adventures part i - chapter 4
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CHAPTER 4. MARKET SEGMENTATION
Along with product positioning, market segmentation is one of the most talked about and
acted upon concepts in marketing. Simply put, the basic ideas are:
• Market segmentation presupposes heterogeneity in buyers’ preferences (and
ultimately choices) for products/services.
• Preference heterogeneity for products/services can be related to either person
variables (e.g.. demographic characteristics, psychographic characteristics,
product usage, current brand loyalties, etc.) or situational variables (e.g., type of
meal in which beverage is consumed, buying for oneself versus a gift for someone
else, etc.), and their interactions.
• Companies can react to (or possibly initiate) preference heterogeneity by
modifications of their current product/service attributes including price,
distribution, and advertising/promotion.
• Companies are motivated to do so if the net payoff from modifying their offerings
exceeds what the payoff would be without such modification.
• A firm’s modification of its product/marketing mix includes product line addition/
deletion decisions as well the repositioning of current offerings.
Market segmentation and product positioning are inextricably related, as buyers and
sellers seek mutual accommodation in product/service offerings that best satisfy preference and
profit objectives. This process takes place in a competitive milieu of other brands/suppliers in the
same product category or even other categories of goods competing for the buyer's budget.
ADVENTURES IN CONJOINT ANALYSIS 2
In a priori segmentation, the number of segments, their relative size, and their
description are known in advance. In post hoc segmentation, these three characteristics are found
after the fact. In terms of researcher activity, the newer methodology of cluster-based
segmentation appears to have received considerable user attention in the past decade.
The Role of Conjoint Analysis
As we illustrate subsequently, conjoint analysis is well suited for the implementation of
selected types of market segmentation. First, the focus of conjoint analysis is squarely on the
measurement of buyer preferences for product attribute levels (including price) and the buyer
benefits that may flow from the product attributes. Second, conjoint analysis is a micro-based
measurement technique. Part worth functions (i.e., preferences for attribute levels) are measured
at the individual level. Hence, if preference heterogeneity is present, the researcher can find it.
Third, conjoint studies typically entail the collection of respondent background information (e.g.,
demographic data, psychographic data). One should bear in mind, however, that buyer
background variables, particularly demographic ones, do not necessarily correlate well with
attribute preferences. Increasingly, background data information is collected on respondents’
perceived importance of purchase/use occasions. Fourth, even rudimentary conjoint studies
usually include a buyer choice simulation stage in which the researcher can enter new or
modified product profiles and find out who chooses them versus those of competitors.
Two recent trends in conjoint analysis have served to make the method even more
applicable to market segmentation. First, user friendly and relatively inexpensive PC software
packages for conducting conjoint studies appeared during the mid-1980s. The second trend is the
development and application of optimal product and product line positioning models. Optimal
product design models extend the conjoint analyst’s traditional search for the best profile in a
3 MARKET SEGMENTATION
small set of simulated alternatives. Product design optimizers search for the best profile in what
may be hundreds of thousands (or even millions) of possible attribute-level combinations.
Market Segmentation in the Context of Conjoint Analysis
Exhibit 4-1 is a schematic diagram of the proposed segmentation approach. We first
consider the researcher's initial focus: buyer background characteristics versus product attribute
part worths (as computed from conjoint analysis). All segmentation approaches ultimately
consider both facets. However, in some cases we first target the type of buyer we are looking for
and then design the best product for that type of buyer. In other cases we use the part worths
themselves as a basis for clustering buyers’ attribute-level preferences and then design the best
product for each resulting buyer segment.
_______________________________
PLACE EXHIBIT 4-1 HERE _______________________________
At the next level in Exhibit 4-1, we choose either an a priori or post hoc (cluster-based)
method. If our initial focus is on buyer background characteristics, the user either defines a set of
a priori target segments or clusters the battery of background characteristics to find segments. In
either case, once this step is done, the product design model is used to find the best product for
each segment (defined, illustratively, as the product profile that maximizes contribution to
overhead/profits).
The segmentation procedure is somewhat different when we focus on the part worths. In
the a priori approach, the researcher may segment buyers in terms of their part worths for one (or
more) product attributes. Examples include sensitivity to price, most preferred brand, and
preferences across selected features. In the post hoc approach. it is the part worths (or some
ADVENTURES IN CONJOINT ANALYSIS 4
function of them) that are clustered to obtain buyer segments having preference similarities
across the full set of attributes.
However, the main distinction between the buyer characteristics and part worth
segmentation approaches is in the fifth branch, labeled “stepwise segmentation.” In that
procedure, each buyer is considered a “segment of one.” The product design optimizer is used to
find the best single product, for the firm in question, that maximizes contribution to the firm’s
overhead/profits. This can be done in two basic ways. First, the optimizer cm be used to find the
best replacement for the firm's current product. Alternatively, the optimizer can be used to find
the best product addition. That addition maximizes the sum of contributions across all products
in the firm's line (and, hence, cannibalization as well as competitive draw is taken into account).
In a stepwise way, other products can be added, each based on the preceding criterion.
Unlike the other segmentation branches, stepwise segmentation does not design optimal products
to rnatch specific segments (a priori or post hoc, as the case may be). However, in either the
targeted or stepwise approach, multiple products can he designed; in the former approach a
specific new product is simply designed for each target segment.
As noted from Exhibit 4-1, the stepwise selection procedure ultimately induces a buyer
segmentation in the sense that a final pass in the model identifies the background characteristics
of the buyers who choose each product in the array (including competitive products).
All five branches in Exhibit 4-1 eventually produce two sets of outputs:
• Product profiles with associated returns to the firm under study.
• A size and background description or each buyer segment choosing one of the
product profiles (or perhaps a competitive product) from the resulting array of
choices.
5 MARKET SEGMENTATION
Which approach is “best” becomes a managerial question, once more subjective criteria
such as reachability, substantiality, and actionability, are introduced.
Additional Considerations
Three additional considerations underlie the schematic framework work of Exhibit 4-1.
First, we assume that once a product profile has been designed optimally for a pre-specified
buyer segment, it becomes available as a potential choice option for all buyers. We do not “wall
off” buyers by constraining the availability of each of the firm’s products to selected subsets of
buyers. The model does not require free buyer access to all options (including competitive
products). It can be adapted to handle the “walled-off” approach. However, in our experience we
have found that most firms consider it more realistic to permit all competitive items in the firm’s
product line to be available to buyers. Hence, the buyer is free to select the option he or she finds
most attractive.
Second, more subjective criteria (segment reachablity, etc.) can be handled in part by
researcher-assigned weights on various background characteristics if the buyer-focus option is
chosen. Weights can be assigned to either buyer characteristics, levels within characteristic, or
both. Often, these researcher-supplied weights will reflect information on advertising audience,
demographic characteristics, and the like. Whatever the source, the weights provide a differential
attraction score for each buyer. That score, in turn, affects the composition of the optimal product
profile.
Third, we emphasize that the principal criterion adopted here is a financial one – finding
a set of products whose overall contribution to the firm's overhead/profits is optimized. As can
be surmised, the approach of Exhibit 4-1 places less emphasis on statistical criteria (e.g.,
goodness-of-fit measures in cluster analysis) and greater emphasis on financial return to the firm.
ADVENTURES IN CONJOINT ANALYSIS 6
Illustrative Application
An empirical example should help clarify the proposed approach. Our application
involves a pharmaceutical firm (herein called Gamma) that produces an antifungal medication
for the treatment of various female disorders. (The product class and attribute descriptions are
disguised.)
Gamma currently has a modest share (14%) of the market. Alpha and Beta, two lower-
priced but less efficacious brands, have shares of 6% and 10%, respectively. The “Rolls Royce”
of the marketplace is Delta, whose share is 70%. Because of Delta’s dominant position in the
marketplace, other competitors tend to compare their entries with Delta’s brand as a reference
product.
Table 4-1 illustrates this point. Clinical cure rate, rapidity of symptom relief, and
recurrence rate are each expressed in terms of departures from Delta as a reference point.
(Physicians also use Delta as a basis for comparing competitive brands.) As shown in Table 4-1,
the antifungal therapeutic class is described in terms of eight attributes related to efficacy, side
effects, dosage regimen, and patient cost over the course of therapy.
_______________________________
PLACE TABLE 4-1 HERE _______________________________
Table 4-2 shows the current brand profiles of each of the four competitors, as well as their
current market shares. Gamma and Delta are priced the same. Gamma is superior to Delta in
terms of clinical cure rate, rapidity of symptom relief, and recurrence rate, whereas Delta is
better than Gamma in terms of side effects and dosage regimen.
7 MARKET SEGMENTATION
_______________________________
PLACE TABLE 4-2 HERE _______________________________
Market Survey
Gamma’s managers felt that their current pharmacological research efforts could produce
product improvements in attributes on which it was currently deficient in relation to Delta. Some
of those improvements would necessitate higher production costs, however. Managers decided to
commission a conjoint-based research study to determine what the demand effects of various
product improvements might be.
A sample of 320 physicians were contacted by a nationally-known marketing research
firm. Conjoint data were collected at the individual-respondent level by personal interviews.
Respondents received an honorarium for their participation. In addition to the conjoint exercise,
physician background data (including psychographic data) were obtained.
As background, Exhibit 4-2 shows average part worths for the total sample obtained from
the conjoint exercise. To reduce clutter, only the “best” level is labeled; Table 4-1 gives
descriptions of all levels. We note from Exhibit 4-2 that cure rate and cost of therapy are highly
important attributes on average.
_______________________________
PLACE EXHIBIT 4-2 HERE _______________________________
Gamma’s managers were able to estimate variable costs at the individual-attribute level.
Their estimates were crude, but generally followed a pattern that one would expect – more
attractive levels (on efficacy, side effects, etc.) would entail higher production and quality
control costs. With cost estimates at the attribute level (and price data), we can compute a
ADVENTURES IN CONJOINT ANALYSIS 8
contribution to overhead and profit for each profile combination that is composable from the
eight attributes.
For illustrative purposes, we assume that Gamma’s managers want to retain the firm’s
current brand profile but are interested in extending its line with the addition of two new
products. The new products could cannibalize the firm’s current brand, but might also draw share
from competitive products. As illustrative options. we consider five ways of selecting two new
product additions for Gamma.
1. Buyer-focused a priori segment selection
2. Buyer-focused post hoc segment selection 3. Part worth-focused post hoc segment selection
4. Importance-weight-focused post hoc segment selection
5. Stepwise segmentation.
Buyer-Focused Segmentation
Three demographic/psychographic characteristics were available for segmentation.
1. Physician practice (solo vs. group-based practice)
2. Physician specialty (gynecology, internal medicine, general practice)
3. Psychographic profile (six different segments obtained from a previous cluster
analysis of 24 psychographic variables).
For illustration, we chose the first background variable – type of physician practice. The sample
breakdown was 48% solo versus 52% group practice. We then found the best product for each
separate segment, conditional on Gamma’s current product remaining in the line. This analysis
illustrates the a priori approach.
9 MARKET SEGMENTATION
To implement the post hoc (or cluster-based) approach, we used a two-step procedure.
First, multiple correspondence analysis was applied to the characteristics (type of physician
practice, specialty, and psychographic segment) to obtain a coordinate representation of the
physician respondents in a common space. The respondents then were clustered by a k-means
program. Four different starting configurations were used and split-half replications of the
clustering were done to obtain the most highly replicable two-cluster solution.
The product-optimizing program was again used to find the best product for each of the
two clusters, conditional on Gamma’s current product remaining in the line. As a final step for
both the a priori and post hoc procedures, all six products (four original and two additional) were
entered into the optimal product design program. Returns were computed for each Gamma
product and identification numbers were recorded for all respondents choosing each product,
including competitors’ brands.
Part Worth-Focused Segmentation
We applied two different cluster-based approaches (using the same split-half method just
described) to these data. First, we clustered respondents according to the part worths themselves,
after centering the data around each respondent’s mean. Second, we clustered attribute
importances, as obtained from the conjoint model, by the same procedure. These two
approaches, in general, produce different clusterings (which was the case here). In each case, two
clusters were found.
Next. the same procedure was used to find two new product additions. These products
were entered and returns were computed for each of Gamma’s first-choice products, as well as
competitors’ brands.
ADVENTURES IN CONJOINT ANALYSIS 10
Stepwise Segmentation
The last approach involved stepwise segmentation. First, the optimal design model was
applied to the total sample to find the highest return product for Gamma, conditional on its
current product remaining in the line. The new product was added to the array. The model was
used again to find a second optimal product for Gamma, conditional on the first two products
remaining in the line. A similar procedure was used to find Gamma/s shares and returns and
respondents’ selections for the six products in the total competitive array.
In sum, five different approaches were used to select two new products for Gamma. All
new products were selected so as to maximize return to Gamma’s whole product line (i.e., the
potential for cannibalization was taken into consideration).
Results of Analysis
We first discuss the findings on market shares and returns received by Gamma under each
segmentation and product design strategy. We then consider the segments themselves in terms of
respondent background characteristics.
Market Shares and Returns
By design, each of the five segmentation strategies produces two new product profiles for
Gamma. The first finding of interest is that for three of the product attributes (duration of side
effects, severity of side effects, and cost per completed therapy), the results are the same: one
day, mild, and $65.20, respectively (see Table 4-3). That is, virtually all respondents wanted the
same side-effect profiles in terms of duration and severity. Not surprisingly, the high cost
($65.20) was not desired by most respondents. However, because of the costs necessary to
achieve highly desired efficacy and side-effect profiles, the highest price turned out to be optimal
from Gamma’s standpoint.
11 MARKET SEGMENTATION
_______________________________
PLACE TABLE 4-3 HERE _______________________________
Table 4-3 gives comparative results for the segmentation strategies, based on the five
varied attributes. Also shown are cumulative market shares for Gamma’s three products
(including its status quo product) and return to the company expressed as an index value with a
base of 100.
The first point to note from Table 4-3 is the result for the first strategy, whereby buyers
are segmented according to their type of practice (solo vs. group). The new product profiles are
identical between the two segments. Not surprisingly, this strategy gives Gamma the lowest
share and return of all five strategies (because the second product is redundant with the first).
Clearly, type of physician practice is not a useful segmenting attribute in terms of new
product design for our dataset. What happens is that buyers in the two segments are reasonably
homogeneous when it comes to the best product for Gamma to market. Of course, they could
differ in product preferences that would entail less attractive products for Gamma, but evidently
do not differ in terms of its best product strategy. This result illustrates the value in coupling
product design with segmentation strategy. Not surprisingly. buyer similarity in preference
depends on which products are being offered.
The other four strategies provide differentiation between products 1 and 2. For example.
in the case of buyer-focused post hoc segmentation, the two products differ in four of the five
attributes shown in Table 4-3. However, in this case the best segmentation is provided by the
stepwise approach, with a return index of 111.
ADVENTURES IN CONJOINT ANALYSIS 12
Still, the buyer-focused post hoc strategies each show a return index of 109, with
cumulative market shares that are only slightly lower than that associated with the stepwise
segmentation approach.
All of the preceding results are tempered by (at least) the following assumptions.
1. Gamma can produce the appropriate attribute levels at the costs used in the model.
2. Competitors do not retaliate by changing their profiles and/or adding new
products.
3. The list of attributes and levels is reasonably exhaustive of the important attributes
in the therapeutic class.
4. The sample is representative of the relevant population and parameter estimation
error is relatively small.
5. Firms are at a rough parity in advertising, promotion, and distribution.
6. Physicians’ preferences for product attributes remain reasonably stable over the
firm's planning horizon.
7. The share and return estimates are based on “steady-state” attainment (i.e., the
time path by which these are reached is not considered).
8. Segments are reachable, actionable. and substantial.
We examine the last assumption in more detail by summarizing physician profiles of brand
selectors.
Background Profiles
At the user's request, the optimal design model records who chooses which brand/service
in the array. These files can be cross-tabulated with other variables, in this case the three
background characteristics – type of practice, physician specialty. and psychographic segments.
13 MARKET SEGMENTATION
In each segmentation approach, we found that the respondents who selected Gamma’s new
products 1 or 2 had similar background attribute levels. In particular. the modal attribute levels
were (1) group practice, (2) internal medicine specialty, and (3) a psychographic segment
identified as “primary interest in drug efficacy, information seeker, and proneness to brand
switch.”
Exhibit 4-3 shows the profiles for four of the segmentation approaches. In the buyer-
focused a priori approach, the two new products turned out to be the same. (Their modal
background profiles were also the same as those found in the other four segmentation
approaches.)
_______________________________
PLACE EXHIBIT 4-3 HERE _______________________________
From Exhibit 4-3 we see that the profiles are fairly similar across new products 1 and 2
and across segmentation approaches. The stepwise segmentation approach seems to produce the
most dissimilar background profiles for products 1 and 2, particularly in the percentages
classified as group practice and internal medicine specialty. However. the differences are not
extreme.
Though the finding is not shown in Exhibit 4-3, respondents who chose Alpha, Beta, and
Delta were drawn primarily from the solo practice group in all five of the segmentation
approaches. Modal profiles for specialty and psychographic characteristics do not differ from
those found for Gamma. Other datasets, of course, may not show such high agreement across
background attribute classification. In the illustrative case, Gamma might do well to emphasize
product attribute levels that distinguish its new products from competitors’ products (and let
buyer self-selection take over).
ADVENTURES IN CONJOINT ANALYSIS 14
Recapitulation
The case example shows how different segmentation approaches can lead to different
product positionings. In our example. stepwise segmentation produces the highest return for
Gamma (as measured across all three of its products). We also note that the buyer-focused a
priori approach fails to discriminate between solo and group practice physicians in terms of best
new products.
In the other four approaches, attribute-level differences are noted across products, even
though the returns are fairly close. The three post hoc clusterings produced clusters of
approximately the same size. The clustering of the part worths produced somewhat different
results than clustering only on the importance component of the part-worths. In our example, the
part worth-based clustering produced a somewhat higher product line return for Gamma.
Though stepwise segmentation should, in principle, do very well in terms of market share
(because its product selection potential is less restricted), the researcher should also consider
reachability and other aspects of its segmentation. This more general objective accounts for the
last step in the segmentation strategy shown in Exhibit 4-1.
Caveats and Limitations
The advent of conjoint-based product line optimizers has led to a new tool for selected
types of market segmentation. As Exhibit 4-1 shows, segmentation and product positioning are
interrelated. The emphasis of thus dual approach is on constructing and using an operational
measure of segmentation that addresses share/return. For example, post hoc clustering is
evaluated less by statistical discrimination tests of the clustering results than by how well the
15 MARKET SEGMENTATION
associated new product positioning strategy is forecasted to perform in terms of corporate
financial return.
We believe the suggested approach can be helpful in real world applications (and has
already received limited application), but several caveats and limitations must be mentioned as
areas for future research.
Measurement and Parameter Estimation Issues
Parameter estimation in conjoint analysis is subject to error. Also, the model might be
incomplete – important product attributes and/or important buyer characteristics could be
omitted. To some extent, focus groups and survey pretests can be used to reduce model
specification errors, and those preliminary steps are undertaken routinely by experienced
conjoint analysts.
Cost estimation is also a difficult undertaking. The firm’s cost accounting group is
assumed to be able to estimate independent, direct, variable costs at the individual-attribute level.
If future investment outlays are also required, they must be estimated and assignable to
individual products. As would be surmised, the proposed approach appears to be most applicable
to cases involving recombinations of current attribute levels as opposed to radically new
products. Concomitantly. we assume that the firm’s engineers can produce the desired level of
each attribute as dictated by the model.
Part-Worth and Cost Stability Over Time
Conjoint analysis is essentially a static, steady-state preference measurement technique
(though some conjoint applications have involved parameter estimation over a series of time
periods). The market share and return changes noted in our example obviously would not be
ADVENTURES IN CONJOINT ANALYSIS 16
expected to occur instantaneously. Rather, time trends would have to be introduced to make the
model more realistic as a forecasting technique.
Some research is underway to make conjoint analysis more “dynamic.” Procedures entail
a variety of techniques, ranging from having respondents estimate the anticipated share of their
business that a product profile would obtain over the next (say) two years to analyses of time
paths and diffusion patterns of previous new brand introductions in the same product category.
Competitive Retaliation
For ease of presentation, our example does not include competitive retaliation. However,
the model is capable of including action/reaction sequences. Consider the following examples.
1. Delta, having observed Gamma’s new product introductions, could in turn
optimize its product. assuming status quo attribute-level conditions for all
competing products. This action could be followed by the actions of Alpha. Beta,
and so on.
2. Delta, in conjunction with Alpha, could offer a joint new product, designed to
provide the highest net contribution to their current products.
Other retaliatory actions are also possible. However, the measurement problems associated with
those product extensions are considerable. If Gamma wants to forecast Delta’s response, it must
be able to estimate Delta’s attribute-level costs and must assume that Delta’s information about
buyers’ part worths is the same as Gamma’s. Moreover, our model does not provide help on
when competitive reactions might take place.
Models based on game theory ideas have been proposed recently, but their application to
real world problems is still in its infancy.
17 MARKET SEGMENTATION
Incomplete Optimization
The proposed approach has been designed for conjoint data and, hence, applies primarily
to product/service attributes and price. A more comprehensive model would incorporate
advertising expenditure levels, message content, media mix, sales promotional expenditures, and
distribution outlays. In principle, such additions could be made, but the measurement problems
are formidable. For the short run at least, applications of the proposed approach will continue to
treat those elements of the marketing mix outside the conjoint model.
Predictive Validity
Above all, the manager wants to know how well the model predicts. Our applications of
the proposed model have emphasized pharmaceuticals, high tech products (such as computers
and telecommunications), and consumer financial services such as credit cards. We have found
that managers view the model primarily as a planning and sensitivity analysis tool for exploring
alternative product and pricing strategies.
In sum, research on conjoint-based segmentation/positioning is still in its early stages.
Though the approach shows promise for the development of buyer- and part-worth-focused
segmentation strategies, much additional research is needed before its potential is realized.
Appendix 4-A
Throughout our discussion of the case example, we employ an optimal product design
model called SIMOPT (SIMulation and OPTimization model). The SIMOPT model (and
computer program) is designed to provide a systematic search for product profiles that maximize
either share or return for a user-specified brand/supplier.
ADVENTURES IN CONJOINT ANALYSIS 18
In the case example, the total number of possible attribute-level combinations is 46 - 32 =
36,864. This problem is a relatively small one for SIMOPT; in this case the program evaluated
all profiles in a few seconds.
For larger problems (e.g.. in which the number of combinations exceeds 1 million),
SIMOPT employs a divide-and-conquer algorithm that iteratively optimizes subsets of attributes
until the program converges. This heuristic works very well in practice. In many cases however,
complete enumeration (as used here) is practical.
SIMOPT Features
SIMOPT is designed to work with large-scale problems entailing up to 1500 respondents
and as many as 40 attributes, with up to 10 levels per attribute, and up to 20 competitive
suppliers. Its features include:
1. Market share and/or profit-return optimization.
2. Total market and/or individual segment forecasts.
3. Sensitivity analysis.as well as optimal profile seeking.
4. Cannibalization issues related to product complementarity and line extension
strategies.
5. Calibration of results to current market conditions.
6. Constrained optimization, through fixing of selected attribute levels for any or all
suppliers.
7. A decision parameter (alpha) that can be used to mimic any of the principal
conjoint choice rules (mar utility, logit, BTL). The alpha rule assumes that the
probability of buyer k selecting brand s is given by
19 MARKET SEGMENTATION
∑=
=ΠS
sksksks UU
1/ αα
where Uks is the utility of buyer k for brand s, α is an exponent (typically greater
than 1.0) chosen by the user, and S is the number suppliers.
8. Sequential competitive moves, such as line extensions or competitor
actions/reactions.
9. Capability for designing an optimal product against a specific competitive
supplier.
10. Provision for accepting part worth input that contains two-way interaction effects,
in addition to the more typical main effects.
11. Preparation of output files containing ID numbers of buyers selecting each
competitive option.
12. Computation of the “Pareto frontier”; the frontier consists of all product profiles
that are not dominated by other profiles in terms of both market share and return.
The SEGUE Model
In addition to SIMOPT, a complementary model (and program) called SEGUE has been
designed. SEGUE has two principal functions. First, it provides the user with descriptive
summaries of part worths and attribute importances for user-composed target segments. Second,
it prepares a respondent weights file that summarizes each buyer’s “relative value” in meeting
segment desiderata. This buyer weights file is input to SIMOPT to obtain optimal products (etc.)
for user-composed target segments.
Table 4-4 summarizes the input/output aspects of each program, as well as several of the
operations that each program performs.
ADVENTURES IN CONJOINT ANALYSIS 20
_______________________________
PLACE TABLE 4-A1 HERE _______________________________
21 MARKET SEGMENTATION
Table 4-1. Attribute Levels Used in Conjoint Survey Clinical cure rate in comparison with Delta 10% below Equal to Delta 10% above 20% above
Rapidity of symptom relief in comparison with Delta 1 day slower Equal to Delta 1 day faster 2 days faster
Recurrence rate in comparison with Delta 15% above Equal to Delta 15% below 30% below
Incidence of burning/itching side effects 17% 10% 5% 2%
Duration of side effects 3 days 2 days 1 day
Severity of burning/itching side effects Severe Moderate Mild
Dosage regimen: 1 dose per day for 14 days 10 days 5 days 2 days
Drug cost per completed therapy $65.20 $58.85 $44.60 $32.40
ADVENTURES IN CONJOINT ANALYSIS 22
Table 4-2. Current Drug Profiles of Four Competitors
Attribute Alpha Beta Gamma Delta Clinical cure rate in comparison with Delta 10% below 10% above 10% above Equal
Rapidity of symptom relief in comparison with Delta 1 day slower 1 day faster 1 day faster Equal
Recurrence rate in comparison with Delta 15% above Equal 15% below Equal
Incidence of burning/itching side effects 17% 10% 5% 2%
Duration of side effects 2 days 3 days 2 day 1 day
Severity of burning/itching side effects Severe Moderate Moderate Mild
Dosage regimen: 1 dose per day for 14 days 10 days 5 days 2 days
Drug cost per completed therapy $44.60 $44.60 $58.85 $58.85
Current Market Share 6% 10% 14% 70%
23 MARKET SEGMENTATION
Table 4-3. Profiles of New Gamma Products from Optimization Program (five attributes)
Segmentation Strategy Clinical Cure
Rate Rapidity of
Relief Recurrence
Rate Incidence of
Burning/Itching Dosage:
1 Dose Per Buyer: A Priori Product 1 Product 2 Gamma share Return (Index)
20% above 20% above
2 days faster 2 days faster
Equal to Delta Equal to Delta
74.9% 100
17% 17%
10 days 10 days
Buyer: Post Hoc Product 1 Product 2 Gamma share Return (Index)
10% above 20% above
2 days faster 2 days faster
Equal to Delta
15% above 80.6%
109
2% 17%
10 days 14 days
Part Worth: Post Hoc Product 1 Product 2 Gamma share Return (Index)
Equal to Delta
20% above
2 days faster 2 days faster
Equal to Delta Equal to Delta
81.8% 109
2% 17%
10 days 10 days
Importances: Post Hoc Product 1 Product 2 Gamma share Return (Index)
20% above 20% above
2 days faster Equal to Delta
Equal to Delta Equal to Delta
79.2% 103
17% 2%
10 days 10 days
Stepwise Segmentation Product 1 Product 2 Gamma share Return (Index)
20% above
Equal to Delta
2 days faster Equal to Delta
Equal to Delta Equal to Delta
83.1% 111
17% 2%
10 days 10 days
ADVENTURES IN CONJOINT ANALYSIS 24
Table 4-A1. Characteristics of Computer Programs Used in Case Study SIMOPT • Individual part worth files
• Individual’s importance weight file
• Demographics (background) file • Current market shares for all
suppliers • Each supplier’s profile • Value of alpha and demographic
attribute weights • Control parameters for
organization • Attribute-level cost/return data
• For any set of competitive profiles, the program computes share/return for each supplier
• All shares/returns are automatically adjusted to base-case conditions
• Sensitivity analyses can be performed at the individual attribute level
• Optimization can be carried out by supplier or for groups of suppliers; attribute levels can be fixed for conditional optimization
• Analyses can be conducted at the total market or selected target segment level
• Market share/return for each supplier
• Individual supplier selection file • Optimal product description for
total market or selected segment
• Sensitivity analysis results by level within attribute
SEGUE • Individual part worths file • Individual’s importance weight
file • Demographics (background) file • Segment attribute weights
• For any target segment composable from the background variables (with weights supplied by the user), the program computes size of segment, ideal levels, attribute importances, and attribute desirability levels
• Both additive and conjunctive segments can be created
• The user can also input any trial product profile and find its total utility compared to the best profile
• A respondent weights file is prepared for later use in SIMOPT
• Attribute importance, level desirabilities, and ideal levels, by selected segment
• Profile utilities by selected segment
• Respondent weights file summarizing each individual’s relevance to the target segment (input to SIMOPT)
25 MARKET SEGMENTATION
Exhibit 4-1. Market Segmentation in the Context of Conjoint Analysis
Initial Researcher Focus
Segmentation Approach
Buyer background characteristics(including use occasions)
Product attributepart worths
A priori
User selects target segment background
characteristics
Optimal Product Design Model Finds Best Product for Each of the Segments
Total Contribution to Overhead/Profits is Computed
Background Profile is Found for Selectors of Each Competitive Product
Post hoc A priori Post hoc Stepwisesegmentation
User clusters buyers on set of background
characteristics
User selects target part worths for buyer
segmentation
User clusters part worths of attribute
importances
Optimal Product Design Model Finds Best K
Products Sequentially
ADVENTURES IN CONJOINT ANALYSIS 26
Exhibit 4-2. Average Part Worth Values from Conjoint Model (see Table 4-1).
CureRate
0.6
0.5
0.4
0.3
0.2
.01
Rapidityof Relief
•Recurrence
RateIncidence ofSide Effects
Duration ofSide Effects
Scale Values
Unreadable
Unreadable
• •
•
•• •
•
•• •
•
•• •
•
• ••
Severity ofSide Effects
0.6
0.5
0.4
0.3
0.2
.01
Rapidityof Relief
•Recurrence
Rate
Scale Values
•
•
•• •
•• • •
•
Unreadable
UnreadableUnreadable
UnreadableUnreadable
Unreadable
27 MARKET SEGMENTATION
Exhibit 4-3. Profile Charts of Background Attributes by Segmentation Types
Stepwise Segmentation
Importances: Post Hoc
Part Worths: Post Hoc
Buyer Post Hoc
Percent Efficacy/ Seeker/Switcher
Percent Internal Medicine
Percent Group Practice
Segmentation Approach
0 100 0 100 0 100
Product 1
Product 2
Lengths of bars refer, respectively to percent of segment classified as group practice, internal medicine specialty, and psychographic segment: efficacy/seeker/switcher