ationa l Journ al o f M arket Research Vol. 54 Issue 5

Table 5 Bivariate im por tan ce scores Pearson correlations): latent class regression versus

genet ic a lgor i thm approach

Latent class regression

Genetic algorithm

approach

Correlation with value for money 1

81

0.35

0.15

0.02

0.03

0.04

0.49

0.02

0.01

0.25

2

84

0.84

0.47

0.15

0.17

0.19

0.47

0.12

0.23

0.01

3

66

0.07

0.17

0.31

0.08

0.05

0.80

0.02

0.15

-0.20

4

94

0.06

0.07

0.11

0.04

0.27

0.43

0.49

0.26

0.25

1

70

0.35

0.35

0.60

0.22

0.27

0.14

0.04

0.03

0.04

2

81

0.61

0.40

0.07

0.02

0.09

0.03

0.00

0.07

0.05

3

98

0.05

0.03

0.09

0.04

0.02

0.66

0.10

0.10

0.03

4

76

0.22

0.29

0.19

0.12

0.12

0.29

0.52

0.20

0 34

Class size

Sufficient parking spaces available

Ab ility to fin d a space

Ease of m anoe uvring into a space

Ease of entr y/ex it in to and o ut of the car park

Navigation aroun d the car park

Ease of payin g

The cleanliness of the car park

The level of lig hting in the car park

Feelings of security whilst in the car park

Note: Cells w ith absolute correlations greater than 0.3 are highligh ted for ease of illustration .

certainly influencing factors, ease of getting into and out of spaces is thecritical single factor. This has clear implications, specifically, for the way

that spaces are designed and potentially fitted into existing architecture e.g. positioning pillars in relation to spaces, size of spaces). While the inputvariables do not offer this level of interpretation, it is certainly sufficient towarrant greater investigation of the issue. It is also important to note that

satisfaction with the ease of getting in and out of spaces is relatively low,so we have the additional benefit of being able to note that there is roomfor improvement in this area. This level of differentiation with respect tothe raw variables is not achieved by the LCR approach.

Class 2 GA) are unconcerned about anything other than having

spaces to park in. While Class 2 in the LCR model is also influencedby availability, it is not as distinct in this regard as Class 2 in the GAsolution. The action for this segment is clear, of course, but there arelikely to be barriers to increasing space in many car parks. This is likely

to be a group that it is simply going to be too expensive to please in theshort-term, so meeting their needs may be delayed while easier wins arefound.

Class GA) are focused on ease of paying, and it is worth noting that this

is a group with high levels of satisfaction. While this is an already-satisfiedgroup, they could well be responsive to further innovation in this area.




Class 4 of tbe GA approacb (like Class 4 generated by LCR) is mostdriven by environmental factors of cleanliness, lighting and security. Wbilewe have noted that tbe LCR method yields a class where these issues arealso important it seems to be to the detriment of tbe importance of otheraspects of the car park. We believe that the moderate importance of tbe(potential) hygiene factors of being able to find a parking space addscredibility to the GA version of this group.

Our conclusion based on this comparative exercise is that using theGA approacb offers clear interpretive benefits and greater ability to assistdecision making.

pplying the method to data containing system atically missing v lues

As noted above, systematically missing values cause major problems inderiving importance measures in customer satisfaction studies. This ismost critically so in the specific context of multivariate analysis.

LCR simply cannot handle missing values without eitber removing somecombination of variables and/or cases from the analysis, or imputing datafrom an unrepresentative sub-sample. We have argued that adopting either

of tbese approacbes is a cause for concern.Bivariate metbods are not so hampered by missing data. By extension,

therefore, neither is the GA methodology described above.We do anticipate an objection to tbe application of tbe GA method to

datasets witb systematically missing values: can tbe method guarantee thata latent class placing high importance on a specific area bas a sufficientlylow level of missing values witbin tbat variable? While our position is thatit must be a strength of tbe approacb tbat tbe analysis is even possible, weshall give this some consideration.

The analysis above details a latent class wbere ease of paying was ofcritical importance. There may, however, be people in tbe sample wbo didnot have to pay for a range of possible reasons. As a result, tbese caseswould not bave valid data recorded for tbis variable.

These people can still, statistically speaking, be represented in tbis latentclass as tbeir otber responses will still contribute to tbe overall sbape ofthe solution. Our grouping still only formally means, among those intbe group wbere X is relevant, X is important . We appreciate that any

substantial number of instances wbere tbis takes place may reduce end-userconfidence in tbe resulting solution.



Intern ationa l Journal of M arket Research Vol 54 Issue 5

a large number of missing cases for those variables where importance is

high. While this will decrease the m odel fit and the levels of differentiation

between latent classes, the ability to apply this constraint does seem to bea further benefit of the GA approach.

onclusion

While this paper supports the principle of considering sample heterogeneity

in the context of key driver analysis, it also argues that the conventionalmethod used to overcome this - latent class regression - has some

drawbacks when used in applied customer satisfaction research.This paper has focused on four issues concerning the applied usage oflatent class regression:

1. spurious multicollinearity occurs in many customer satisfaction datasets2. systematically missing data are a common occurrence3. over-fitted results that are , at best, difficult to use

4. a tendency for latent classes to offer little differentiation with respectto the raw variables used in the analysis.

Most of these stem from a fundamental disconnect between (sound) theoryand the practical realities that tend to constrain the data generated inresearch of this kind.

Given these concerns we have proposed, and trialled, an alternativeapproach using supervised genetic algorithms to assess bivariate relationships

in order to achieve the primary objective of finding distinct latent classes.Our results showed that the genetic algorithm approach was superior

and, critically, provided us with a way of drawing better commercially

focused conclusions than a latent class regression solution.

cknowledgement

The author would like to thank Ian Brace, Director of Research Methods at

TNS, for his invaluable guidance throughout the preparation of this paper.

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hoosing the right baskets for your eggs

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bout the author

Sam Davis has been working in market researcb for ten years in operations,client-service and analytic roles - most recently as a director at TNS'sMarketing Science Centre in London. In this role he bas worked witb awide spectrum of clients across many sectors, ranging from government

agencies to EMCG products. Sam's main researcb interests are marketsegmentation and customer satisfaction/loyalty researcb.



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