Segmenting Markets with Conjoint Analysis

Segmenting Markets with Conjoint AnalysisAuthor(s): Paul E. Green and Abba M. KriegerSource: Journal of Marketing, Vol. 55, No. 4 (Oct., 1991), pp. 20-31Published by: American Marketing AssociationStable URL: http://www.jstor.org/stable/1251954 .

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Paul E. Green & Abba M. Krieger

Segmenting Markets With Conjoint Analysis

Conjoint analysis is a useful measurement method for implementing market segmentation and product positioning. The authors describe how recently developed optimal product design models provide a way to test the effectiveness of a selected class of market targeting strategies. They first propose a conceptual framework for describing segmentation in the context of conjoint analysis input data. Then they apply that framework to an illustrative case study entailing physicians' preferences for a newly developed prescription drug. They conclude with a discussion of the limitations of the proposed method.

ALONG with product positioning, market segmentation is one of the most talked about and

acted upon concepts in marketing. Much has hap- pened since Smith's (1956) initial article on market segmentation. Wind (1978) provided a comprehensive account of research progress in segmentation. Since his review, other authors (e.g., Dickson 1982; Dick- son and Ginter 1987; Elrod and Winer 1982; Malhotra 1989; Rudelius, Walton, and Cross 1987; Srivastava, Alpert, and Shocker 1984) have made important contributions to the burgeoning segmentation literature.

So much has been written on market segmentation that it is easy to lose sight of its essentials. 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 vari-

Paul E. Green is the S. S. Kresge Professor of Marketing and Abba M. Krieger is Professor of Statistics, The Wharton School, The University of Pennsylvania. The authors express their appreciation to the Editor and three anonymous JM reviewers for their helpful comments on a draft of the article.

ables (e.g., type of meal in which beverage is con- sumed, buying for oneself versus a gift for someone else, etc.) and their interactions. See Green (1977), Green and DeSarbo (1979), and Dickson (1982).

* Companies can react to (or possibly produce) 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 in- cludes product line addition/deletion decisions as well as 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.

Normatively Based Segmentation Academic researchers (Claycamp and Massy 1968; Frank, Massy, and Wind 1972) have long considered ways to aggregate buyers optimally into groups. They have proposed elasticity coefficients, marginal reve- nues, and response function coefficients as appropri-

Journal of Marketing Vol. 55 (October 1991), 20-31 20 / Journal of Marketing, October 1991



ate data for examining buyer similarities (Tollefson and Lessig 1978); their approach is referred to as normative segmentation.

Normative segmentation methodology has been criticized as unrealistic by several researchers. Criti- cisms include insufficient allowances for managerial and institutional constraints (Mahajan and Jain 1978), restriction to a single marketing control variable (Tol- lefson and Lessig 1978), and failure to consider the competitive environment (Young, Ott, and Feigin 1978). It seems fair to say that "classical" normative segmentation has received relatively little application by industry researchers.

Industry-Based Segmentation Wind (1978) identifies two principal approaches to applied market segmentation (also, see Green 1977).

* In a priori segmentation, the researcher first chooses some variable(s) of interest (e.g., buyer's age, gender, principal benefit sought, current brand) and then classifies buyers according to that designation.

* In post hoc or cluster-based segmentation, the researcher chooses a battery of interrelated variables (e.g., psychographic characteristics, preferences for various user benefits associated with the product category). Person-by- variable "scores" then are clustered into person groups whose average within-group similarity is high and whose between-group similarity is low.

In a priori segmentation, the number of segments, their relative size, and their description are known in advance. In post hoc segmentation, those 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 (e.g., Guerlain 1989; Stannard 1989). In keeping with managers' interest in unambiguous descriptions of the segments, most commercial market segmentation studies employ nonhierarchical partitioning techniques for clustering buyers; each individual appears in one and only one segment (Mitchell 1983; Tomasino 1984).1

Whichever of the preceding approaches is used (and sometimes they are employed in tandem), the researcher ultimately must relate any market partitioning to the firm's product/marketing mix. Often this is done in two stages. First, the researcher ascertains how the segments differ in terms of product attribute preferences (or other aspects of the offerings that relate to buyer choice). Second, the researcher considers the implications of preference heterogeneity for (1) changing the firm's current offerings, (2) reaching selected segments, and (3) evaluating whether the con- templated changes are profitable.

'A second reason for using nonhierarchical clustering (e.g., k-means) programs is that they can accommodate the large sample sizes associated with market segmentation studies.

These analyses are not easy, because the redesign of product/service offerings and/or the advertising messages associated with those offerings usually must be done in the context of competitive suppliers. Com- petitors may be armed with similar market information. Also, as in all marketing research projects, the marketplace under study is subject to both spatial and temporal variability.

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 (Green and Srinivasan 1978, 1990) 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 (Moore 1980). Increas- ingly, background data include information collected on respondents' perceived importance of purchase/use occasions.2

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. Wind (1978) calls this approach flexible segmentation.

Two recent trends in conjoint analysis have served to make the method even more applicable to market segmentation. First, user friendly and relatively in- expensive PC software packages for conducting conjoint studies appeared during the mid-1980s. The principal conjoint package developers are Sawtooth Software (Johnson 1987) and Bretton-Clark (Herman 1988). Interestingly, the authors of both packages stress the importance of (1) including respondent background variables in the choice simulator and (2) clustering respondent part-worths (or derived attribute importances) as a type of post hoc segmentation, followed by a cross-classification of the resulting segments with respondent background variables.

The second trend is the development and application of optimal product and product line positioning

2Componential segmentation is sometimes used to find optimal segments for products and vice versa (Green, Krieger, and Zelnio 1989).

Segmenting Markets With Conjoint Analysis / 21



models. Reviews of those advances are provided by Kohli and Krishnamurti (1987), Sudharshan, May, and Gruca (1988), and Green and Krieger (1989). Optimal product design models extend the conjoint analyst's traditional search for the best profile in a small set of simulated alternatives. Product design optimizers search for the best profile in what may be hundreds of thou- sands (or even millions) of possible attribute-level combinations.

Objectives Our objectives are twofold. First, we describe a conceptual framework for market segmentation, as viewed in the context of conjoint analysis and optimal product design models. We illustrate how the major approaches to market segmentation fit into the framework and how conjoint data provide an operational capability for implementing selected types of market segmentation and positioning strategy.

Second, we apply the ideas described in our con- ceptually oriented discussion to an actual (disguised) commercial database. The data pertain to the development of a new product strategy for an antifungal prescription drug. A variety of segmentation strategies are illustrated, all of which flow from our conceptual framework.

Some caveats should be kept in mind throughout our subsequent discussion. First, the proposed approach does not cover strategy formulation across all aspects of the marketing mix. Conjoint studies typically focus on product/service attributes, brand name, and price. Advertising, sales promotion, and distribution efforts usually are considered outside the conjoint model.

Second, in the case study we describe, several difficult measurement and parameter estimation issues arise that limit the applicability of the approach. Fi- nally, conjoint-based product design is basically a static technique that considers buyer preferences at a particular point in time. We comment on these (and related) limitations in the concluding section.

Market Segmentation in the Context of Conjoint Analysis

Figure 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

FIGURE 1 Market Segmentation in the Context

of Conjoint Analysis

INITIAL RESEARCHER FOCUS

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

SEGMENTATON APPROACH

User

segme ch

A poriri Post-hoc A priori Post hoc Stepwise

Segmentation

rselects target User clusters buyers User selects target User clusters ent background on set of background part-worths for part-worths or aracterstlcs characteristics buyer segmentation attribute importances

OPTIMAL PRODUCT DESIGN OPTIMAL PRODUCT DESIGN MODEL FINDS BEST MODEL FINDS BEST K

PRODUCT FOR EACH CF THE K SEGMENTS PRODUCTS SEQUENTIALLY

TOTAL CONTRIBUTION TO OVERHEAD/PROFITS IS COMPUTED

BACKGROUND PROFILE IS FOUND FOR

SELECTORS OF EACH COMPETITIVE PRODUCT

and then design the best product for each resulting buyer segment.

At the next level in Figure 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 pre- ferred 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.3

However, the main distinction between the buyer characteristics and part-worth segmentation ap-

3Somewhat surprisingly, a priori-based part-worth segmentation appears to have received considerably less attention than the post hoc approach.

22 / Journal of Marketing, October 1991



proaches 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 can be used to find the best re- placement for the firm's current product. Alterna- tively, 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 match specific segments (a priori or post hoc, as the case may be). However, in either the targeted or stepwise approach, multiple products can be designed; in the former approach a specific new product is simply designed for each target segment.

As noted from Figure 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 Figure 1 eventually produce two sets of outputs:

* Product profiles with associated returns to the firm under study.

* A size and background description of 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 related (and more subjective) criteria such as reachability, substantiality, and actionability are introduced.4

Additional Considerations Three additional considerations underlie the schematic framework of Figure 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 up" 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-up" approach. However, in our experience we have found that most firms consider it more realistic to permit all competitive items in the

4Such criteria as reachability are not easy to operationalize; even if operationalized they are often difficult and expensive to measure.

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 reachability, 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 characteristic/level 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 Figure 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 pharma- ceutical firm (herein called Gamma) that produces an antifungal medication for the treatment of various fe- male 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 dom- inant position in the marketplace, other competitors tend to compare their entries with Delta's brand as a reference product.

Table 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 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.

Table 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.

Segmenting Markets With Conjoint Analysis /23



TABLE 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: One 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 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 1 day slower 1 day faster 1 day faster Equal

with Delta

Recurrence rate in comparison with Delta 15% above Equal 15% below Equal Incidence of side effects 17% 10% 5% 2% Duration of side effects 2 days 3 days 2 days 1 day Severity of side effects Severe Moderate Moderate Mild

Dosage regimen 14 days 10 days 5 days 2 days Drug cost $44.60 $44.60 $58.85 $58.85 Current market share 6% 10% 14% 70%

Market Survey Gamma's managers felt that their current pharmaco- logical research efforts could produce product improvements in attributes on which it was currently de- ficient 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 hon- orarium for their participation. In addition to the conjoint exercise, physician background data (including psychographic data) were obtained.

As background, Figure 2 shows average part-worths for the total sample, obtained from the conjoint exercise. To reduce clutter, only the "best" level is labeled; Table 1 gives descriptions of all levels. We note from Figure 2 that cure rate and cost of therapy are highly important attributes on average.

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

FIGURE 2 Average Part-Worth Values

From Conjoint Model (see Table 1)

scal Values

0.6 -

0.5 _

~~~~~~~~~/ ~ ~30% below Delta 0.2 - 2 days last.e hn Delta ,

01 - 2% day

Cue Rapidity Reorence Side Duan o rat ol eliet rate effects enects

Scale Values

0.6 $3240

0.4 -

0.3 -

0.2 - Md I dose/2 days

0.1 - t I

Severiy Dosage Cost ol regmen of

effects therapy




would expect-more attractive levels (on efficacy, side effects, etc.) would entail higher production and qual- ity 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 Gam- ma'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 selec-

tion. 5. Stepwise segmentation.5

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 break- down 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 (Green, Schaffer, and Pat- terson 1988) 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

5We also selected two product additions in the stepwise approach to be consistent with the four other cases.

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 the 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 3). That is, virtually all respondents wanted the same side-effect profiles in terms of duration and severity. 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.

Table 3 gives comparative results for the segmentation strategies, based on the five varied attributes. Also shown are cumulative market shares for Gam- ma's three products (including its status quo product) and return to the company expressed as an index value with a base of 100.6

The first point to note from Table 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 strat-

6Clearly, the high shares for Gamma are unrealistic because no competitive retaliation is assumed. They should be considered relatively rather than absolutely.

egies (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 3. However, in this case the best segmentation strategy is provided by the stepwise approach, with a return index of 111.

Still, the buyer-focused post hoc and the part-worth- focused post hoc strategies each show a return index of 109, with cumulative market shares that are only

TABLE 3 Profiles of New Gamma Products From Optimization Program

(five attributes)

Incidence of Clinical Cure Rapidity of Recurrence Burning/ Dosage:

Segmentation Strategy Rate Relief Rate Itching 1 Dose Per Buyer: A Priori

Product 1 20% above 2 days faster Equal to Delta 17% 10 days Product 2 20% above 2 days faster Equal to Delta 17% 10 days Gamma share 74.9% Return (index) 100

Buyer: Post Hoc Product 1 10% above 2 days faster Equal to Delta 2% 10 days 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 Product 2 20% above 2 days faster Equal to Delta 17% 10 days Gamma share 81.8% Return (index) 109

Importances: Post Hoc Product 1 20% above 2 days faster Equal to Delta 17% 10 days Product 2 20% above Equal to Delta Equal to Delta 2% 10 days Gamma share 79.2% Return (index) 103

Stepwise Segmentation Product 1 20% above 2 days faster Equal to Delta 17% 10 days Product 2 Equal to Delta Equal to Delta Equal to Delta 2% 10 days Gamma share 83.1% Return (index) 111




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 exhaus- tive 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 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 values are reached is not considered).

8. Segments are reachable, actionable, and substantial.

FIGURE 3 Profile Charts of Background Attributes

by Segmentation Types

Percent Group Segmentation Approach Practice

Buyer: Post Hoc

Part-worths: Post Hoc

Imporlance: Post Hoc

Stepwise Segmentation

Percent Internal Medicine

I I I

11 1 i

-::: .,..................... :: ...... .. . . . . . . ....1

II I

A . _ _ , A _ _ . _ A~~~~~~~~~~~~~~~~~ 100 0

1 Product 1

rI Product 2

100 0

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 rec- ords who chooses which brand/service in the array. Those 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."

Figure 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.)

From Figure 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 Figure 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).7 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

7Gamma's current product, however, was effectively cannibalized by the two new line additions. Its share after their introduction was only 4% of the total market.


Percent Efficacy/ Seeker/Switcher

100

Lengths of bars refer, respectively, to percent of segment classified as group practice, Internal medicine speciaty. and psychographic segment: efficacy/seeker/switcher.



researcher should also consider reachability and other aspects of its segmentation. This more general objec- tive accounts for the last step in the segmentation strategy shown in Figure 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 Figure 1 shows, segmentation and product positioning are interrelated. The emphasis of this 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 ana- lysts.

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 assign- able 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. Concom- itantly, 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 anal-

ysis 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 (Finkbeiner 1986).

Competitive Retaliation

For ease of presentation, our example does not include competitive retaliation. However, the model is capable of including action/reaction sequences. Con- sider 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 (e.g., Choi, DeSarbo, and Harker 1990), 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 ex- penditures, 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. Green and Srinivasan (1990) provide discussion on the general topic of predictive validity. Our applications of the proposed model have empha- sized pharmaceuticals, high tech products (such as computers and telecommunications), and consumer financial services such as credit cards. We have found




TABLE 4 Characteristics of Computer Programs Used in Case Study

Program Input What the Program Does Output SIMOPT Individual part-worths file For any set of competitive profiles, Market share/return

Individual importance weights the program computes share/ for each supplier file return for each supplier Individual supplier

Demographic (background) file All shares/returns are automatically selection file Current market shares for all adjusted to base-case conditions Optimal product

suppliers Sensitivity analyses can be description for total Each supplier's profile performed at the individual- market or selected Value of alpha and attribute level segment

demographic attribute Optimization can be carried out by Sensitivity analysis weights supplier or for groups of results, by level

Control parameters for suppliers; attribute levels can be within attribute optimization fixed for conditional optimization Pareto-optimal

Attribute-level cost/return data Analyses can be conducted at the frontier total market or selected target segment level

SEGUE Individual part-worths file For any target segment Attribute importance, Individual importance weight composable from the background level desirabilities,

file variables (with weights supplied and ideal levels, by Demographic (background) file by the user), the program selected segment Segment attribute weights computes size of segment, ideal Profile utilities by

levels, attribute importances, and selected segment attribute level desirabilities Respondent weights

Both additive and conjunctive file summarizing segments can be created each individual's

The user can also input any trial relevance to the product profile and find its total target segment utility compared to the best (input to SIMOPT) profile

A respondent weights file is prepared for later use in SIMOPT

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 re- alized.

Appendix 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 on a VAX mainframe).

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 enumer- ation (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 complemen-

tarity 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 (max utility, logit, BTL). The alpha rule assumes that the proba- bility of buyer k selecting brand s is given by

/ s

nk, = Uks U ks, s= 1

where Uks is the utility of buyer k for brand s, a is an exponent (typically greater than 1.0) chosen by the user, and S is the number suppliers.8

8If alpha equals 1, the model mimics the BTL share-of-utility rule; as alpha approaches infinity, the model mimics the max-utility rule.




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 summarizes the input/output aspects of each program, as well as several of the operations that each program performs.

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