The benefits of archetypal prototyping when profiling market segments
Chris Sherley, Charles Sturt University
Mark Morrison, Charles Sturt University
Roderick Duncan*, Charles Sturt University
Abstract
Prototyping is a market profiling technique used to develop more detailed descriptions of
market segments than is possible with traditional segmentation analyses. This method
describes real people that embody the salient features of a market segment. Morris and
Schmolze (2006) demonstrated an approach that, unlike previous studies, used predominately
quantitative procedures to identify prototypes. We extend Morris and Schmolze’s (2006)
approach by utilising more sophisticated quantitative tools and archetypal interviewing
techniques to build information rich segment profiles for Australian attitudes to climate change
policy. Our improvements allow an improved selection of prototypical respondents, and the
results highlight the importance of using both qualitative and quantitative analysis procedures.
For practicing market and advertising researchers, this new technique offers a fast and cost
effective way of researching those consumer traits such as archetypes that are often only found
using extensive qualitative research.
Keywords:
Archetypes; prototypes; market profiling; segmentation; target marketing; climate change
policy
*
Corresponding author: Dr. Roderick Duncan, Email: rduncan@csu.edu.au, Postal Address: School of Accounting
and Finance, Faculty of Business, Panorama Avenue, Bathurst NSW 2795 Australia.
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1. Introduction
Traditional profiling procedures are often limited in their ability to explain the drivers behind
consumer behavior, however understanding these motivations can be an important part of
advertising and marketing research. For this reason, there is a need to develop better research
procedures that not only identify what behaviors and attitudes define a market segment, but
why these trends occur.
Wansink (2000) suggested that traditional profiling techniques often result in over
generalisations and, at times, may neglect important underlying attitudes or motivations of a
market segment. Morris and Schmolze (2006, p. 291) also noted that:
“The weakness of these [traditional profiling techniques] however is that they are, at
best, approximations of tendencies within a segment, and at worst serious
misrepresentations of the diversity within a particular segment.”
Given these limitations, prototyping has been suggested by a number of authors as a more
effective way of profiling targeted market segments (Hogg and Reid, 2006; Morris and
Schmolze, 2006; Wansink, 2000).
A prototype is a human representation of a market segment that embodies the typical
behavioral, attitudinal and emotional responses of that segment to marketing stimulation
(Hogg and Reid, 2006; Morris and Schmolze, 2006; Wansink, 2000). In recent literature,
researchers have attempted to discover reliable methods of identifying and profiling
representative prototypes from a data set (Hogg and Reid, 2006; Loken, Barsalou, and Joiner,
2008; Morris and Schmolze, 2006; Wansink, 2000).
However, these methodologies are often subjective and require the researcher to make
personal decisions regarding who may be considered prototypical of a segment. Wansink
(2000, p. 34) argued that this issue can limit the effectiveness of prototyping as a research tool
because “Our personal background heavily influences the degree to which we understand the
psychology of people of different race, religions, ages or geographic backgrounds”
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To increase objectivity, Morris and Schmolze (2006) suggested an innovative method for
identifying prototypes in a market segment. Core members (prototypes) are found using a
series of quantitative steps in which respondents with the highest probability of categorical
membership to a market cluster are selected as representatives of the broader cluster. Once
they are selected, these people are then profiled using in-depth interviews.
However, while Morris and Schmolze (2006) refined the process of identifying and selecting
prototypes, there were several limitations associated with their application. In terms of their
quantitative analysis tools, they used k-means clustering to identify segments and single rather
than multiple discriminant analysis to identify segment cores. The use of k-means clustering is
problematic if there is missing data in the survey responses, as discussed below. We found a
multiple discriminant analysis to be more effective than a single discriminant analysis at
identifying probabilities of group membership. There was also an issue with Morris and
Schmolze’s (2006) final discriminant analyses in which they narrowed down the remaining
members using probability of group membership scores.
We found that a substantial
proportion of respondents scored 100%, so that the analysis did not effectively differentiate
between members. Along with this, Morris and Schmolze (2006) used an expert panel when
selecting the final prototypes – an essentially subjective step and not consistent with the
objective approach that they recommend. Instead, we offer a more process without the use of
experts in which members that are closest to the cluster centroid are selected. Along with
these quantitative issues, Morris and Schmolze’s (2006) follow up qualitative research was
limited and produced little new information about the prototype respondents.
Given these limitations, we seek to make a number of methodological improvements to Morris
and Schmolze’s (2006) original paper. This included replacing the k-means clustering procedure
with a latent class cluster analysis, using multiple discriminant analysis procedures, as well as
using a new objective procedure for identifying respondents at the centre of the archetypal
pools. Finally, we offer a new interview structure for the qualitative research which was
developed from archetypal literature in the marketing and advertising fields (Maso-Fleischman,
1997; Peattie and Peattie, 2009; Randazzo, 2006; Rapaille, 2007).
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In the results, we find evidence that the new prototyping methodology is effective in providing
a number of key insights that were not apparent in previously conducted segmentation analysis
procedures. The archetypal profiles of the selected prototypes indicated that there were
unique pools that differed according to a range of demographic variables, not discovered using
more traditional segmentation techniques. These profiles indicated that there were likely to be
a number of differing motivations for the attitudes identified at a segment level.
While the quantitative analysis was found to be useful in identifying differing prototypes within
a market segment, the analysis also demonstrated the critical importance of the qualitative
interviews in producing comprehensive profiles of prototypes.
Next, prototypes and
archetypes are defined and the rationale for their usefulness in advertising is discussed.
For practitioners, this new procedure has three key benefits.
Firstly, having a better
understanding of the motivations that drive consumer behaviour in target markets may allow
more appropriate marketing strategies to be developed for those markets. Secondly, for
research involving quantitative then qualitative procedures, archetypal prototyping may
uncover important preliminary findings in the quantitative research that can be utilized to help
direct more extensive follow-up qualitative research. As the number of interviews required
when prototyping is relatively small, this technique can be a cost effective way of ensuring that
follow-up qualitative research is focused on those areas that are most crucial to understanding
consumer behaviour or attitudes.
The final benefit for practitioners using archetypal
prototyping is multi-faceted. This procedure can provide a more nuanced description of market
segments than is possible using quantitative research. On the other hand, it is has more
statistical basis than is perhaps the case with some qualitative research strategies, this can be
important when presenting research to clients that are preferably disposed to quantitative
research.
2. Prototypes and archetypes and their use in advertising
2.1 Prototypes
A prototype is a human representation of a market segment that embodies the typical
behavioral, attitudinal and emotional responses of that segment to marketing stimulation
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(Hogg and Reid, 2006; Morris and Schmolze, 2006; Wansink, 2000). Wansink (2000) suggested
that prototyping is a quick and effective way of identifying key consumers that closely resemble
a broad market segment. This technique builds on segment profiles developed using traditional
market research methods, such as cluster analysis.
Hogg and Reid (2006) argued that members of a market segment share a series of visual, audio
and olfactory schemas in their subconscious cognitive faculties. These schemas influence
consumers by encouraging particular responses to certain stimuli. Ratneshwa and Shocker
(1988) referred to this trend as ‘prototypicality’.
Prototypicality is drawn from the
psychological categorization field and refers to common cognitive, emotional and behavioral
responses in groups of people.
For example, if an Australian consumer walked into a
McDonald’s in India they may be surprised to find that there is no beef available. This is
because most Australian consumers have an underlying schema of what should be expected at
a McDonald’s restaurant. When this schema is not met, the consumer may feel a sense of
disbelief or dissonance towards the initial experience (Babin and Babin, 2001).
Conflicts between consumer schemas and reality can be an issue when developing advertising
communications. Certain imagery may trigger unexpected responses from targeted consumers.
These may differ from what is considered rational by the researchers developing the
communications.
Identifying prototypes from a targeted population gives researchers the ability to study the
prototypicality of that group. Wansink (2000, p. 30) argued that studying these members
compared to more traditional market profiling techniques will allow companies to “develop
specific characteristics [of a market segment] as opposed to the non-descriptive statistics
provided by demographics”.
In a study testing the effectiveness of one prototyping
methodology, 23 brand managers were given a sample of 261 respondents. Twelve brand
managers were asked to use traditional clustering procedures to predict which of these
respondents were likely to be members of a public radio station. The remaining 11 brand
managers were asked to use prototyping methods. The results showed that those managers
using traditional segmentation analysis procedures predicted cluster membership correctly 45%
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of the time, while those managers using prototyping predicted correctly 87%. While the
potential advantages of prototyping are evident, there is one fundamental limitation with its
use: deciding who is prototypical of a market segment.
Only specific members of a social group can be treated as fully representative of that group.
Hogg and Reid (2006) referred to these members as prototypical or core members of the group.
These members are held to embody the prototypicality of a social grouping such as a market
segment. Cantor and Mischel (1979, p. 188) argued that these people or prototypes can be
used to identify a “large set of more or less associated qualities that together represent or
exemplify the meaning of that personality category [the target social group].” For companies
conducting marketing or advertising research, profiling these central or core members would
allow a much more in-depth understanding of who the target customer really is.
Identifying a core member of a market means it may be possible to observe important
consumer traits that can be lost using traditional market profiling. However, a number of issues
arise. Firstly, individuals must actually be representative of the broader segment. Wansink
(2000) argued that if the wrong or too few prototypes are selected, the researcher may be
given a narrow indication of the segment. Similarly, if too many consumers are selected, too
much information would be produced to make the process viable. For this reason, Morris and
Schmolze (2006) suggested using an innovative quantitative approach to address these
problems.
Qualitatively, prototyping not only requires the researcher to identify representative members
of a market, but also conduct an in-depth analysis of various consumer traits to develop
information rich consumer profiles.
Morris and Schmolze (2006) suggested that Jungian
archetypes may be effective at providing the basis for this analysis. That is, we may observe the
prototypicality of a market segment by studying the archetypes of specific respondents.
2.2 Archetypes
Archetype literature first became prominent with the psychologist Carl Jung in the 1950s.
Recently, the application of archetypal research has become more accepted in the advertising
field (Maso-Fleischman, 1997; Peattie and Peattie, 2009; Randazzo, 2006; Rapaille, 2007).
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Archetypes can be broadly described as a set of subconscious associations or schemas learnt
through previous sensory stimulations (Woodside, Stood, and Miller, 2008). Very similar to
prototypicality, archetypal associations trigger particular emotional, behavioral and attitudinal
responses when the same stimulus or very similar stimuli are re-encountered (Knox, 2004).
There are a number of iconic archetypal figures that have been utilized by advertisers over the
years.
An example of a successful archetypal advertising campaign can be seen in the famous
Marlboro cowboy mascot who spearheaded the company’s marketing communications in the
1950’s and 60’s. Randazzo (2006) suggested that in the USA, the image of a cowboy activates a
distinct emotional response in certain consumers. To baby boomer consumers, the cowboy
represents the archetypes of FREEDOM and the PIONEERING SPIRIT; this archetypal association
was learnt by baby boomers as children when the cowboy was a prominent feature of US
entertainment media. Randazzo (2006) accredited much of Marlboro’s commercial success to
the company’s cowboy mascot. He suggested that the cowboy made American consumers feel
a subconscious loyalty to the company. In essence, the company became the U.S. pioneering
spirit to these consumers.
In recent years, archetypal interviewing has become an accepted qualitative research technique
by marketing research firms and corporations. Well known companies such as Chrysler, Shell,
the Ritz-Carlton, Subaru and many more have utilized this interviewing style for new product
development and market communications (Randazzo, 2006; Rapaille, 2007; Shalit, 1999). The
technique aims to identify underlying archetypes and the way they may affect consumer buying
habits.
As can be seen, archetype and prototype theories share very similar backgrounds. Underlying
schemas or archetypes are shared by members of a consumer group. These archetypes
encourage particular responses to various marketing stimuli.
2.3 Archetypal prototyping
We define an archetypal prototype as: a detailed description of a person who may be used to
represent the archetypal, emotional and behavioral tendencies of a wider group of people. In
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Morris and Schmolze’s (2006, p. 290) original study, they used an outdated description of
archetypes that made the overall procedure somewhat confusing. They defined an archetype
as:
“A detailed description of a single person who represents a particular expression of a
wider group of people along a prescribed dimension.”
This definition is closer to that of prototyping. Therefore, we describe Morris and Schmolze’s
(2006) procedure as archetypal prototyping.
2.4 Case study
The data for our prototype analysis were collected from a broader case study into Australian’s
attitudes towards climate change. This involved a replication of Maibach, Leiserowitz, RoserRenouf and Mertz’s (2011) Six America’s study. The intention was to observe the differences
between Australian respondent’s attitudes towards climate change and those of American
respondents. A sample of 1927 respondents was collected using an online database. The
sample was selected to be representative of the Australian population based on 2007 Census
data.
Six Australian clusters were defined: Alarmed, Concerned, Cautious, Disengaged, Doubtful and
Dismissive. The segments were located on a scale; Alarmed were the most inclined toward
climate change action, while people in the Dismissive cluster were the least likely to believe in
climate change.
The segmentation analysis was done using a latent class analysis (LCA) clustering procedure.
LCA is a classification technique used to group respondents using maximum likelihood methods
(Maibach, et al., 2011). Madgison and Vermunt (2002) suggested that LCA is a much simpler
form of clustering, not requiring standardization of variables. LCA is also able to handle
nominal, ordinal, and continuous variables simultaneously meaning the technique is more
robust than alternative clustering procedures such as hierarchical or K-means clustering
(McCutcheon, 1987). LCA is also capable of handling missing data (Maibach, et al., 2011). This
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is particularly useful for the prototype analysis and will be discussed further in the methodology
section.
For the prototype analysis, the Cautious (n=502) and Disengaged (n=385) clusters were
selected. These two clusters were the most relevant to policy makers as they were most likely
to capture the median voter, and hence were chosen for further analysis.
The Cautious cluster was found to believe in climate change and support climate change action.
However, they were very cautious about policies that may threaten them fiscally such as a
carbon tax. They were also unlikely to believe that climate change policies in Australia would
have any real effect.
The Disengaged cluster was slightly less inclined towards supporting climate change action.
The members of this segment were hesitant to believe that climate change was being caused by
humans. They also felt that policies such as a carbon tax were a bad idea. Instead, they
preferred direct action policies.
They were also unlikely to believe any government
communications on the issue.
These segment profiles indicated distinct attitudinal characteristics. However, the profiles were
limited in their ability to demonstrate other relevant trends within the data that may help
explain why the segments have these particular attitudes. For example, in the Cautious cluster,
approximately a third of people have full time work, while a fifth of people are retired or on a
pension. Although these people may share similar attitudes towards climate change, these
socio-demographic differences may be important. However, in the initial cluster analysis they
were ignored.
People in a segment who have similar attitudes are often treated as having the same
motivations for those attitudes arising.
However, while segments may appear to be
homogenous, there can be multiple underlying factors that divide members into sub-categories
(Woodside, et al., 2008). Archetypal literature suggests that researchers should seek to not
only explain what people feel, but why they feel it (Hofstede, 1997; Peattie and Peattie, 2009;
Randazzo, 2006; Rapaille, 2007). In this example, the 20% of Cautious respondents that are
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aged pensioners are unlikely to have the same motivations for their attitudes as the younger
35% that have full time work. This issue is what the archetypal prototyping process aims to
address.
3. Method
Although our methodology is based on Morris and Schmolze’s (2006) prototyping methodology,
we have refined a number of steps. Table 1 briefly outlines the steps used in this study.
Stage 1: Initial segmentation analysis
In the first stage of the process, we conduct a cluster analysis. While Morris and Schmolze
(2006) used a K-means analysis in their study, we used LCA because it is able to model with
missing data. When choosing a clustering procedure like K-means, any missing data will often
be converted to mean scores. This poses a problem as the aim of the procedure is to identify
the most appropriate cluster centroids for prototyping. If missing data is converted to mean
scores, respondents that may have originally been segment outliers can be inadvertently
moved to the cluster centre, which can lead to inappropriate prototypes being selected.
Stage 2: Identify core members
In the second stage we are looking to identify those members that have the highest probability
of belonging to the segment. To complete this task we used SPSS to conduct a standard
multiple discriminant analysis on all the segments, not just the two that would be prototyped.
This step contrasts to Morris and Schmolze (2006) who undertook a single discriminant analysis.
While the single analysis does show membership probabilities, we found that the multiple
discriminant analysis had better discriminating power and the Wilks Lambda produced higher
significance levels.
We used a discriminant analysis because of the functions ability to predict respondents’
likelihood of categorical membership (Hair, William, Babin, and Anderson, 2010). Although
certain regression approaches such as binary logits will also predict group membership,
standard software packages do not have the ability to test multiple groups simultaneously
without reference to a base category.
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The items used in this discriminant analysis are the same as those used in the initial clustering
procedure. For our analysis we used the 36 attitudinal items which were used by Maibach et al.
(2011) to define their original six clusters. We use these variables because we are trying to
identify the core members of the clusters.
When the analysis was completed, members with the lowest probabilities of group
membership were removed from the selected clusters. Morris and Schmolze (2006) suggested
that eliminating approximately two-thirds of the segment population at this point leaves
sufficient members to conduct the remaining steps of the analysis.
Stage 3: Archetypal pool analysis
In the archetypal pool analysis stage, we conduct another LCA using socio-demographic
variables. This allows us to identify various sub-clusters or ‘archetypal pools’ that may exist
within the broader cluster. These groups show differing socio-demographic profiles that may
not be identified in the initial clustering solution which was based on attitudinal items (Morris
and Schmolze, 2006). Each of the archetypal pools identified within a segment are likely to
have differing behavioral patterns and archetypal profiles.
Morris and Schmolze (2006) used a hierarchical cluster analysis. However, we again used a LCA
as it is a more appropriate clustering procedure. Using Morris and Schmolze (2006) as a guide,
we specified a three cluster solution for our archetypal pools as this is a feasible number of
prototypes from a marketing perspective.
Stage 4: Select the prototypes
In the final quantitative stage, the individuals that will be used as prototypes are identified. A
stepwise multiple discriminant analysis is run and the function scores are saved. The same
socio-demographic items used in the previous analysis are treated as predictive variables while
the archetypal pools are treated as categorical dependent variables.
Unlike the first
discriminant analysis, the goal is not to identify members with the highest probability of group
membership.
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Instead, using functional scores, we identify members that are closest to the cluster centroid.
These respondents may not have the highest probability of group membership. However, their
closeness to the centroid suggests that these people are most likely to embody their archetypal
pool. This is because these members have scores that are closest to the mean points of the
segment.
Using the saved function scores, a scatterplot is developed. From this, we identify the mean
function points on both the X and Y axes and the closest members to this centroid are selected
as prototypes.
This step differs from Morris and Schmolze’s (2006) final selection in which they undertook
another two discriminant analyses, keeping the members with the highest probabilities of
group membership. They were left with a small pool of approximately seven people. A panel
of experts then selected the members that would be used as prototypes. The process used in
our study is arguably easier and less subjective as it avoids the use of experts and provides an
objective basis for the selection of respondents as prototypes.
Stage 5: Archetypal profiles
Once the final prototypes are selected, Morris and Schmolze (2006) recommended undertaking
qualitative interviews. As they indicated, the process was drawn from Jungian literature.
Hence, we chose to utilize a semi-structured archetypal interview format. A number of authors
in the marketing field have suggested question types that should be included in archetypal
interviews (Knox, 2004; Maso-Fleischman, 1997; Peattie and Peattie, 2009; Randazzo, 2006;
Rapaille, 2007; Woodside, et al., 2008).
We used a structure similar to the archetypal focus group suggested by Rapaille (2007). We
begin by asking the respondent about themselves, their hobbies and their family. This step is
very relaxed and occurs much like an unstructured conversation. The goal is to understand the
behavioral and psychographic profile of the respondent, as well as build rapport.
In the second step of the interview, a projective technique is applied (Donoghue, 2000) to begin
discovering why the respondent feels and acts the way they do regarding the topic. Rapaille
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(2007) suggested that this may help to provide access to respondent archetypes. From these
archetypes we may begin to identify what marketing strategies may engage these prototypes.
Using this projective technique, the moderator begins by telling the respondent he or she is an
alien from another planet (Rapaille, 2007). They then ask the respondent to describe the
research topic (climate change) in as much detail as possible. This exercise serves to get the
respondent thinking about the topic without being directed toward specific issues by the
researcher. The light hearted nature of the exercise also allows the researcher to continue
building a positive relationship with the respondent.
In the third section, another series of projective exercises involving free association are used. A
number of relevant words such as “global warming” and “environment” are stated. The
respondent is asked to indicate any images, thoughts or words that come to mind when each
word is stated. The point of this exercise is to uncover underlying archetypal schemas that may
exist. For example, when asked about climate change, all of the respondents spoke about
clouds. This type of response indicates that marketers should be cautious of what images they
use when discussing climate change. Using images that are not clouds or the sky may challenge
Cautious and Disengaged consumers’ interpretation of the topic.
Next the respondent is shown a number of pictures with a bland, but topical call to action on
them, such as: “You can help stop climate change”. The pictures are all archetypal; for
example, the archetypal image of a polluting power plant, the clean image of a wind farm, the
farmer standing over his crop, the image of children walking into the future through nature,
and so on. The respondent is then asked to describe the pictures and indicate the emotions
they feel when they see the image. This exercise provides an indication of what marketing
imagery is likely to stimulate consumer action. This section is completed by running the same
exercise with six phrases that also make a call to action. The respondent is again asked to
communicate their feelings for each and what their behavioral response may be.
In the last part of the interview the respondent is asked their thoughts on a number of issues,
such as the role of science in the climate change debate. This section is located last so as to not
influence the respondent prior to the archetypal exercises occurring. These questions are
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aimed at clarifying the respondent’s conscious thoughts on climate change issues. These
questions have no archetypal implications. However, they are helpful when attempting to build
contextually relevant consumer profiles.
4. Results
4.1 Quantitative
Stages 1 and 2
As previously noted, six clusters were found by Maibach et al. (2011) and then in our replication
study. From these six clusters, the two moderate segments Cautious and Disengaged were
selected for prototyping.
In order to identify the core members of each segment, the
probability of group membership scores for the Cautious and Disengaged segments were
identified using multiple discriminant analysis. For the Cautious segment, respondents that
scored less than 98.7% of group probability were removed. This left a total of 167 core
members from the original 502. For the Disengaged segment, members that scored less than
98.8% probability of group membership were removed. This left a total of 129 core members
from the original 385. These people provided the basis for the archetypal pool analysis.
Stages 3 and 4
Seven socio-demographic variables were used in the LCA to define the archetypal pools. These
were: gender, education, age, household income, religion, political orientation, employment
status.
The statistics for the LCA suggested that the optimal number of clusters was larger than three
for both the Cautious and Disengaged segments. However, using more than three prototypes
per segment is not practical, and the archetypal pools for each of the segments did not present
notably different profiles when a larger cluster solution was used.
The Cautious archetypal pools were labeled: C1, C2 and C3; while the Disengaged segments
were labeled D1, D2 and D3. Each pool had a unique set of socio-demographic variables that
were not highlighted in the original segmentation. For example, the first Cautious pool C1
(n=78) was 83% women, had an average age of 39, most of the pool had completed a trade
14
certificate or TAFE course, approximately 90% of the pool did not work or worked part time,
and most had at least one child living at home. Finally, the average household income of this
segment was approximately $65,000 - $77,999 per year ($1,250 - $1,499 per week).
This profile differed quite notably from the next two pools C2 (n=59) and C3 (n=30).
Approximately 98% of respondents in C2 had full time work. The average age of the pool was
40 and the majority had completed a university degree. Gender was split with 56% of members
being male. C3 differed again having approximately 93% retirees. The average age of the pool
was 63 years old, 70% of the pool was male and the majority had not completed any education
beyond high school.
These socio-demographic differences may mean there are a number of alternative motivations
for the same attitudes (Morris and Schmolze, 2006). For advertisers and marketers, this could
mean that promotional campaigns targeted at specific segments may not be appropriately
aligned. Identifying prototypes and utilising in-depth qualitative interviews may help identify
more relevant promotional activities that would have a greater likelihood of appealing to the
various archetypal pools within a broad segment.
To isolate the prototypes, we conducted a stepwise multiple discriminant analysis. The analysis
produced mixed results. For the Cautious cluster, three distinct and tightly bound clusters were
identified. Figure 1 shows a Scatterplot of the three Cautious archetypal pools. As can be seen,
there was a small number of outliers. These members affect the placement of the group
centroids so they were removed. This step helps provide an accurate indication of the group
centroid and, in turn, the identification of the most appropriate prototypes. In contrast to the
Cautious segment, the Disengaged analysis (figure 2) produced pools that had less central
tendency. As with the Cautious analysis, outliers were removed.
A Scatterplot of the function scores for each archetypal pool was used to make the final
prototype selection. Members that were closest to the centroid were identified as the possible
prototypes. Figure 3 shows an example of this step using the C1 archetypal pool.
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An issue with discriminant analysis at this step relates to missing data. Although the LCA was
able to run without converting missing scores to means, the discriminant analysis cannot do
this. In the Disengaged pool; D2, the most central member had a number of missing scores.
Hence this person was not interviewed.
4.2 Qualitative results
In this section, we report the results of the C1 prototype that was identified using the approach
outlined in this paper. For each of the prototypes interviewed, including the one discussed
below, the results indicated that the prototype was representative of their archetypal pool.
Furthermore, the follow up interviews revealed many relevant characteristics that were not
evident solely from the quantitative data.
Table 2 shows the quantitative profile of the C1 archetypal pool. Although this gives us some
indication of who is in the sub-segment, it is impersonal and gives no real indication of who
these people may actually be.
4.3 Archetypal profile
In order to present the final profile, we develop a narrative from the results of the quantitative
questionnaire and the in-depth interview describing the prototype. This humanizes the subsegments resulting in a rich profile of the people in each segment. Although this format is
similar to the more traditional Pen Portrait (Ratneshwar and Shocker, 1988), there are a
number of improvements that need to be noted:
1. We are able to avoid approximations and over generalisations. For example, if we were
describing the C1 archetypal pool we’d say that the majority of C1 think climate change
is real and it is caused by man. However, this is not entirely accurate. When we
interviewed the C1 prototype the results indicated that while she believed climate
change is caused by mankind, she also believes that the climate will always change
because of a natural cycle.
2. The prototype is given the opportunity to explain why they think and act as they do.
The original C1 profile showed that the respondents were apathetic towards climate
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change actions. However, when given a chance to elaborate on this, the prototype
explained that she strongly supported any pro-environmental activities. The problem
was that she felt many government policies directly threatened the well being of her
family by targeting their weekly budget. Therefore, she was unable to support many
policies such as a carbon tax.
3. The researcher is given the opportunity to describe what marketing communications are
most likely to engage the sub-cluster. During the third section of the in-depth interview
the prototype showed strong support for communications that focus on the impacts
climate change may have on farmers.
A simple pen portrait is unable to provide this level of detail. Therefore, the researcher will
often have no real direction with which to begin developing further advertising research or
activities. Table 3 shows the narrative developed by the researchers for the C1 prototype to
whom we allocated the pseudonym, Angela. All of the information in the narrative was
taken from either Angela’s questionnaire results, or from the Archetypal interview. The
bolded text indicates information that was uncovered in the interview. This was also true
for the other prototypes, and gives the reader some indication of the value of the follow up
research.
5. Conclusion
Effective and reliable market profiling techniques are an important part of market research.
Successful marketing strategies can often rely on having an in depth understanding of who
constitutes a target market (Wansink, 2000). The archetypal prototyping technique outlined in
this paper offers a more nuanced consumer profiling process than traditional techniques that
may overlook important underlying trends within a market.
For practitioners, this new
procedure may be beneficial for three reasons. Firstly, having a better understanding of the
underlying motivations that drive target market behaviour may allow more appropriate
marketing strategies to be developed.
Secondly, for mixed methods research involving
quantitative then qualitative research, archetypal prototyping may uncover important
preliminary findings that can be utilized to help direct follow up focus groups or in-depth
17
interviews. As the number of archetypal interviews required is minimal, this can be a cost
effective way of ensuring that extensive follow-up qualitative research is focused on those
areas that are most crucial to understanding consumer behaviour or attitudes. The final benefit
for practitioners using archetypal prototyping is that this procedure can provide a more human
description of market segments than is possible using purely quantitative research. On the
other hand, it is has more statistical reliability than is perhaps the case with some qualitative
research strategies, this can be important when presenting research to clients that prefer
research with a strong statistical basis.
The motivation for this research was to perform a methodological improvement of Morris and
Schmolze’s (2006) consumer understanding framework. The results of this paper present a
number of interesting findings.
5.1 Conclusions for researchers
Firstly, we found that the archetypal prototyping technique provided a richer description of the
Cautious and Disengaged segments than what was identified in the replication of Maibach et
al.’s (2011) study. While the original segmentation analysis identified a series of consistent
attitudes, our results showed evidence that there may be a number of different motivations for
these attitudes occurring. For example, the C1 archetypal pool and its prototype, Angela,
showed that this group of people were cautious of climate change action because they felt it
may threaten their family’s fiscal situation. However, this differed from the results of the C2
and C3 prototypes.
The C2 sub-cluster were opinionated professionals that considered
themselves to be well informed on the climate change debate.
The results of the
corresponding interview indicated that the C2 prototype felt he was being ignored by the
government. This is why he is cautious of climate change action. Different again, the C3
archetypal prototype was a less informed retiree. His caution of climate change was driven by
underlying apathy. Although he supports climate change action, he doesn’t think he is the man
for the job.
The second finding was that the prototypes were representative of the archetypal pools. As
stated in the introduction, traditional market profiling techniques can often lead to issues of
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over-generalising and subjective interpretations of data (Morris and Schmolze, 2006; Wansink,
2000). However, by identifying real individuals that embody the salient features of a market
segment, it is possible to reduce these limitations and increase the level of information we have
for a segment. Although Morris and Schmolze (2006) suggested in their original study that the
archetypal prototypes would in fact be representative, they provided no evidence to support
this claim. However, this study shows the technique is valid.
The third finding is that follow-up archetypal interviews with the prototypes are a useful tool
for uncovering important data. While a good preliminary profile of a prototype may be
developed using their quantitative responses, we found that the qualitative interviews gave the
researchers a much better understanding of who the prototype was and why they act and feel
the way they do.
Perhaps the most beneficial part of the qualitative interview was the ability to test preliminary
marketing communications. By exposing the respondent to archetypal stimuli we were able to
test what words, images and climate change messages were most likely to engage the
respondent (Randazzo, 2006). As the prototype is representative of their archetypal pool we
can then translate these results into what will be effective for the majority of the sub-segment.
Our final finding was the benefit the new methodology gave to the validity of this technique.
Morris and Schmolze’s (2006) original study was an innovative way of selecting prototypes from
a data set. However, the steps used by Morris and Schmolze were, at times, limited. We found
that LCA and multiple discriminant analyses used in this study had fewer of these limitations
than with the previously used K-means and single discriminant procedures. We also found that
the final two discriminant analyses used by Morris and Schmolze (2006) gave a misleading
indication of who may be the most appropriate prototype. Instead, we found that identifying
the most centrally located members using their functional scores gave a much better idea of
who would ideally represent the archetypal pool.
5.2 Other issues for further research
Nonetheless, there are a few limitations of this technique that should be noted. Wansink
(2000) suggested that prototyping may be too in-depth. This can lead to communication
19
strategies that are too specific. However, it is possible to overcome this by running a thematic
analysis over all three prototypes from a broad segment. Reoccurring themes may be treated
as applicable to the whole cluster. For example, the Cautious prototypes all responded well to
communications about Australian farmers and farmland.
Another issue is the impact of missing data on the final solution. Although the LCA can model
missing data, this is not the case with discriminant analysis. Missing scores need to be
converted to mean scores, meaning respondents with a large amount of data missing will be
moved to the centre of the cluster. For this reason, we suggest closely studying the results of
any potential prototype to ensure that they do not have an unacceptable number of missing
scores.
Despite these issues, archetypal prototyping is an innovative technique that has the potential to
produce a better understanding of target segments in a population. The results of this paper
indicate that, with the improvements to Morris and Schmolze’s (2006) original methodology,
this technique allows researchers to objectively and accurately identify respondents to be
prototypes. By interviewing these people, one is able to build comprehensive archetypal
profiles of broader market segments. From this, advertisers and marketers may develop
effective communication strategies.
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Tables and Figures
Table 1: The five steps of the archetypal prototype analysis procedure
Stages
1
Stage name
Initial segmentation analysis
Details
Run the initial cluster analysis using attitudinal
items
Select segments for prototyping
2
Identify core members
Run a multiple discriminant analysis on all clusters.
Remove the members from the selected segments
with the lowest probability of group membership
(approximately two-thirds of the segment)
3
Archetypal pool analysis
Run a cluster analysis on the remaining members
using socio-demographic items
Select the number of clusters or 'archetypal pools'
4
Select the prototypes
Run a stepwise discriminant analysis on the
archetypal pools
Save functional scores
Select members that are closest to the mean
functional scores
5
Develop archetypal profiles
Conduct qualitative interviews with the selected
respondents
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Figure 1: Scatterplot of the of the discriminant function scores for the Cautious archetypal pools. The
results show three distinct clusters supporting the results of the second LCA.
22
Figure 2: Scatterplot of the of the discriminant function scores for the Disengaged Archetypal pools.
23
Figure 3: Scatterplot of the final scores for archetypal pool C1.
-
The cross hair shows the cluster centroid. The circled members are the possible prototypes for the pool.
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Table 2: Mean scores of the C1 archetypal pool.
C1 (means)
Is climate change real? (1 =
yes, 2 = not sure, 3 = no)
1.35
Is it man made or natural? (1
= yes, 2 = other, 3 = natural)
1.04
Age
Income
39.5
$46,800.00
Education
Trade or TAFE
qualification
Gender (1 = male)
0.17
Employment status
76% - Stay at home
mothers/ part time
work
Household description
61% - Couple with at
least one child living at
home
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Table 3: The archetypal prototype narrative
Angela
Angela (35) is one of many married women in Australia that are cautious of climate change. She lives with her
husband and two children in the outskirts of a capital city. Angela has completed studies at TAFE for her work in
administration.
However, she now takes pride in being a stay at home mother. This puts extra strain on her family’s weekly
budget meaning that they must be aware of any extra costs in their lives.
Angela believes in climate change, she also believes it should be a high priority issue for the Australian government,
however, only if it will have a moderate impact on the economy.
She goes out of her way to reduce her personal impact on the environment with simple cost effective methods
such as having her own vegetable garden and recycling. She believes that every person can make a difference
by taking these small steps. Angela has noticed the area around her being affected by climate change and she is
concerned about the impact this will have on her family’s health.
Despite these beliefs, Angela is a Liberal voter. She believes that direct action policies are the best way for Australia
to reduce climate change. Angela is not interested in Julia Gillard or Bob Brown’s policies on the issue.
She feels that these politicians have lied to her and they will do it again. If she is to support climate change
policies it can only be from people that are trustworthy. People that are trustworthy are those politicians that
are looking out for the good of her family. Angela is threatened by new taxes as she comes from a single income
household. Any new taxes may threaten her family and as a mother this is unacceptable. Instead, Angela thinks
that the government should support companies that have a low impact on the environment, while high polluters
should be punished.
Angela is likely to use Newspapers and pop media to find her information on climate change. She is willing to listen
to people like Ray Hadley and Alan Jones but doesn’t like to hear from celebrities such as Cate Blanchett. Angela
does not often discuss climate change with her friends. However, she feels that a few of them follow her thoughts
on the matter.
Along with her tendency towards popular media, Angela wants to hear from people she feels are trustworthy
scientists, such as the experts that are interviewed on popular current affair programs.
Angela responds to marketing communications that show how we can help Australia and her children. For
example, how can she help protect Australian farmers from drought and other extreme weather conditions?
Alternatively, she responds to messages that encourage the protection of her children’s future. Angela also
engages with positive messages that empower her such as: “You can help stop climate change” or “Help make a
better life for everyone in the future”. These sorts of messages make her feel like she is a better person when
she contributes.
In summary, Angela feels that climate change is a major issue that needs to be addressed soon. She is not sure that
Australia will have any real impact, but, it is worth it to try and secure a future for her kids. She does not support
the current government’s policies. Direct action and punishing high polluters is the way to go, not taxing her
average family.
26
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