Adventures part i - chapter 4

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.

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PLACE EXHIBIT 4-1 HERE
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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


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.


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.


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.

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PLACE TABLE 4-1 HERE
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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.


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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.

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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


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.


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.


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.


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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.


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.


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.)

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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).


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


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


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.


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.


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


S
Π ks = U ks / ∑U ks
α α

s =1

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.


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PLACE TABLE 4-A1 HERE
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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


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%


Table 4-3. Profiles of New Gamma Products from Optimization Program (five attributes)

Clinical Cure Rapidity of Recurrence Incidence of Dosage:
Segmentation Strategy Rate Relief Rate Burning/Itching 1 Dose Per
Buyer: A Priori
Product 1 20% above 2 days faster Equal to Delta 17% 10 days
Gamma share 74.9%
Return (Index) 100
Buyer: Post Hoc
Product 2 20% above 2 days faster 15% above 17% 14 days
Gamma share 80.6%
Return (Index) 109
Part Worth: Post Hoc
Product 1 Equal to Delta 2 days faster Equal to Delta 2% 10 days
Gamma share 81.8%
Return (Index) 109
Importances: Post Hoc
Product 2 20% above Equal to Delta Equal to Delta 2% 10 days
Gamma share 79.2%
Return (Index) 103
Product 2 Equal to Delta Equal to Delta Equal to Delta 2% 10 days
Gamma share 83.1%
Return (Index) 111


Table 4-A1. Characteristics of Computer Programs Used in Case Study

SIMOPT • Individual part worth files • For any set of competitive profiles, • Market share/return for each
• Individual’s importance weight the program computes supplier
file share/return for each supplier • Individual supplier selection file
• Demographics (background) file • All shares/returns are automatically • Optimal product description for
• Current market shares for all adjusted to base-case conditions total market or selected
suppliers • Sensitivity analyses can be segment
• Each supplier’s profile performed at the individual • Sensitivity analysis results by
• Value of alpha and demographic attribute level level within attribute
attribute weights • Optimization can be carried out by
• Control parameters for supplier or for groups of suppliers;
organization attribute levels can be fixed for
• Attribute-level cost/return data conditional optimization
• Analyses can be conducted at the
total market or selected target
segment level
SEGUE • Individual part worths file • For any target segment • Attribute importance, level
• Individual’s importance weight composable from the background desirabilities, and ideal levels,
file variables (with weights supplied by by selected segment
• Demographics (background) file the user), the program computes • Profile utilities by selected
• Segment attribute weights size of segment, ideal levels, segment
attribute importances, and attribute • Respondent weights file
desirability levels summarizing each individual’s
• Both additive and conjunctive relevance to the target segment
segments can be created (input to SIMOPT)
• 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


Exhibit 4-1. Market Segmentation in the Context of Conjoint Analysis

Initial Researcher Focus

Buyer background characteristics Product attribute
(including use occasions) part worths

Segmentation Approach

A priori Post hoc A priori Post hoc Stepwise
segmentation

User selects target User clusters buyers User selects target User clusters part
segment background on set of background part worths for buyer worths of attribute
characteristics characteristics segmentation importances

Optimal Product Design
Optimal Product Design Model Finds Model Finds Best K
Best Product for Each of the Segments Products Sequentially

Total Contribution to Overhead/Profits is Computed

Background Profile is Found for Selectors
of Each Competitive Product


Exhibit 4-2. Average Part Worth Values from Conjoint Model (see Table 4-1).

Scale
Values
0.6
Unreadable
0.5 •
0.4
0.3 Unreadable
Unreadable •
0.2 • Unreadable
• Unreadable
.01 • • • • • • • •
• •
• • • • •
Cure Rapidity Recurrence Incidence of Duration of
Rate of Relief Rate Side Effects Side Effects
Scale
Values
0.6 Unreadable
•
0.5
0.4
0.3
Unreadable
0.2 • Unreadable
.01 •
• • •
• • • • •
Severity of Rapidity Recurrence
Side Effects of Relief Rate


Exhibit 4-3. Profile Charts of Background Attributes by Segmentation Types

Segmentation Approach Percent Group Percent Internal Percent Efficacy/
Practice Medicine Seeker/Switcher

Buyer Post Hoc

Part Worths: Post Hoc

Importances: Post Hoc


0 100 0 100 0 100

Product 1
Lengths of bars refer, respectively to percent of segment classified as
group practice, internal medicine specialty, and psychographic segment:
Product 2 efficacy/seeker/switcher

Adventures part i - chapter 4

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