Segmentation White Paper*
Mike Morgan, PhD
First edition: January 19, 2006
Current edition: October 9, 2012
The purpose of this paper is to:


Give a brief overview of the history of market segmentation.
Provide a statement of the client value proposition and a “Market Segmentation
Manifesto.”
 Illustrate how segmentation addresses the heterogeneity of consumer preferences.
 Illustrate how segmentation addresses the fact that many dimensions of consumer
preference are essentially unobservable and require unique types of modeling to
capture them.
 Describe different kinds of segmentation approaches, along with their limits and
advantages.
 Delineate typical segmentation projects.
 Describe the characteristics of “good” segmentation solutions, along with benchmark
goals for success.
Some of the material herein is borrowed or adapted from Wedel and Kamakura’s
Market Segmentation, which is, in turn, borrowed from other theorists over the past
40 years in this wonderful, diverse, and still very vibrant field of research and
analysis. Thanks very much to Scott Van Manen for his invaluable insights in this
endeavor.
*
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Overview of the history of market segmentation
In the Beginning was the Market…
Marketing can be thought of as a framework of ways in which sellers deliver value to
buyers in a market economy. It involves creating wares for sale, communicating to
buyers their availability, pricing of the wares, and making them available. The theory
and practice of marketing has evolved, sometimes slowly and sometimes by leaps and
bounds, ever since the emergence of the village marketplace.
The most profound early reflections about what capital-driven markets deliver to
consumers were made by John Stuart Mill in the mid-nineteenth century, in his book
Utilitarianism (1861). For Mill, consumer value could be measured in “utiles,” or utility,
a close relative if not synonym of the word “happiness.” All products and services could
be reduced to this single common denominator. All buyers had the same preferences, and
all competing goods were commodities. This economic worldview dominated the theory
and practice of marketing until the early 20th century. Its last great cultural expression in
America was Henry Ford’s mass production and sale of the Model T.
What drives advancements in the practice of marketing?
Despite advancements in agricultural, manufacturing, and information technologies over
the past 150 years or so, the supply side of the market has not necessarily been the
primary driver of marketing’s evolutionary process. Granted, it is still largely true that
“If you build it, they will come.” But, the supply side alone has not taken us as far as we
have so obviously come beyond John Stuart Mill’s rudimentary, commodity market
economy, into the age of mass customization.
Instead, it is the evolution of consumers – physically (in numbers, shapes, and sizes),
psychologically, socially, morally, spiritually, intellectually, ethnically and racially,
sexually, and last but not least, financially – that primarily drives advancements in
marketing in the modern world. In other words, marketing has evolved mainly by
specializing – the wares themselves, the pricing schemes, channels of distribution, and
communications – to fit an ever-increasing degree of diversity in the consumer
population.
Did the remarkable range of consumer diversity we observe today first begin to explode
when it became popular to “do your own thing” in the late 1960s? Actually, economists
and marketers began to notice key differences among consumer types at the turn of the
20th century. In his book, Theory of the Leisure Class (1899), Thorstein Veblen (who was
age 15 when John Stuart Mill died) argued that economic life is not driven by notions of
utility, but by differences between the more privileged class – those who control politics,
money, and society – and the less privileged who could, at best, emulate the consumption
power of the privileged in limited and artificial ways. Without really knowing it, Veblen
identified not only the first basis for market segmentation – in socioeconomic terms – but
also discovered the earliest strategy for marketers to target their wares more effectively –
by enabling ordinary consumers to emulate the wealthy though “conspicuous
consumption” of powerfully symbolic products and services.
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Client Value Proposition – What is the Real Value of Market Segmentation?
There is a lot of discussion in this article about a range of theories and practices in
marketing segmentation. To help build some perspective, here is a starting point, perhaps
a framework of values. Please consider each of these points carefully as well as how they
might tie together.
Market Segmentation Manifesto
o The successful marketer depends on uncovering hidden values in the market – new
product opportunities, unmet consumer needs, and sources of demand from the very
“edges” of every existing and potential target market.
o Innovation, launched from a baseline of deep insight into competitors, products, and
consumers, is what ultimately delivers marketing value to consumers and wealth to
company stakeholders.
o In order to identify market opportunities comprehensively and innovate towards them
effectively, the marketer must have a clear view, not only of these hidden opportunity
values themselves, but of the sheer range and complexity of such values.
o It is towards understanding this range and complexity of hidden values that market
segmentation offers its strongest and most unique contribution to the marketer’s
competitive arsenal and, ultimately, to consumer happiness.
How Market Segmentation addresses the Heterogeneity of Consumer
Preferences.
Target Markets Don’t Come in Neat Packages…
Everyone likes to think in terms of discrete, mutually exclusive categories. He’s an
African American, she’s a nuclear scientist, they belong to the Democratic Party, those
people are Sinners, we are proud Americans. In real life, we know (and will readily
acknowledge if asked) that such mutually exclusive categories are just convenient
generalizations. Instead of categories, the universe is made up of continuous dimensions
– we slice it up in order to manage it for ourselves, in order to create discrete rules for our
beliefs and behaviors.
We do this so that we do not become paralyzed by the fact that no single category is ever
really distinct once you begin to dig into it. The African American is not purely of
African descent (as Malcolm X continually reminded people during his own short life),
nuclear science is just a portion of the vast body of physical science and mathematics,
Democrats often cross party lines to vote for the other candidate, sometimes sinners are
indeed saints, and Americans are not always so proud, at least, not of everything.
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Nevertheless, we go ahead and slice and dice up our world to make better sense of it.
Thorstein’s classification into leisure versus working class, in fact, requires one to draw a
line, however arbitrarily, between higher and lower income and wealth ranges. As we
know, income and wealth are instead continuous dimensions, upon which we can draw
whatever lines we wish to suit our own purposes.
If we think of continuous consumer dimensions for a moment, we need a word to
describe differences along these dimensions. The word used in this article is
“heterogeneity.” Heterogeneity exists along a continuum. It is the opposite of
homogeneity, or sameness, among a group of objects, people, or ideas. The rainbow
appearing after a thunderstorm, while appearing to consist of distinct colors, is actually a
continuous spectrum of light – we just don’t see the spectral shifts from our own distant
perspective.
Consumer heterogeneity exists along a spectrum – actually, along multiple spectra. It is
continuous and usually smoothly distributed. Heterogeneity contains a subtle element of
surprise – not what we expect, not a neat package that is easy to think about or easy to act
upon. More than anything else, this means that if you don’t like surprises, and if you
don’t like heterogeneity and diversity, you should probably stay out of the market
segmentation business.
Figure A. Homogeneity to Segmentation to Continuous Heterogeneity:
Types of Marketing Phenomena
Homogeneous
Population
Commodities
Utility
Production
Market Demand
Market clearing prices
Product Purchase Volume
Consumer Surplus
Market requirements
Information
Mass-Production
Isolated individuals
Households
Market Share
Discrete Market Segments
Competitive Set
Feature Bundles
Styles
Segment preferences
Price tiers
Product line(s)
Choices
Key drivers
Awareness (yes/no)
Segment offerings
Peer Groups
Socio-demographics
Segment acquisition/retention
Continuous
Heterogeneity
Brand Equity
Features/Options
Design
Individual preferences
Price elasticity
Product relationships
Participation
Value-laden dimensions
Honesty
Customization
Word-of-Mouth
Family/Life Styles
Responsibility/Ownership
Figure A illustrates differences between a view of the market as homogeneous,
categorically segmented, or continuously heterogeneous. The market segmentation
business focuses efforts, traditionally, on the middle paradigm. We try to identify which
segments prefer which brands in the competitive set, what particular combinations of
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features are important, and we attempt to project that segment’s preferences onto a total
market opportunity.
Homogeneous Population
The
Homogeneous
market
Members of the market are pretty
much the same. The important
thing is to understand the market
overall.
Discrete Market Segments
Members of the market fall into
discrete groups, based on
similarity of preferences or
member characteristics.
Continuous Heterogeneity
Buyers have different preferences
and characteristics along different
continua, so even if we have
segments, within-group variation
is also part of the scheme.
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An important note is that we cannot yet, with today’s research technologies, always attain
the one-to-one marketing paradigm using the “heterogeneous” column in Figure A,
however ideal that may seem. While categorical segmentation is, at best, only an
approximation, a convenient generalization of “true” individual reality, it does provide
marketers with manageable categories. It may be years before we can fully understand
the consumer’s true personal, idiosyncratic, emotional, and cognitive relationships with
our company’s products. Today, though, we can generalize these relationships to a
manageable segment level.
Does this mean that we cannot ever capture more heterogeneity in the client’s market
than we currently do? Not at all! In fact, this article will help lay out a path to capitalize
on new and existing research and statistical technologies, and in doing so to achieve
maximum effectiveness to explore, elucidate, and forecast the heterogeneous frontiers of
any of our clients’ markets. Here is the primary challenge with respect to consumer
heterogeneity.
Challenge #1 – To capture the most consumer heterogeneity possible in any market
segmentation analysis.
o Market segments generally tie together consumers in such a way that their traits,
attitudes, and responsiveness are relatively similar within each segment.
o However, each segment always retains, to some extent, a range of diverse traits,
attitudes, and responsiveness.
o Ultimately, market segmentation must account for this diversity – within market
segments – in ways that add valuable marketing opportunities.
Many dimensions of consumer preference are essentially unobservable
and need to be modeled as such.
It’s a kind of magic. The purpose of any magic trick is to obscure or hide the true
mechanism behind the outcome. In any consumer (or business) market, the magic is
already built in – what the buyer really wants is “unobservable” until he or she is
presented with the product. Perhaps this is because the buyer is not sure of the desired
outcome until the solution is presented (i.e. the case of a new product). Or, perhaps we
observe sales volume for an existing product, but the consumer types who buy are hidden
beneath this, each buying at a different rate and for different reasons. Only the magician
– or the marketing research specialist – can uncover those hidden forces.
If the seller doesn’t care about consumer types, the underlying, unobserved dimensions of
the market will forever remain unobserved. This is the case with most commodities –
who cares if the buyer of pork belly futures is gay, bisexual, or straight, as long as the
price is right?
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On the other hand, suppose a given consumer market is crowded with competitors, each
capable of reaching consumers independently with their own magical marketing tools.
Each competitor is (or should be) desperate to find those consumers who – with the right
messages, product types, distribution channels, and prices – can be uniquely drawn to its
own unique offering.
If this weren’t the case, marketing would not exist. Fortunately for us marketers, research
tools allow any competitor to sample the market and ask consumers what they would like
to buy and then develop marketing strategy accordingly.
Available data to use in market segmentation. Consumers can be classified into groups
based on age, income, race, ethnicity, education, size and type of family, geographic
location, and other “observable” factors. In fact, database marketing companies provide
lists of (nearly) all consumers in America on all geographic and demographic
dimensions.
How do they know these things? Because the U.S. Federal Government has itself
conducted surveys of the population to obtain the information and made it available to
anyone with the tools to aggregate and store the data. Thus, we might say that the most
observable information is already obtained, and (as yet) unobserved information is
available, at some cost, to any competitor in the market. It’s what we don’t easily see
that gives marketing its magical power.
Figure B. Easily Observed versus Hard-to-Observe Bases of Segmentation
Type of Information
Consumer Traits
Consumer’s Relationships
with Product
Easily Observed <-----------> Hard-to-Observe
Culture, Geography,
Psychographics, Values,
Demographics, SocioPersonality, Lifestyle
Economics
Product Usage, Options,
Product Benefits,
Styles, and Extras
Attribute preferences,
Purchased.
Behavioral Intentions
Market share and volume
response to price changes
Maximum Willing-to-Pay,
price elasticity
Consumer’s Relationships
with Promotion
Market share and volume
response to ad campaigns,
promotional offerings, etc.
Awareness, Consideration,
Evaluation based on
promotional information
Consumer’s Relationships
with Distribution Channels
Sales volume and cycle per
channel. Penetration and
repeat purchase rates from
channel offerings.
Perceptions of
Convenience of, Locus of
Control within the
Channel, Value-Added
features of the Channel.
Consumer’s Relationships
with Price
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Figure B illustrates differences in marketing-relevant information on the basis of how
easily observable such information is. In the academic literature, you might see the term
“unobservable heterogeneity” used to describe items in the rightmost column.
History behind the idea of modeling unobservable heterogeneity.
A handful of econometricians originally built models that “observe” the unobservable
data by modeling them with latent variables (more on this below). One such
econometrician, James Heckman, won a Nobel Prize for his work in this area in 2003.
During the 1970s, it was believed by many social theorists and politicians that the longer
a person remained unemployed, the less employable that person would become. If this
were true, then social policy called for quick intervention – skills training, job seeking
help, etc. – as soon as possible after a person became unemployed. This would be a very
expensive social policy, since it would involve selective investment of government funds
– and selective about who gets the most help and when.
What is the “unobserved” data in this case? It is the actual employability, or skill level,
of the worker. Employability was, from the view of social policy, “unobservable.”
Heckman found a way to model this dimension, though, using a latent variable approach.
He showed that, once you control for employability, the amount of time one is
unemployed is unrelated (uncorrelated) with one’s chances of getting a new job. This
saved American taxpayers, needless to say, a tremendous amount of money.
From the perspective of the marketing services company, we need to keep in mind two
important principles:
1. Nearly anything is observable if you throw enough money into the effort! Collecting
and modeling the Hard-to-Observe Data, in fact, is one of the major benefits that
clients pay for, using survey research.
2. BUT, before you throw all of the client’s money into revealing the unobservable, we
should consider modeling alternatives.
3. Modeling – as opposed to collecting – unobserved data is always a second-best
solution – IF costs of collecting such data are within the client’s budget.
Are there any kinds of unobservable dimensions – or unobserved heterogeneity in the
market – that absolutely cannot and will not be observable? There is one – we can call it
The Future. We can guess at it, predict it, or even expect it, but no one can actually
observe the future until it happens.
From the perspective that any market is continually in flux, any market segmentation is
obsolete the moment it is completed. However, if the segmentation solution is based on
measurements that are relatively stable over time, we can be assured that the solution will
remain useful for some time. Estimates of this time vary. Rules of thumb that I
subscribe to, based on my own experience over a number of segmentation analyses, are
given below.
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The life of a segmentation solution:
o A solution based on demographics – with or without a geographic component, should
last three years.
o A segmentation scheme based on consumer attitudes or needs should continue to be
valid for at least 12 months.
o Segmentation based solely on purchase behavior should last at least six months.
Do we ALWAYS have to renew a segmentation study periodically? So far, in both
theory and practice, we should. We need to uncover new methodologies that take into
account dynamic changes and evolution in the consumers’ geo-demographics, attitudes,
and behaviors. The first commercial research firm to offer a proven approach to
“dynamic segmentation” will indeed make history in the marketing industry.
Challenge #2 – Capture more UNOBSERVED consumer heterogeneity than has
been previously possible with existing methodologies.
o Market segmentation must account for both observed and unobserved (or less
easily observed) dimensions of the market.
o Tools for revealing these latent aspects of the market, and market segments, need to
be applied as vigorously as possible – this is where a company can gain the most
competitive advantage in its markets.
o Changes in the market – or market segments – over time are intrinsically
unobservable, and these are very difficult to measure and take into account.
Different kinds of segmentation approaches, limits and advantages, and
characteristics of “good” segmentation solutions. Benchmark goals for
success.
Criteria for a Successful Analysis
It is important to have objective standards for assessing the value of any segmentation
solution. We would like to set the bar for such products fairly high, since that makes it
harder for our competition to match our products at lower prices. Importantly, our
criteria for a “good” segmentation need to be quantifiable, so we can both (1) objectively
evaluate any given segmentation solution and (2) track our performance over time, by
client, by industry, and across our client base.
Below is a relatively concise list of criteria for assessing a market segmentation solution:
 Identifiability – Will we recognize the segments when we see them in the
marketplace?
 Substantiality – Even if we can recognize them in the marketplace, are more
favorable target segments large enough to make it worthwhile investing marketing
dollars in them?
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



Accessibility – Even if we can recognize them and they are worthwhile to target, can
we reach them through our targeting efforts?
Stability – Suppose we can find them, target them, and make money in doing so.
Will they be around long enough to respond to our client’s offerings? Or, is there a
lack of stability in the market that will cause segments to shift attitudinally or
behaviorally?
Actionability – Assume we have identifiable, targetable, profitable, and stable
segments. Will targeting them effectively serve the client’s overall strategic needs?
This is not just a question of making a profit, but of how the client positions itself in
the market, leverages its core capabilities, and remains competitive – a much harder
question for most marketers to answer. Yet, it is actionability that delivers the most
vital long-run value to the client’s company.
Responsiveness – Now we are at the point at which our chosen segments are indeed
strategically the best ones we can target – they have value and can be reached
successfully through marketing. But wait! Will they respond to our marketing efforts
(price, product design, distribution channels, and marcom)? If this final hurdle is not
overcome, all has been for naught.
We still have the issue of whether segmentation criteria can be quantified. Some initial,
suggested metrics are shown in the table below. These are treated in more depth in a
subsequent section of this article.
Figure D. Recommended Criteria for Assessing the Value of a Specific
Segmentation Solution, with Suggested Performance Metrics
Criteria for Segmentation
Identifiability
Substantiality
Accessibility
Stability
Actionability
Responsiveness
Some quantifiable measures/metrics
Probability that a predicted segment member
actually belongs to the segment we predict, from
cross-validation tests.
Segment size (percentage of the total market
belonging to the target segment(s)) and economic
value (spending or profitability of the segments).
Also, actual ROI on targeting programs.
Audience TRPs available, return rate on direct
mailings, and other reachability metrics.
Cross-tabulation of earlier against later segment
classifications.
Client satisfaction rating of the solution.
Number of sales closed, hands raised, interests
piqued, etc.
The easiest measurement in the table above, under Substantiality, is segment size – the
results of the segmentation analysis include this automatically. Once segment size is
determined, and we have data on spending and purchase frequency, we can project the
economic value component of Substantiality as well.
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Next easiest is cross-validation for satisfying Identifiability. This is because standard
methods are available using the same software and data as that used to do the
segmentation analysis.
The rest of the segmentation criteria can be evaluated only after the client has
implemented the segmentation and applied it to marketing strategy. In fact, calculating
the ROI to assess Substantiality, while not impossible, is more difficult than for any other
measure. Quantitative methods leveraging recency, frequency, and monetary value of
customers and segments are available but not as yet widely tested. More importantly, can
a follow-up research study be conducted to assess the criteria in a proactive way for the
client?
The use of client satisfaction ratings to assess the actionability of a segmentation solution
seems less objective than the other metrics. Regardless, this particular criterion is crucial
to the marketing services company, because it constitutes the ultimate value of
segmentation. Thus, we all need to maintain constant focus on whether we are meeting
the actionability requirement in our segmentation solutions.
A subsequent section lays out more comprehensively how to assess the Identifiability and
Substantiality criteria for any given segmentation solution. Before that, though, we need
to have an overview of segmentation methodologies.
Different kinds of segmentation approaches, along with their limits and
advantages.
The Magic behind the Method – an overview of Segmentation Methodologies
Despite the dizzying array of segmentation methodologies, there are really only two main
dimensions along which all segmentation methodologies can be positioned.
1. A priori versus Post Hoc.
o An a priori segmentation assumes that the market segments are already clearly
defined in the data. Examples include segments defined by age categories or by
geographic regions. Also included here are segments the client has developed and
rely upon for strategic guidance.
o A post hoc segmentation assumes that the market segments exist in the population,
but are as yet unobserved (see discussion above and Figure B on the issues
surrounding observability – unobserved segments are the sources of market
heterogeneity). Examples of post hoc segmentation include segments defined in
terms of psychographics or product preferences.
2. Predictive versus Descriptive. Every targeted segment has some outcome our client
wants to drive through market strategy, whether it is trial of the client’s new product,
customer satisfaction, increased spending levels, or some other favorable outcome.
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o A predictive segmentation solution explicitly incorporates the outcome and drivers in
the model. That is, different segments have different drivers of the outcome, and this
predicted outcome is sought by addressing the drivers of the target segment(s) via
marketing strategy.
o In contrast, a descriptive segmentation solution seeks only to identify the most
homogeneous subgroups of consumers, with the homogeneity defined by variables of
interest to marketers. For example, suppose that consumers are targetable in terms of
their values, as reflected in VALS2. A valuable descriptive segmentation may then
consist of identifying segments which are homogeneous, within themselves, on the
VALS2 variables.
It is important to note that the objective of the descriptive segmentation is not necessarily
to identify segments from which we might expect a return on marketing investments.
Segments providing a favorable ROI, of course, should emerge from the descriptive
approach, but that would be more of a serendipitous outcome and perhaps require several
different iterations to uncover.
Lack of actionability is a frequent criticism aimed at descriptive segmentation
solutions. However, it is also true that the descriptive approach is more likely to
produce the clearest and most intuitively plausible picture of the market. Why? Because
these are customer or prospect types that we can easily see “in the street.”
When the emphasis is on prediction, not just description, the segmentation model can
group together consumers who tend to respond the same way to the same marketing
stimuli.
If we classify segmentation methods based on whether they are descriptive or
predictive, and a priori versus post hoc, and combine some of the other segmentation
dimensions described in this article, we get Figure E.
Figure E. Types of Segmentation Methods
A Priori
Descriptive
Banner tabulation, ANOVA,
chi-squared tests, t-tests.
Predictive
Selective cross-tabulation
with chi-squared tests,
Regression, Logit,
Discriminant Analysis.
Post Hoc
Hierarchical and K-Means cluster
analysis, latent class cluster analysis,
fuzzy set models, unsupervisedlearning neural networks
CHAID/CART, latent class
regression, latent class choice
models, hierarchical Bayes choice
models, supervised-learning neural
networks.
A priori, descriptive. These methods are classical applications in marketing of what
some fondly call “slicing and dicing” of the data. Banner tabs are the staple product of
all marketing research studies, and ANOVA, chi-squared, and t-tests enjoy a rich
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grounding in experience and credibility among end-users. No one will ever argue with
you about a t-test (perhaps about the underlying assumptions about the data, but not about
the test itself).
A priori descriptive analyses have three fairly strict assumptions:
1. Consumer heterogeneity is measurable typically by dividing up the market into
discrete, non-overlapping groups. Thus, continuous heterogeneity is usually
subsumed into group classification (see Figure A for illustration).
2. Since the rules for grouping, or segmentation, are decided on a priori – in advance of
data analysis – those rules, and the data to be analyzed, are easily observable (see
Figure B for illustration).
3. Appropriately designed, an a priori segmentation satisfies our segmentation criteria of
identifiability, substantiality (with the right cuts), and accessibility, since the
segments are easily observable, their sizes can be determined, and their mailing
addresses can be identified.
It is unclear – some would say unlikely – that a priori, descriptive segments will always
be stable, actionable, or responsive. We might have them well-defined, but they could
very well be distributed completely randomly on other, more important criteria. This is
because a priori, descriptive segments are designed only to describe the market, not
predict how well any given segment will respond to marketing stimuli. In order to
capture segment value in terms of marketing responsiveness, we need a predictive
model.
A priori, predictive. These methods also depend on easily observable data and
predefined segment classification rules. They are perhaps more valuable to marketing
strategy than a priori, descriptive segments. Why? Because they can be measured on
Responsiveness. After all, a regression model – or its discrete version, the choice model
– is intended to predict some valuable consumer response for the marketer. These tools
assume a cause-and-effect relationship between marketing action (product design,
pricing, promotions, channel distribution) and consumer outcomes (shopping visits,
consideration, brand favorability, purchase).
The problem with a priori, predictive segmentation analyses is that they cannot be used
to uncover unobserved (or less easily observed) dimensions that, arguably, more
strongly drive consumer response than do the observable characteristics. Markets may
contain latent segments that cannot be observed in advance of a rigorous research study
and data analysis – that’s what marketing research firms make money on, right? Latent
or unobservable segments, once identified, always offer marketers the strongest inroads
to success (also, see Marketing Manifesto on page 2).
It should be noted that, once latent market segments are uncovered, a priori, predictive
tools can used to predict segment membership in the population. Our main tools for this
purpose are discriminant analysis and logit modeling.
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Post hoc, descriptive. As noted above, marketing dimensions and segments often need to
be teased out of the data – that is, they are not reliably or completely apparent in the
observed ratings data, or database variables themselves. Various exploratory techniques,
such as factor analysis and K-Means cluster analysis, are often helpful in extracting these
latent dimensions and segments. K-Means, in fact, is the method most often used to
do post hoc, descriptive segmentation. Respondents are assigned to clusters based on
how similar they are on key variables – attitudinal or needs-based, behavioral, and others.
The results are (usually) clear-cut, easily described, and intuitively plausible segments.
Unfortunately, the post-hoc, descriptive approach suffers from the same problem as the a
priori, descriptive approaches – it may describe the segments well, but it also may not
predict how well a target segment will respond to the client’s products or marketing
strategy. A secondary drawback, unique to post hoc, descriptive approaches, stems from
the fact that, prior to investigation, segments are not observable. Unless the post hoc
clusters are defined at least in part in terms of observable variables, such as purchase
frequency, age, or geographic region, it may be impossible to find them for targeting
purposes. This may not matter if the client’s marketing activity is primarily national
broadcast advertising, but it makes things quite difficult for direct mail or placing ads on
cable TV or the Internet.
In addition, unless there is a very clear connection between the needs measured in the
study and demand for the client’s products, there is very little guidance from these
solutions on what should be offered to the market and what economic value different
segments might offer. This means we could fail to meet the crucial segmentation criteria
of Substantiality, as well as Identifiability and Responsiveness.
These problems place the researcher in a dilemma. On the one hand, she wants to
identify segments that differ in terms of enduring and consistent attitudes and needs.
This is the best way to make the segments human and thus to think about what the client
can say to them. Clustering study respondents on their ratings of benefits accomplishes
this goal, and K-Means, when done correctly, accomplishes it superbly. (The analog in
database marketing is what we often call “look-alike” models – people with similar needs
based on past purchase behavior).
However, the more you cluster on attitudes or past purchase behaviors, generally, the
more you sacrifice within-segment similarity on observable variables like demographics.
Hence, the less reachable your needs-based segments may become.
The researcher might say, “The market is ultimately created and defined by needs (or
purchase histories). Therefore, my job is to identify these segments as best as possible
and worry about targeting them afterwards.” Moreover, in some industries, finding and
targeting such segments in the population are not so difficult (automobiles are an
example – broadcast advertising potentially reaches everyone). Also, some end-users
may desire crisp attitudinal or behavioral segments because they will be used only for
ideation and long-term strategic planning, not for targeting the consumer in the street or
launching the next brand extension. These uses are exceptions, but they do exist.
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There are scores of segmentation analyses that, despite their huge samples and high
prices, are ultimately never used by the client’s company, because the segments cannot
be targeted by concrete marketing actions. Needs or behavioral patterns may be crisp,
but, unfortunately, demographics are randomly distributed across segments – in other
words, the segments are invisible to targeting efforts. Appropriate messaging to each
segment may be as clear as the summer sky, but the messages cannot be matched to
media in any cost-effective way. The results are client dissatisfaction with and distrust of
large market segmentation studies of any sort.
To sum up, identifying latent segments and defining them in terms of attitudes, needs, or
behaviors alone, while this may describe the market well, can be deadly for the market
segmentation research business. Yet, these still seem to be the methods of choice for
most (but not all) commercial research and database marketing firms.
Post-hoc, predictive. Some of the problems associated with post-hoc, descriptive
segmentation can be addressed by deriving segments based not on who they are, but on
how they respond to marketing stimuli. Such segments, though they may still be
virtually invisible in the population, will presumably buy the product they are predicted
to buy. This helps pinpoint the best new product designs and gauge their market
opportunity value. Offering the right product line allows each segment to “self-select”
into that offering which best meets its needs.
Predicting promotional response works the same way, except we still have the problem of
media allocation unless different segments can be identified with different media
vehicles.
Based on the above discussion of segmentation methods, it seems there are two key areas
which are not easily or completely covered by existing frameworks and tools:
1. Predicting segment responsiveness for the purposes of marketing strategy.
2. Identifying and evaluating unobserved (latent) segments in the population.
That these two areas are difficult to address is actually very good news for high-quality
marketing services vendors – our skills and methods give us a clear advantage in
accomplishing them when our competitors cannot!
Typical segmentation projects.
These include:
o Customized research studies based on primary data collection.
o Database marketing models.
Customized studies. Survey samples produce ratings, rankings, and yes/no responses on
a relatively large number of survey items. These items are designed specifically for a
single study client, not to be conducted more than once unless they are monthly or
quarterly tracking studies. Thus, segments are developed for a specific client in a specific
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competitive market, to uncover previously unobserved targeting opportunities and to
provide the client with competitive advantages and strategic guidance. (See post-hoc
descriptive and predictive model definitions above). The segmentation methodologies
used here include mainly K-Means clustering (of variables or choice/conjoint model
coefficients) and Latent Class Analysis.
Database marketing models. When the models are originally developed here for
segmentation, we produce groupings of records (customers or prospects) in a database
that either (a) look alike, or look like an existing profile, or (b) respond in similar ways to
the same marketing strategy. The segmentation methodologies used here include
mainly a priori segment definitions (provided by people in Marketing and Insights
Integration) and K-Means cluster analysis. However, we have in the past also
applied neural network and genetic algorithm models. Although these have not been
used specifically for segmentation, they are adaptable for that purpose. Most promising
for future applications are neural network segmentation models (self-organizing
networks) and Latent Class segmentation models.
Sometimes database models are developed to predict membership in pre-existing
segments (see a priori, descriptive and predictive model definitions above). Such preexisting segments come from either (a) a client’s customer database or (b) a research
study based on primary data. Usually, we assume that such pre-existing segments need to
be predicted for some external database, for profiling or targeting purposes, and the
quality of our predictions will depend very much on what variables are available in those
databases to use in our models. There are no specific methodologies used here, such
as Latent Class or K-Means, to derive new segments. Instead, we focus on finding
models to predict the segments using discriminant analysis and multinomial logit.
Characteristics of “good” segmentation solutions, along with
benchmark goals for success.
Recap of segmentation issues, bases, and criteria for success. A top-line summary of the
material covered so far is:
 Successful competitive marketing depends on exploiting hidden values in the market,
and the practice of market segmentation offers its best contribution when it is
leveraged for this purpose.
 Target markets exist in the population – we don’t make them up. But neither do
target markets come in neat packages – it takes expertise to uncover them, which is a
key value proposition of the marketing services company.
 Markets are heterogeneous, and segmentation is but one compromise in how to
measure that heterogeneity. Its success will depend on individually customized
consumer preferences are.
 Some dimensions along which market segments can be identified are easily
observable in the population of buyers – for example, demographics. Other
dimensions are hard to observe and may need to be inferred indirectly from observed
patterns of behavior – for example, price sensitivity.
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
Successful segmentation produces valuable target segments for the client. The value
of the target segments can be assessed in terms of their:
o Identifiability – heterogeneity is accounted for, latent dimensions are revealed.
o Substantiality – provides economic value to the client
o Accessibility – targetable through existing media vehicles
o Stability – will retain predictable profile and behaviors long enough to be
targetable
o Actionability – clearly suits the client’s unique value propositions in its
market.
o Responsiveness – will respond favorably to the client’s messages, products,
offerings, and pricing.
There are two particularly difficult areas in doing an excellent
segmentation analysis. These require the marketing services company to
accomplish something difficult, but for the successful marketing services
vendor, offers unique competitive advantages.
1. Uncovering the latent dimensions and
2. Predicting the marketing responsiveness of the segments.
It is important not to consider segmentation performance criteria independently of one
another. For example, a segmentation solution may not need to provide much Stability in
the target segments if they are clearly Identifiable and are only targeted with a series of
short-term promotional offer. All else being equal, however, here are target metrics for
each of our segmentation criteria:
As mentioned earlier, it is clearly beneficial for to quantify these criteria for evaluating a
segmentation solution (see Figure D). This requires looking at, for example, the number
of client sales closed from direct mailings, cross-validated segment classification rates,
ROI on targeting specific segments, cross-tabulation of earlier against later segment
classifications to assess stability, and client ratings of satisfaction with the actionability of
the segmentation solution.
As we saw above, though, there are only two criteria for which metrics can be assessed
before the segmentation solution is implemented in the client’s organization. These are:
o Identifiability – Our ability to correctly predict segment membership. Although we
cannot yet find out whether we can identify them in the market, we can assess
whether the segment assignments in the sample are internally consistent enough to
predict segment membership among respondents.
o Substantiality – Whether we have derived segments of sufficient size and economic
value to make them strategically useful to the client.
Quantifying Identifiability. Here we consider correct classification rates when key
variables – generally including those used to derive the segments to begin with – are used
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to predict each respondent’s segment membership. If there is too much random error in
these predictions, the segmentation solution at least lacks internal consistency. If the
sample is representative of the population, then the segmentation solution also lacks
generalizability. Either or both of these can (and should) be the kiss of death for the
solution.
There are two kinds of correct classification rates to consider: In-Sample and Holdout
Sample.
1. In-Sample classification rates are the rates at which the segments can be correctly
predicted when data on all respondents are used to predict the segment membership
of all respondents.
2. Holdout-Sample classification rates are the rates at which the segments can be
correctly predicted when data from one portion of the sample – the Calibration
Sample – are used to predict segment membership of the other portion of the sample,
the Holdout Sample.
Holdout Sample tests are tougher simply because we limit which data can be used to
predict a given respondent’s segment. On the other hand, “tough” doesn’t always
mean “good.” Any given portion of a single Holdout Sample may or may not be
“representative” of the Calibration Sample, even if it was drawn randomly. Statisticians
have developed several different approaches to minimize this potential problem, and the
most commonly used are those under the heading of “Bootstrap Sampling.” Bootstrap
sampling is a way to “sample from the sample” in a way that eventually produces
o Holdout Samples that are representative of the entire sample (that is, of the
Calibration Sample). There are two easy ways to do bootstrap sampling:
o Sampling without replacement – draw several random samples from the
original respondent base.
o Do not replace these on each draw, but continue to draw until the respondent
base is depleted. Then, project segment membership for each of the Holdout
Samples separately.
o The average classification rates are then used as metrics.
o Sampling with replacement – identical to the previous approach, except the Holdout
Sample members are put back into the respondent base for each successive draw.
o The pluses and minuses of the “with replacement” option are
o Technically,it is more valid statistically, since we are drawing from the same
universe (respondent base) each time we pull a Holdout Sample.
o The minus is that it is possible (even likely) that not all respondents will make
it into any of the Holdout Samples with a finite number of draws.
The best approach is to draw “without replacement.” This allows us to examine the
predictive results for each Holdout Sample individually and to help pinpoint the causes if
the correct classification rates for that sample are unusually low compared to others.
Here we should note that the standard holdout classification approaches in SAS and SPSS
are “one-held-out.” This is a sampling of each respondent in his/her own individual
Holdout Sample, without replacement.
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What size of Holdout Sample is the best? At a minimum, the SAS and SPSS default of
one respondent at a time should be (and currently is) used. When the internal validity of
the segments is in further question, for any reason, it is wise to conduct a 5% and/or 10%,
boot-strapped Holdout Sample test without replacement. The greater the percentage, the
tougher the test. 50% should be considered a maximum, though, based on sound
statistical theory. 50% should never be considered a standard criterion.
Statistical models used for predictive validation. If the segmentation solution was
derived using K-Means or other cluster analysis without a latent class component, both
In-Sample and Holdout-Sample classification rates are predicted using discriminant
analysis or a multinomial logit model.
(Note that these are not the same models as were used to identify or derive the segments
initially, they are used just to test predictive validity once the segments have been
derived.)
o If the segmentation was derived using a latent class or other latent variable analysis,
the In-Sample classification rates are computed from the posterior probabilities of
membership in each of the latent classes. These are provided automatically in Latent
Gold®.
o Holdout-Sample classification rates are also based on the posterior probabilities of
membership, but these need to be computed manually in SPSS or SAS. Note all
classification tests, regardless of segment development model that was used, utilize
the same Holdout Sample draws.
Variables used to predict segment membership in a cross-validation test. So far, no
mention has been made of what variables are used to predict segment membership in
cross-validation tests. There are two different levels of validation at which specific
variables need to be considered:
1. General validation. At a minimum, all the variables originally used to develop the
segments are included. Other variables can be included on a judgmental basis. For
our purposes below, we call this the “Full Variable Set.”
2. Short form validation. One of the key deliverables in most custom studies is a
segment classification model, called a “short form.” This classifier is based on
survey items that can be incorporated into future research studies (or even sales calls)
to identify the segment membership of new contacts. In this case, only a handful of
items can be used, given a limited amount of time to administer them live. Below, we
call this limited number of items the “Short Form Variable Set.”
Benchmarks for classification rates. Below are some benchmark metrics of predictive
performance to support the Identifiability dimension of a segmentation solution. As can
be seen, these depend to a large extent on the type of segmentation analysis to be
conducted (see previous section for a description of the types seen in the table below).
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Figure E. Benchmark Correct Classification Rates for Segmentation Models
Full Variable Set
K-Means/Cluster Analysis
In-Sample
Holdout-Sample
Custom
Research
Studies
80%
75%
Latent Class Analysis
In-Sample
Holdout-Sample
90%
85%
Short Form Variable Set
K-Means/Cluster Analysis
In-Sample
Holdout-Sample
75%
60%
Latent Class Analysis
In-Sample
Holdout-Sample
70%
55%
New Segmentation Analysis using Database Variables
K-Means/Cluster Analysis
In-Sample
Holdout-Sample
Database
Marketing
Models
75%
60%
Latent Class Analysis
In-Sample
Holdout-Sample
80%
75%
Linkage of Database Variables to Existing Segments (assuming more
than 3 or 4 segments)
Customer database + Syndicated
Syndicated Variables Only
Variables
In-Sample
Holdout-Sample
In-Sample
Holdout-Sample
75%
60%
60%
45%
The classification rates in Figure E are meant to be average, rather than minimum
standards. However, average doesn’t necessarily mean “satisfactory,” and we need to
work vigorously on improving these standards over time.
It can be seen from Figure E that, in general, segments derived using a latent class
analysis should have higher correct classification rates in general, although the
advantage is higher for custom research studies than for database models. It should also
be noted that latent class models are not typically available for database modeling due to
the large size of databases and the limitations of processing power in even fast PCs. The
one exception to this is the application of latent class analysis to household grocery panel
data. However, those models typically do not use more than a handful of variables,
compared to hundreds of variables used in the usual database modeling project.
Why, then, do we see less capability to predict latent class segments in the short form
application? The reason is that the short form, utilizing only a small number of survey
items, cannot fully replicate the underlying latent classes that are identified in the original
model used to derive the segments. Instead, the short form predictive model assumes that
the segments are based just on means of observable (i.e., non-latent) variables. More
work needs to be done in this area, since the short form is considered a vital deliverable.
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One alternative is to replace the typical discriminant model in short forms with something
that approximates the latent class structure.
Three general conclusions can be drawn from Figure E:
o First, latent class segmentation solutions are usually more internally consistent (and
hence, more projectable from a representative sample).
o Second, although database models are typically less predictive than custom research
models, relatively powerful models can be built for new segmentation schemes in the
client’s database, as long as client data fields are included in predicting segment
membership.
o Third, the weakest predictive capability resides in linkage models in which preexisting segments are predicted solely by syndicated data fields (not customer data
fields). Unfortunately, most database models tend to fall into this category.
Because of the fact that database clients require a lot of linkage modeling, it may be wise
to prioritize improving the predictive capability of those models. Based on decades of
forecasting model development in academia and commercial research, here is a list of
measures we have taken and hope to take in the near future:
1. We have traditionally used logistic regression model scores to identify the most
appropriate segment into which to classify a record/respondent. We need not always
use linear functions, however, because in SAS both quadratic (non-pooled) and nonparametric classification routines are available. These nonlinear methods tend to
provide up to a 10-percentage-point improvement in classification rates.
2. Due to constraints on computer processing speed, we have not explored a number of
variable transforms and derived variables which, using solely syndicated data, might
provide a better fit to the data and better predictions of segments. These include
principal component (factor) scores and polynomial (squared, cubed, Box-Cox
transform, or logarithmic) expressions of the existing database variables.
This produces our third and final challenge to the practice of market segmentation:
Challenge #3:
o Nonlinear models, variable transforms, and derived variables would most certainly
increase the predictive strength of our linkage models.
o How can these be implemented within a reasonable computing and network
infrastructure?
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Summary of Challenges to Market Segmentation, in Theory and Practice.
Here are the challenges identified in the above discussion. I’ve abbreviated them to
highlight the main points. These are, perhaps, goals to achieve over the next five years.
Challenge #1 – How can market segmentation account for diversity within market
segments, in ways that add valuable marketing opportunities?
Challenge #2 – How can market segmentation better account for both observed and
unobserved (or less easily observed) dimensions of the market? This includes especially
changes in the market – or market segments – over time.
Challenge #3 – how can we utilize nonlinear models, variable transforms, and derived
variables to increase the predictive strength of our predictive – especially linkage –
models?
These are questions even our most advanced theoreticians in marketing science – Michel
Wedel, Wagner Kamakura, and Greg Allenby – would have a hard time answering.
Nevertheless, we should press forward with potential solutions. Perhaps in partnership
with our clients, these questions can and will be aggressively addressed and resolved.
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