A MULTIBRAND INTENTIONS MODEL FOR NEW PRODUCT

FORECASTING AND CONCEPT TESTING

Sharan Jagpal Rutgers Business School

E-mail address: jagpal@andromeda.rutgers.edu

Kamel Jedidi Columbia Business School

E-mail address: kj7@columbia.edu

Maqbul Jamil Novartis Pharmaceuticals

E-mail address: maqbul.jamil@pharma.novartis.com Sharan Jagpal is Professor of Marketing, Rutgers University. Kamel Jedidi is Professor of Marketing at the Graduate School of Business, Columbia University. Maqbul Jamil is Director, Global Business Analysis at Novartis Pharmaceuticals. The authors thank Rajeev Kohli for his helpful comments. Sharan Jagpal acknowledges the research support of the Faculty of Management, Rutgers University. Kamel Jedidi acknowledges the research support of Columbia Business School, Columbia University. Please address correspondence to Kamel Jedidi at Columbia University, Graduate School of Business, 518 Uris Hall, 3022 Broadway, New York, NY 10027, or via e-mail to kj7@columbia.edu.

1

A MULTIBRAND INTENTIONS MODEL FOR NEW PRODUCT FORECASTING AND CONCEPT TESTING

ABSTRACT

This paper develops and tests a multibrand intentions model for concept testing

and new product forecasting. Our methodology provides several statistical and managerial advantages over previous methods. In contrast to most studies, the methodology uses a multibrand intention measure. Consequently, it allows one to filter out measurement error (an endemic problem in intentions studies). In addition, the method allows for unobservable heterogeneity and differential biases in self-stated intentions measures across brands and segments. Hence, for any given new product concept, the firm can measure segment-specific cannibalization effects, determine the impact on competitive brands, and examine adoption patterns following the introduction of the new product into the marketplace. Finally, the method allows the firm to choose customized marketing mix strategies for different segments after allowing for the effects of competitive retaliation following the new product introduction.

We tested the methodology using multibrand intentions data for a major

multibillion-dollar therapeutic prescription drug category in the pharmaceutical industry. Approximately four years ago, a new entrant announced its imminent entry into the marketplace. Soon thereafter, the market leader conducted a large-scale experimental study to measure how the new brand would affect the industry. The major focus of the study was to determine how the leader should revise its marketing mix in view of the imminent new product entry.

The internal and external validity checks demonstrate that the methodology has

good predictive accuracy. Specifically, conditional on the marketing policies implemented in the marketplace, the predicted and actual market shares for the leader one year after the new product introduction are 30.16% and 32.97%, respectively. The corresponding shares for the entrant are 5.13% and 5.60%. The validation results also show that models that do not capture heterogeneity perform poorly.

The results show that the leader’s marketing mix has differential effects across

segments. The optimization results show that, in order to minimize the impact of the entrant on the leader’s product line, the leader should use customized message strategies for different segments. The market share simulation analysis shows that the method can be used to identify the physician segments that are most likely to adopt the entrant’s brand. In addition, the method can be used to determine those segments in which the leader will be particularly vulnerable to the entrant and the corresponding losses of market share to the entrant. Finally, the posterior analysis shows that the classificatory variables analyzed (e.g., the drug class, the physician’s prescription volume and brand loyalty) can be used for predicting segment membership and targeting.

Key Words: Concept Testing; New Products; Intention Scales; Market Segmentation; MCI Models; Latent-Class Models.

2

INTRODUCTION

In today’s rapidly changing markets, firms often have to make new product and

marketing mix decisions with limited information. Extensive market studies and

simulated laboratory tests may be too time-consuming or expensive. And, the

opportunity cost of new product failures is extremely high.

Consider the following scenarios. First, suppose a multiproduct firm is planning a

product-line extension and seeks to test different product concepts.1 Second, suppose a

competitor has announced that it will introduce a new product in the near future. The key

issues are to determine the potential effects of different marketing mixes (including

multidimensional product concepts) on demand for products in the firm’s product line

and the competitors’ brands. For both scenarios the problem is that no sales or test

market data are available; in addition, the market segments that the new product is likely

to attract may be unobservable.

A common approach for analyzing scenarios where purchase data are unavailable

is to use self-stated intentions data for a given brand/product to predict market behavior.

This approach has several limitations. Single-brand intention measures (discrete or

continuous) do not allow one to filter measurement error. Consequently, market share

estimates can be severely biased. For example, Infosino (1986, p. 375) used a 10-point

scale to measure the intention to purchase a new wireless service. The results showed

that only 45% of those who chose a “10” (i.e., the “Definitely will buy” category)

actually purchased the new product. The intentions results for the other nine categories

(scores of “1” through “9” inclusive) were even less accurate. Another limitation of 1 See Moore and Pessemier (1993, pp. 245-265) for a detailed review of extant concept testing methods.

3

using single-brand, self-stated intention measures is the absence of a natural translation

from discrete intention scales to choice probabilities. Consequently, ad hoc approaches

(e.g., the top-box method) are necessary to predict market behavior. In addition, if a

single-brand intention measure is used, one cannot measure how the new product concept

will impact competitive brands and other brands in the firm’s product line (i.e.,

cannibalization effects). Finally, previous methods using self-stated intentions do not

allow for unobservable heterogeneity, including differential measurement errors across

brands and segments (e.g., consumers may be more uncertain about purchasing a new

rather than an established brand). Thus, the model forecasts are likely to be biased.

This paper attempts to address these difficulties. Specifically, we use a

multibrand probability-based intention measure (e.g., a constant-sum type measure) and

formulate the problem as a finite mixture Multiplicative Competitive Interaction (MCI)

model in which the covariance matrix of residuals is nonspherical (i.e., the error terms are

heteroscedastic and correlated across brands).

Our multibrand intentions model provides several statistical and managerial

advantages. In the spirit of structural equation models, having multiple intentions

measures (across brands) allows one to simultaneously filter measurement error for

different brands and capture structural errors (errors in equations). By using a

multibrand probability-based intention measure, the model provides a natural translation

from intention to choice. By allowing for unobservable heterogeneity, the latent-class

approach allows for differential biases in self-stated intentions scores across brands and

segments. Hence the method provides unbiased and objective market share estimates for

all brands at all levels of aggregation (i.e., market- or segment-level).

4

Managerially, the firm can estimate how its new product concept will impact the

market shares of all brands in the market (including its own product line) on a segment-

by-segment basis. Thus, the firm can measure the segment-specific and aggregate

cannibalization effects of the new product concept and determine the corresponding

effects on competitors’ market shares. In addition, by choosing an appropriate

experimental design, the firm can analyze in detail the segment-by-segment effects of

competitive retaliation to the new product concept on all brands in the market, including

its own product line. Finally, the firm can identify the segments that are likely to adopt

the new product and devise a customized marketing plan to reach each targeted segment.

We tested the methodology for a new pharmaceutical brand in a multibillion-

dollar market. Approximately four years ago, a new entrant announced its imminent

entry into the industry. Soon thereafter and prior to the new product introduction, the

market leader conducted a large-scale experimental study to determine how the new

product entry would affect the market. Particular foci of the study were to determine how

the leader should change its product design and modify its message strategies and how

these changes would impact the new entrant.

We tested model robustness using both internal and external validation checks.

Internal validation was performed using the standard cross-validation approach of

holdout samples. External validation was performed by comparing the predicted market

shares, conditional on the actual marketing policies in the marketplace, with the actual

market shares one year after the experiment was conducted for (a) the same sample of

physicians and (b) the total population of physicians in the market.

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Both the internal and external checks show that the methodology is robust.

Models that do not capture heterogeneity perform poorly. The heterogeneity model has

good predictive accuracy; the aggregate model does not. For example, the cross-

validation results for the holdout sample show that the aggregate model significantly

overpredicts the leader’s volume-based market share (actual share = 31.86%, predicted

share = 43.10%). The heterogeneity model performs better (predicted share = 34.54%).

Both external validity checks confirm the cross-validation findings. Conditional

on the actual marketing policies in the marketplace, the heterogeneity model predicted

market shares of 30.16% and 5.13%, respectively, for the leader and the entrant. The

corresponding actual market shares one year after the experiment was conducted were

29.82% and 7.43% for the same sample of physicians and 32.97% and 5.60% for the total

physician population. These results suggest that the sample is representative and provide

strong evidence for the predictive validity of the methodology.

The external validity results also show that the heterogeneity model outperforms

the aggregate model. Thus, the aggregate model significantly overpredicts the market

share for the leader (predicted share = 39.83%). Interestingly, the aggregate model

predicts reasonably well for the entrant (predicted share = 4.97%). However, the

aggregate model is misleading because it fails to correctly identify the sources of these

market share gains across brands and to measure these effects.

The main managerial findings are as follows. The leader’s marketing mix has

differential effects across segments and brands. The optimization results show that the

leader should use different message strategies for different segments to minimize the

impact of the entrant on its product line. The market share simulation results show that

6

the method can be used to determine the physician segments that are likely to adopt the

entrant’s brand and to measure the corresponding share losses to the entrant. Thus, the

results can be used to identify those segments in which the leader is particularly

vulnerable to the entrant.

The posterior analysis shows that the classificatory variables analyzed (e.g., the

drug class, the physician’s prescription volume and brand loyalty) are useful in predicting

segment membership. Specifically, the adoption rates, competitive structures, and

customer profiles for the new entrant vary considerably across segments. Interestingly,

the segment that is most likely to adopt the entrant’s brand includes physicians who are

currently non-users of the drug class in which the leader and the entrant compete. This

result is good news for the leader because the entrant’s share gain comes mostly from

outside the drug class; furthermore, the entrant’s new brand will expand category sales

and lead to market growth.

The rest of the paper is organized as follows. Section 2 reviews the literature.

Section 3 describes the methodology. Section 4 discusses the experimental design and

data collection method used in the empirical study. Section 5 analyzes the statistical

results. Section 6 focuses on the managerial implications of the model. Section 7

summarizes the results and discusses future research directions.

2. LITERATURE REVIEW

Concept testing is a popular managerial tool for evaluating new product ideas.

See Dolan (1993, pp. 209-220) for a succinct discussion of commonly used methods.

Our discussion focuses on three key issues in concept testing and new product

7

forecasting: measuring self-stated intentions; the empirical evidence on the intention-

purchase relationship; and heterogeneity.

Measuring Self-Stated Intentions

Intention can be measured on a continuum using a probability-based estimate of

the likelihood of purchase. Alternatively, intention can be measured using a finite scale

with a fixed number of points (e.g., a 5-point intention scale with end points “Definitely

will not buy” and “Definitely will buy”). 2 See Dolan (1993, pp. 89-92) and Urban and

Hauser (1993, pp. 302-309). Juster (1966) showed in his durable goods study that

probability measures of intention outperform binary intention measures (i.e., “Yes” and

“No” responses). However, self-reported purchase probabilities were biased downwards

(i.e., they underpredicted the actual purchase rate). In contrast, Bird and Ehrenberg

(1966) found that, for the branded packaged goods in their study, the bias was in the

opposite direction. Most marketing studies, however, use a single-brand intentions

measure and a discrete (e.g., a 5-point) scale. This approach leads to several statistical

and managerial difficulties.

Statistically, single-brand intention measures (whether discrete or continuous)

lead to an identification problem. That is, one cannot separate the true intention from

measurement error. Consequently, market share estimates from models using single-

brand intentions measures can be severely biased. Furthermore, the translation from

discrete intention scores to purchase behavior is unknown. For example, not all those

2 Infosino (1986) uses a hybrid approach derived from utility theory. He theorizes that the likelihood of purchase is a monotonically increasing function of value/consumer surplus (i.e., the difference between the consumer’s reservation price for a product and its price). As in Infosino’s study, suppose the researcher uses a 10-point scale. Then the monotone transformation of value is truncated to an integer between one and ten. Note that Infosino assumes (restrictively) that the monotonic transformation from value to the intention scale is identical across consumers.

8

who choose the highest score (“5”) will purchase the product; similarly, some of those

who choose a score of “3”, say, will purchase the product. To address this problem,

practitioners often use a differential weighting scheme (see Jamieson and Bass 1989, pp.

337-340 for a discussion). For example, the top-box method assumes that the best point

estimate of demand is the proportion of respondents who score in the top box (“5” in our

example). Unfortunately, no one weighting scheme dominates the others for all products

(see Jamieson and Bass 1989). Finally, data from discrete scales are subject to

categorization bias because different individuals interpret the scale idiosyncratically.

This introduces an additional source of measurement error and further attenuates the

measured intention-purchase relationship. Although not subject to categorization bias 3,

single-brand probability-based intention measures are also imperfect. For example,

some intenders will not buy and some non-intenders will buy (see Theil and Kosobud

1968, p. 53).

Managerially, single-brand intention measures (discrete or probability-based) are

restrictive. One cannot measure the cannibalization effects of a new product concept on

the firm’s product line. In addition, one cannot measure the impact of the new product

concept on competitors’ market shares or determine the impact of competitive retaliation.

The Intention-Purchase Relationship

In an important paper, Morrison (1979) emphasizes that intentions models must

explicitly allow for measurement error (measured intentions ≠ true intentions which are

unobservable), systematic biases, and multiple sources of heterogeneity. Specifically,

Morrison develops and tests a beta binomial intention model using intentions data for two

3 A probability-based scale should leave less ambiguity in the respondent’s mind about what is really meant by the question.

9

durable goods: automobiles and appliances. The results show that over time intentions

data are more stable for automobiles than for appliances. Kalwani and Silk (1982) tested

Morrison’s model for several additional durable and nondurable products. They found

that the beta binomial model fits better for durables than for nondurables. Furthermore, a

linear intention-purchase model is appropriate for durables; in contrast, a piecewise linear

model does better for nondurables.

In contrast to the previous studies, Jamieson and Bass (1989) examined new

products (five durables and five nondurables). They found that the intention-purchase

relationship is somewhat stronger for nondurables than for durable products.

Furthermore, predictive accuracy was significantly improved when perceptual

dimensions (e.g., product awareness and affordability) were incorporated into the model.

Morwitz and Schmittlein (1992) examined the intention-purchase relationship for a range

of product categories. They found that intentions can be biased either upwards or

downwards. Furthermore, for any given product, the intention-purchase relationship is

segment-specific.

Recently, Young, DeSarbo and Morwitz (1998) developed an intention-purchase

model in which purchase behavior is related to covariates according to a binary

regression model, intention is measured as a binary variable, and intentions represent a

random perturbation of the purchase behavior data. As the authors note (p. 201), their

model uses a scalar intention measure. Consequently, it cannot be used to analyze

competitive effects unless one makes restrictive assumptions (p.195). In addition, the

method uses an aggregate specification. Consequently, it cannot capture heterogeneity or

be used for segmentation.

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Finally, Morwitz, Steckel, and Gupta (1999) performed a meta-analysis of

intention-purchase studies conducted during 1957-1994. They found that intentions are

better predictors for existing products than for new products. Furthermore, in contrast to

Jamieson and Bass (1989), intentions are better predictors for durables than for

nondurables.

Heterogeneity

Several authors have noted that intentions studies must explicitly incorporate

heterogeneity. Morrison’s model (1979) assumes that true intentions vary on a

continuum, are heterogeneous, and can be captured by a beta distribution. Morrison (p.

72) also suggests additional segmenting dimensions that may be useful in exploring the

intention-purchase relationship. Manski (1990, p. 937) provides an example

demonstrating that individual-level differences do not average out in the aggregate. This

result shows that it is crucial to explicitly model heterogeneity in intentions models.

Jamieson and Bass (1989) show that predictive accuracy increases when perceptual

heterogeneity is included in the model. Similarly, Morwitz and Schmittlein (1992) show

that, for a given product, the intention-purchase relationship is segment-specific.

Discussion

Extant concept testing and new product forecasting models do not address several

key problems: correcting for measurement error in self-stated intentions data, capturing

unobservable heterogeneity, allowing for differential biases in self-stated intentions

scores across brands and segments, measuring how different marketing strategies (e.g.,

product concepts and marketing mixes) impact different brands and segments in the

marketplace, and determining the effects of competitive retaliation if the new product

11

concept is introduced into the marketplace. We now propose a method that attempts to

address these problems.

3. THE METHODOLOGY

Suppose the firm is interested in determining how a given product concept (as a

result of a product-line extension or a new competitive entry) will impact the

marketplace.4 As Moore and Pessemier (1993, p. 253) point out, the key issues in

standard concept testing are: (a) To choose the most appropriate target segments (which

are often unknown at this stage of product development); (b) To obtain robust estimates

of demand for the new product; and (c) To determine whether there is sufficient

consumer appeal to warrant further product development.

This paper argues and demonstrates that concept testing can be used to address

additional managerial issues. Thus, the firm can use concept testing to identify and target

unobservable consumer segments. In addition, it can determine the potential impact of a

new product concept and its marketing mix on other products in the firm’s own product

line (i.e., cannibalization) and those of its competitors. By choosing an appropriate

experimental design, the firm can assess the effects of competitive retaliation on the

performance of its product line including the new product, even before the new product is

introduced in the marketplace. Finally, the firm can develop a customized marketing plan

for the new product to reach targeted segments, after allowing for the effects of

competitive reaction.

4 See Moore and Pessemier (1993, pp. 245-265) for an extensive discussion of standard concept testing methods.

12

Experimental Design and Data Collection

To address the problems described above, suppose that the product development

process is sufficiently advanced so that the firm is ready to test the new product concept

under different marketing mix strategies (i.e., different positioning statements, different

price levels, etc.) and possibly under different competitive conditions. The competitive

conditions can be specified to reflect the most likely reactions of current competitors

(e.g., price reductions) to the new product introduction. The marketing mix levels and

the competitive reactions represent the experimental factors that need to be tested.

Depending on one’s research objectives, one can use either a between- or a within-

subjects design.

The new product concept and its associated marketing mix strategy can be

presented to a representative sample of consumers in several ways (e.g., written

descriptions, pictorial representations, or a combination). If the effects of competitive

reactions are to be measured, information about competitive products should be provided

using print advertisements or other alternative formats. As usual, subjects should be

randomly assigned to different experimental conditions (i.e., a product concept described

using different marketing and competitive conditions).

Our data-collection strategy differs from standard practice. Specifically, most

concept testing studies use a scalar measure of intentions. In contrast, our method

requires respondents to provide a multibrand intention measure using the constant-sum

approach. Thus, one can ask respondents to allocate 100 points across brands (including

the new product) to reflect the appropriate purchase likelihoods. If the number of brands

in the market is small, the choice set should include all brands. If the number of brands

13

is large, one should ask respondents to only allocate points to brands in their

consideration sets.

In addition to the multibrand intentions data, respondents should be asked to

provide information on key individual-level characteristics that will be used for

describing the market segments and identifying the early adopters of the new product. In

addition to demographic, socioeconomic, and attitudinal variables, the individual-level

characteristics can include such behavioral measures as usage and frequency rates and

regular brand purchased.

The Model

To simplify exposition, we first describe the aggregate model and then present the latent

class extension.

The Aggregate Model

Suppose the product category consists of J brands including the new product

concept. For notational convenience, consider a between-subjects experiment.

Consumer i’s task is to evaluate the new product concept and allocate points to the J

different brands using a constant-sum approach. Let Pij be consumer i’s self-stated

probability of choosing brand j from choice set C=j, j=1, Ω, J. Following Nakanishi

and Cooper (1988), we use a Multiplicative Competitive Interaction (MCI) specification.

Let ijA be the attraction score of brand j for consumer i. This attraction score depends on

brand-specific variables (e.g., the price and positioning strategies of different brands in

the marketplace) and consumer characteristics (e.g., demographics).

Specifically, let

14

where )( ijlijk zx is the value of brand j on the kth continuous (lth discrete) variable for

consumer i, )( jljk γβ is the associated parameter that measures the impact of this variable

on the attraction of brand j, and ijδ is a structural error term that captures missing

variables and model misspecification.

Then the observed probability that consumer i chooses brand j is

)2(,,,1;,,1

1

NiJjA

AP J

mimim

ijijij LL ===

∑=

ζ

ζ

where ijζ denotes the measurement error in the self-stated choice probabilities due to

respondent bias. We assume that both ijδ and ijζ follow independent lognormal

distributions. (Independence of the specification and measurement errors is not

statistically necessary. However, it is conceptually reasonable.)

To estimate the aggregate intentions model, we choose a reference brand (say

brand J) and apply the log-odds ratio transformation as follows:

)3(,1,,1,loglogloglog −=

+

+

=

Jj

AA

PP

iJ

ij

iJ

ij

iJ

ij

iJ

ijL

ζ

ζ

δ

δ

where ∏∑+=k

ijkl

ijljljijjkxzA .)exp( 0

βγγ Let .log

=

iJ

ijij P

Py Then the (J-1) multivariate

regression system in Equation (3) can be simplified to

)1()exp( 0 ijk

ijkl

ijljljijjkxzA δγγ β∏∑+=

15

)4(,0 ijk

imkmkk

ijkjkl

imlmll

ijljljJij xxzzy εββγγγ +

−+

−+= ∑∑∑∑

where

+

=

iJ

ij

iJ

ij

ζ

ζ

δ

δε loglogij and 000 JjjJ γγγ −= is the difference in intercepts.

Recall that ijδ and ijζ follow log-normal distributions. Therefore, the (J-1) vector

),,( iJi1 ′= εε Liε follows a multivariate normal distribution ).,( Σ0MVN

Several observations should be made about the model before discussing its latent

class extension. In contrast to single-brand models, the multibrand attraction model in

Equation (1) allows one to filter measurement error in intentions data. The model is

general because it can accommodate both the multiplicative and the exponential

functional forms and allows all parameters to vary across brands. As usual, in practical

applications the model can be simplified by introducing constraints (e.g., restricting some

parameters to be invariant across brands).

The observed probabilities depend on both structural errors (the δ’s) and

respondent biases (the ζ’s). Note that the structural and measurement errors are

confounded and cannot be empirically separated (see Equation (4)). However, this lack

of identifiability does not pose any statistical or managerial problems. Note that the

mean biases and variances for the self-stated probabilities (intentions) can vary across

brands. This model property is important (e.g., consumer intentions may be biased

upwards for brands with high market shares and downwards for brands with low market

shares).

The self-stated intentions values for certain respondent-brand combinations may

be zeroes. See Nakanishi and Cooper (1988, pp. 153-155) for an excellent discussion of

16

this issue in the context of MCI market-share models. We address this problem using

the approach suggested by Nakanishi and Cooper (p. 154). Thus, suppose consumer i’s

self-stated intention score for brand j is zero. Then, for consumer i add the same small

positive constant to the self-stated intentions scores for all brands. Finally, the choice of

reference brand is irrelevant because we use maximum likelihood (see Berndt 1991, pp.

473-4).

The Latent Class Model

The aggregate model in Equation (4) is reasonable if consumers are homogeneous

in their responses to marketing variables. Alternatively, if segments can be defined a

priori, one can use the aggregate model separately for each segment. However, if a

priori segmentation is not possible and consumers are heterogeneous, aggregate analysis

will produce biased results. .

We propose a latent class approach to capture unobservable consumer

heterogeneity. Let s denote membership in a latent segment (s=1, Ω, S) and

=

sPsP

siJ

iji |

|log|y denote a (J-1ä1) vector of log-odds where sPij | is the conditional

choice probability of brand j given consumer membership in segment s. Suppose si |y

has a conditional multivariate normal distribution with mean vector ),,( 11 ′= −sJ

ss µµ Lµ

and covariance matrix sΣ where

)5(.0

−+

−+= ∑∑∑∑

kimk

smk

kijk

sjk

liml

sml

lijl

sjl

sjJ

sj xxzz ββγγγµ

Then, the unconditional distribution of the observed vector )',,( 11 −= Ji yy Ly is a finite

mixture of these distributions. That is,

17

( ) )6(,|~1

ssis

S

ssi fw Σµyy ∑

=

where ),,( 1 ′= Sww Lw is the vector of the S mixing proportions such that 0>sw and

,1=∑S

ssw and f(.) is the conditional multivariate normal density function ).,( ssMVN Σµ

The likelihood function for a sample ),,( 1 Nyy L of i=1, Ω, N randomly drawn

observations from the mixture is then:

( ) )7(,,|1 1∏∑= =

=N

issis

S

ssS fwL Σµy

where LS is a function of the segment-level parameters sjk

sjlsw βγ ,, and sΣ (s=1, Ω, S).

The problem is to maximize LS (or, equivalently, log LS) with respect to the parameters

given the sample data and the pre-specified number of segments S, while taking into

account the constraints imposed on w above.

Model Estimation and Selection

We use an E-M algorithm to maximize the likelihood function in Equation (7).

For a detailed discussion of the E-M methodology see Dempster, Laird, and Rubin

(1977).

To formulate the E-M algorithm, begin by defining a latent indicator variable isλ

as:

=otherwise. 0

s,segmentlatenttobelongsiconsumer1 iffisλ

Assume that for a particular consumer i, the unobserved vector ),,( 1 ′= iSii λλ Lλ is i.i.d.

multinomially distributed with probabilities w. That is,

18

)8(~|1∏=

S

ssi

iswλwλ

Then the distribution of ii λy given is:

( ) ( )[ ] )9(.,|,|~|11∏∑==

=N

ississsis

S

sisii

isff λλ ΣµyΣµyλy

With ))(( isλ=Λ considered as missing data and ))(( ijy=Y as the input data matrix, the

complete-data, log-likelihood function to be maximized is given by:

)10(,)log()()(21

21)2log(

2)1(

),|,,(Log

1 1

1

1 1

1 11 1

∑∑∑∑

∑∑∑∑

= =

−

= =

= == =

+−′−−

−−−

=

N

i

S

ssississi

N

i

S

sis

s

N

i

S

sis

N

i

S

sisc

w

JL

λλ

λλπ

µyΣµy

ΣΛYwΣµ

where ).,,( 1 SΣΣΣ L=

The E-M algorithm maximizes (10) by iterating between two steps until

convergence occurs. The first is an E-step in which we compute the expected value of

Yλ giveni and provisional estimates for wΣµ and,, . The second is an M-step where we

maximize (10) conditional on the newly estimated values ))(( isλ=Λ to estimate all

model parameters. One advantage of the E-M algorithm in this context is that, because of

conditioning, all parameters have closed-form solutions. Thus it is not necessary to use

non-linear optimization routines. Once convergence is obtained, it is straightforward to

obtain final estimates of the model parameters and the corresponding asymptotic

covariance matrix.

We assign consumers to each of the S segments by using Bayes’ rule:

19

( )( )

)11(ˆ,ˆ|ˆ

ˆ,ˆ|ˆˆ

1ggig

S

gg

ssissis

fw

fw

Σµy

Σµy

∑=

=π

where isπ denotes the estimated posterior probability that consumer i belongs to segment

s. These probabilities represent a fuzzy classification of the N consumers into the S

segments.

To assess the degree of separation among segments, we use an entropy measure

defined by:

)12(.)(

)ˆlog(ˆ1

SLogNE

isi s

is

s

ππ∑∑−−=

This measure is bounded by 0 and 1. A value close to 0 indicates that the

segments are not well separated. A value close to 1 indicates excellent separation.

In many practical applications, the number of segments is not known a priori. To

address this problem, we estimate the latent class model by varying the number of

segments (say from S=1 to 8 segments), and choose the solution that corresponds to the

minimum value of the Bayesian Information Criterion (BIC):

(13),log(N) M L log 2- BIC SSs +=

where LS is the likelihood function value (see Equation (7)) and MS is the number of

parameters in the S-segment model. Note that, because BIC is an increasing function of

the number of parameters MS, it penalizes models with more parameters and segments.

Managerial Analysis

20

We distinguish among the following managerial uses of the model: (a) Demand

estimation and demand structure; (b) Targeting key customer segments; and (c)

Determining the effects of competitive reaction.

Demand Estimation and Demand Structure

The key issues are as follows. What is the likely demand for the new product? Which

brands and segments are the sources of this demand? Which market segment(s) should

the firm target? What customized marketing mix should the firm use to target a particular

segment?

The demand for the new product is obtained by multiplying the market size by the

new brand’s predicted share (added across segments) conditional on a marketing mix

plan. To obtain this demand estimate, proceed as follows. For any given marketing mix

plan, it is straightforward to predict the unconditional individual-level choice

probabilities, ijP , for each brand as follows:

)14(|1

sPPS

sijisij ∑

=

= π

where sPij | is the conditional choice probability of brand j given consumer membership

in segment s and isπ is the posterior probability of membership of consumer i in segment

s.

Because consumers have heterogeneous product usage rates, we need to weight

these choice probabilities by the individual-level usage rates, ,iQ to estimate the

aggregate market shares, ,jMS for each brand.5 That is,

5 If the product is a durable, the consumer usage rates are trivially equal across all respondents.

21

)15(.

1

1

∑

∑

=

==N

ii

N

iiji

j

Q

PQ

MS

The predicted market shares in equation (15) can be further adjusted to capture

differences in awareness and availability of the brands in the market6.

To determine the sources of market share gains for the new product, one can

simply compare the predicted brand shares when the market includes and does not

include the new brand. Gains from brands in the firm’s current product line reflect losses

due to cannibalization and gains from competitive brands reflect the gains from

customers switching to the new brand. Note that a major advantage of our methodology

is that the market shares and the gains in market shares for any brand can be analyzed

both at the aggregate and the segment levels.

Targeting Key Segments

The key issues are threefold. Which segment(s) should the firm choose? Can the

firm develop customized marketing plans to reach each targeted segment? How can the

firm efficiently reach each targeted segment?

The firm can identify the segments that are most likely to adopt the new product

by analyzing the segment-level market shares for the new product. This information

combined with the size, usage rates, and the growth rates of different segments can then

be used for selecting key segments.

The model results can be used for developing customized marketing mixes

tailored to the needs of a targeted segment. Recall that the model parameters measure

6 These measures should be available for extant brands or brand modifications. However, managerial guesstimates must be used for brands (products) that are new to the marketplace.

22

consumers’ responsiveness to the marketing mix variables analyzed in the concept test.

For any specified marketing plan, we can use the parameter estimates to predict both

aggregate and segment-level market shares. By comparing the effects of different

marketing plans on a segment-by-segment basis, the firm can develop a customized

segment-specific marketing plan that maximizes the firm’s objective function (e.g.,

market share, sales, and profitability).

The firm can identify the target segments by performing a posterior analysis using

the posterior probabilities of segment membership as the dependent variables and the

relevant set of classificatory variables as independent variables. Because the posterior

probabilities are bounded in the [0,1] interval and must add to one across segments, a

suitable dependent variable is the log-odds ratio, .log

iS

is

ππ The results of the posterior

analysis can be used to determine the demographic, socioeconomic, attitudinal, and

behavioral variables that are significant in predicting segment membership.

Competitive Reaction

The key issue is: How will competitive reaction affect the success of any given

marketing plan for the new product? Recall that our methodology allows one to include

anticipated competitive reactions (e.g., price cuts) as factors in the concept test. Thus, by

choosing an appropriate experimental design, the firm can use the model results to

determine the likely impact of implementing a customized marketing plan for the new

product on a segment-by-segment basis, after allowing for competitive reaction.

4. EMPIRICAL STUDY

23

Approximately four years ago, a major pharmaceutical company announced its

imminent entry into a major multibillion-dollar therapeutic prescription drug category

containing six established brands. In view of this announcement, the market leader was

faced with several choices. Should it match the product attribute (dosage strength) to be

introduced by the new entrant? Should it change its pricing strategy as a result of the new

entry? Should it reposition its product by emphasizing additional benefits (i.e.,

indications)? How will the leader’s strategies affect the market shares of competitors,

particularly the new entrant?

Shortly after the new brand was announced and prior to the new brand’s entry, the

market leader decided to apply to the FDA for approval to introduce a new product

variation to match the dosage strength of the new entrant’s brand. In addition, the market

leader conducted a large-scale physician study to measure the potential impact of the new

brand on the industry. Recall that, at the time the study was conducted, the new brand

had not been introduced into the marketplace. Consequently, no sales data were available

for the new brand.

The research design was as follows. A set of physicians was screened using

selection criteria provided by the firm (e.g., specialty and category prescription

behavior). Based on the selection criteria, a sample of 780 physicians was recruited to

participate in the study.

Following is a brief description of the parts of the experiment that are relevant to

our study. A (3 X 4) full-factorial, between-subject experiment was conducted; thus, 65

physicians were assigned to each cell. The experiment manipulated two key marketing

variables for the market leader: prices (3 levels) and types of detail message (4 levels).

24

The three price treatments are labeled “Regular,” “Low,” and “High,” respectively. For

the regular price treatment, the leader maintains its current pricing policy. For the Low

(High) price treatments, the leader reduces (increases) all regular prices by 10%.

The four detail treatments consist, respectively, of the current detail message and

three new messages. The current detail message focuses on those medical indications for

which the market leader already has FDA approval. In contrast, each new message

strategy focuses on new sets of medical indications that the leader’s brand could treat.

The leader’s goal in this manipulation was to determine the best set of additional medical

indications for which to seek FDA approval. Note that dosage was not manipulated

experimentally because the leader had already decided to match the new dosage strength

to be introduced by the entrant. However, the study measured physicians’ reactions to

the leader’s new dosage strength. Consequently, we treat this information as a covariate

in our study.

The experiment was conducted as follows. An extensive mail questionnaire was

sent by overnight mail to each physician in the sample. Approximately one week after

the mailing, a phone interview was conducted with each participating physician.

After answering several warm-up questions, the physician was instructed by the

interviewer to open a sealed envelope (Envelope 1) and read the contents. Specifically,

the envelope contained price and dosage information for all seven brands (including the

new brand) in the marketplace. Next, the physician was asked several questions

pertaining to the information contained in Envelope 1; in addition, (s)he was asked

several questions measuring physician-specific variables (e.g., demographics, patient

mix, and prescription behavior). One of the questions asked the physician to use an

25

assigned scale to rate the benefits of the new dosage strength to be introduced by the

market leader.

The physician was then instructed to open another sealed envelope (Envelope 2)

containing information about one of the four message strategies tested in the experiment.

After reading the enclosed information, the physician was asked to answer several

questions pertaining to that information. After answering several additional questions,

the physician was asked to perform the following constant-sum task. Consider the next

ten patients you will treat who suffer from the disease in question. How many patients

will you treat using each of the brands listed in the first envelope? We use this measure to

compute the proportion of patients allocated to brand j by physician ., ijPi

5. EMPIRICAL RESULTS

We tested the model using the following approach. The brands in the category are

labeled Brands 1 through 7 inclusive, where Brand 3 denotes the market leader and Brand

7 the new entrant. The four detail message treatments are labeled Message 0 (current

message) and Messages 1 through 3 inclusive (new messages). Message 0 pertains to

medical conditions for which the leader’s brand has already obtained FDA approval.

Messages 1 through 3 inclusive pertain to medical conditions for which the leader has

applied for but not obtained FDA approval.

Brand 5 was chosen as the reference brand. We used the “Low” price and current

detail “Message 0” as the baseline treatments for price and message, respectively. Thus,

all parameter estimates should be interpreted as deviations from the appropriate

quantities. For example, the brand intercepts for the market leader (Brand 3) and the new

26

entrant (Brand 7), respectively, are the corresponding deviations from the brand effect for

the reference brand (Brand 5).

We estimated the complete model including all pairwise interactions among the

treatments (price and message strategy). However, to avoid notational clutter, we

describe the main-effects model below. Let ssj

sj 50050 γγγ −= denote the differences in

intercepts between brand j (j=1,2,3,4,6,7) and the reference brand 5 in segment s. Let

sl

sjl

slj 55 γγγ −= measure the differential impact of marketing mix variable l relative to the

reference brand (Brand 5). Then the segment-specific model has the following template:

)16(,| 2561555435325215150 isji

sji

sji

sji

sji

sj

sjij PRICEPRICEDOSDETDETDETsy γγγγγγγ ++++++=

where

=

sPsP

si

ijij |

|log|

5y is the log-odds ratio of conditional choice probabilities, DOSi is

a variable that measures physician i’s reaction to the leader’s new dosage, PRICEil is an

indicator variable for the price level assigned to physician i where 1=l ( 2=l ) represents

the regular (high) price, and DETik is a dummy variable that indicates whether physician i

was assigned to new detailing message k (k=1,2,3).

None of the price coefficients (including the relevant interaction terms) was

significant. Thus physicians are not price-sensitive for the price range and product

category examined. This result is consistent with previous pharmaceutical studies (e.g.,

Gönul et al. 2001). Consequently, these price parameters were dropped from the analysis.

27

Aggregate Model

The results for the aggregate model (which assumes homogeneity) are shown in

Table 1, Column 3.7 All brand-specific intercepts are significant. The marketing mix

effects are as follows. Relative to the current message, only Message 1 has a positive and

significant impact on the leader. Only Message 3 has a significant direct impact on

competitors. Specifically, Message 3 has a negative effect on the new entrant (Brand 7).

The new dosage strength for the market leader has no impact whatsoever in the

marketplace.

Heterogeneity Model

We used the latent class methodology to test for unobservable heterogeneity. The

results show that the data are not homogeneous. Using the BIC criterion, we find that

7 Recall that our multivariate regression system has six equations (one equation for each brand except the reference brand 5). To save space, Table 1 reports the estimates for all six equations in a condensed form. .

Parameter Aggregate Seg 1 Seg 2 Seg 3 Seg 4 Seg 5Brand 1 0.41 0.186 1.778 -1.832 3.852 1.238Brand 2 2.061 3.44 1.476 -0.905 5.383 4.17Leader 2.497 3.709 1.774 -0.758 6.181 4.293Brand 4 0.795 1.153 1.591 -1.272 3.823 1.517Brand 6 1.452 3.363 2.445 -1.324 5.158 1.528Entrant 0.416 0.716 1.566 -2.013 4.046 1.054Detail 1 0.459 0.077 0.685 0.68 0.344 -0.016Detail 2 0.181 -0.049 0.541 0.543 -0.099 0.073Detail 3 -0.02 0.004 -0.397 0.77 0.248 0.119Detail 3 -0.653 -1.14 -0.883 0.274 -1.066 -0.557Dosage 0.068 0.034 0.461 0.175 -0.036 0.016

100% 18.33% 19.50% 30.42% 7.57% 24.18%

Table 1: Parameter Estimates for Aggregate and Five-Segment Solutions

Parameter estimates in bold are significant at p < 0.05 level.Parameter estimates in italics are significant at p < 0.1 level.

Inte

rcep

tM

arke

ting

Mix

Variable

Segment Proportions

s150γs250γs350γs450γ

s750γs351γs352γs353γs753γ

s650γ

s354γ

28

there are five segments of highly unequal size.8 The proportions of physicians in each

segment are as follows: 18.33% (Segment 1), 19.50% (Segment 2); 30.42% (Segment 3);

7.57% (Segment 4); and 24.18% (Segment 5).

The results for the heterogeneity model (see Table 1, Columns 4 through 8) show

that all brand intercepts are significant across all five segments. In contrast to the

aggregate model, the new dosage strength will impact the market (see the results for

Segments 2 and 3). Importantly, as discussed in the next section, there are considerable

differences in how the leader’s marketing mix affects each segment on a brand-by-brand

basis.

Model Validation

Model validation was performed for the aggregate and heterogeneity models

using both internal and external validity checks.

Internal Validity

We performed an internal validity check using cross-validation. Thus, 80% of the

respondents were randomly assigned to the analysis sample and the remaining 20% to the

holdout sample. We used the results from the posterior analysis (see Section 6) to

probabilistically classify respondents from the holdout sample into the five segments.

We then used the segment-specific parameter estimates to predict each respondent’s

choice probability vector for the seven brands. Predictive accuracy was analyzed by

comparing the predicted market shares to the actual (self-stated) market shares for the

holdout sample.

8 The ordered BIC values for the one- to six-segment solutions, respectively, are 22593.36, 21217.33, 20839.47, 20766.83, 20696.45 (five-segment solution), and 20719.47. Thus, the five-segment solution, which corresponds to the minimum BIC value, is chosen.

29

The results in Table 2 show that the methodology has high internal validity.

Models that do not capture heterogeneity perform poorly. The heterogeneity model has

good predictive accuracy; the aggregate model does not. Specifically, the market share

predictions from the cross-validation analysis show that the heterogeneity model is

superior. Thus, for the holdout sample the prediction error for the leader’s market share

using the aggregate model is 11.24% (i.e., 43.10% - 31.86%). The corresponding

prediction error for the heterogeneity model is much lower (2.68%). The results for the

entrant are similar: the prediction errors are 3.68% (aggregate model) and 2.08%

(heterogeneity model). Over all, the average Mean Absolute Deviations (MAD) between

the actual and predicted market shares across brands for the aggregate and heterogeneity

models, respectively, are 4.20% and 2.26%.

The cross-validation results also show that the predictions from the heterogeneity

model are more stable than those for the aggregate model. See Table 2. The prediction

stability varies considerably across brands for the aggregate model compared to the

heterogeneity model. The MADs for the leader are 11.24% (aggregate model) and 2.69%

Aggregate Heterogeneity Aggregate Heterogeneity1 7.51% 4.65% 7.54% 2.86% 0.04%2 20.83% 24.15% 17.91% 3.32% 2.92%

Leader 31.86% 43.10% 34.54% 11.24% 2.69%4 10.99% 6.85% 7.90% 4.14% 3.09%5 5.37% 3.08% 9.44% 2.29% 4.07%6 15.05% 13.19% 16.00% 1.86% 0.94%

Entrant 8.40% 4.72% 6.32% 3.68% 2.08%4.20% 2.26%Average Mean Absolute Deviation

Table 2: Cross-Validation Results

Predicted Share MADActual ShareBrand

30

(heterogeneity model). The MADs for the entrant are 3.68% (aggregate model) and

2.08% (heterogeneity model).

External Validity

We used IMS prescription data for the one-year period following the experiment

to measure the actual market shares of different brands in the marketplace. We

performed the external validation by comparing the predicted market shares, conditional

on the actual marketing strategies in the marketplace, with the actual market shares one

year after the experiment. This validation analysis was conducted in two ways: (a) using

the same sample of physicians and (b) using the total population of physicians in the

market.

At the end of the one-year period following the experiment, the leader had not

obtained FDA approval for the new dosage strength. Nor had the leader changed its

message or price strategies in the marketplace. Thus, the leader’s marketing mix was

unchanged from that at the time the experiment was conducted.

Using the leader’s marketing strategy as model input, we followed the same

forecasting approach as that used in the internal cross-validation analysis to predict each

physician’s choice probability vector for the seven brands. To allow for differences in

prescription volume across physicians, these probabilities were re-weighted by

physicians’ prescription rates to estimate volume-based market shares (see Equation

(15)). For the industry analyzed, brand awareness among physicians is almost universal

for all brands; furthermore, all brands have nationwide distribution. Hence it was not

necessary to adjust the results for awareness and distribution.

31

Both external validity analyses support the cross-validation findings. See Table 3.

The results suggest that the sample is representative. The leader’s in-sample market

share (29.82%) is close to the leader’s actual market share (32.97%). The corresponding

market shares for the entrant are 7.43% and 5.60%, respectively. The results also show

that the heterogeneity model predicts better than the aggregate model. Thus, the leader’s

predicted market share using the heterogeneity model is 30.16% whereas the actual

market share is somewhat higher (32.97%). The aggregate model predicts poorly

(predicted share = 39.83%). In contrast, both the aggregate and heterogeneity models

predict well for the entrant. The corresponding predicted market shares (4.97% and

5.13% respectively) are close to the actual market share (5.60%). However, as discussed

next (Section 6), the aggregate model provides biased estimates of the sources of the

market share gains for the entrant. Hence the aggregate model is managerially restrictive.

Aggregate Heterogeneity In-SampleMarket-Level

(Units)Leader 39.83% 30.16% 29.82% 32.97%Entrant 4.97% 5.13% 7.43% 5.60%

1 Actual market shares are calculated using IMS data.

Table 3Actual Versus Predicted Market Shares One Year After Study

Brand

Predicted Shares Actual Shares1

32

6. MANAGERIAL IMPLICATIONS

This section examines the sources of the entrant’s share gain and analyzes the

segments that are most likely to adopt the new brand. In addition, we discuss the

segment-specific message strategies that the market leader should use to defend its

competitive position in the marketplace.

Sources of Entrant’s Share

To determine the sources of the entrant’s market share, we compare the predicted

brand shares when the market includes and does not include the entrant. Our market

share predictions are based on the actual marketing strategies implemented by the leader

in the one-year period following the experiment: Because of time needed to obtain FDA

approval, the leader did not introduce the new dosage strength and did not change its

message strategy. Table 4 reports the entrant’s share gains from the six incumbent

brands both at the aggregate and the segment levels.

The aggregate model implies that the entrant’s expected market share is 4.97%.

Most of this share will come from physicians who switch from the leader’s brand to the

entrant (2.08%) and from Brand 2 to the entrant (1.35%). Interestingly, the market-level

Seg 1 Seg 2 Seg 3 Seg 4 Seg 5 OverallBrand 1 0.26% 0.02% 2.18% 0.31% 0.27% 0.04% 0.55%Brand 2 1.35% 0.56% 1.61% 0.77% 1.25% 0.79% 0.94%Leader 2.08% 0.73% 2.17% 0.89% 2.78% 0.90% 1.26%Brand 4 0.38% 0.06% 1.81% 0.53% 0.26% 0.06% 0.56%Brand 5 0.17% 0.02% 0.37% 1.91% 0.01% 0.01% 0.66%Brand 6 0.73% 0.51% 4.25% 0.51% 1.00% 0.06% 1.17%Entrant's Share 4.97% 1.89% 12.40% 4.93% 5.57% 1.86% 5.13%

Aggregate Model

Heterogeneity ModelBrand

Table 4Sources of Entrant's Share

33

prediction for the entrant’s market share using the heterogeneity model (5.13%) is similar

to that for the aggregate model (4.97%). However, the two models imply very different

sources of this gain in market share. Thus, the heterogeneity model implies that the

leader’s loss of market share to the entrant is 1.26%. The aggregate model implies that

the corresponding share loss is higher (2.08%). Recall that the therapeutic class is a

multibillion-dollar industry with high gross margins. Hence a 0.82% reduction in market

share leads to a considerable loss of profit.

The heterogeneity model shows that the entrant’s market share varies

considerably across segments. Thus, the entrant will be most successful in Segments 2

and 4 capturing, respectively, 12.40% and 5.57% share points in those segments. The

leader and Brands 1, 2, and 6 are particularly vulnerable in these segments. Thus, the

leader will lose 2.78 share points in Segment 4 and 2.17 share points in Segment 2.

Brands 6 and 1 are vulnerable in Segment 2 losing, respectively, 4.25% and 2.18% share

points. Brands 2 and 6 are vulnerable in Segment 4 losing 1.25% and 1.00%,

respectively.

Posterior Analysis

We performed a posterior analysis to determine which classification variables are

useful in determining segment membership. Two groups of variables were examined:

those that are available using secondary data (i.e., information on doctors’ specialties,

prescription volumes, mechanism of drug action, doctors’ loyalties to different brands in

the product category, and the number of years that the doctor has been in practice) and

those that are available using primary data such as those collected in the study (i.e.,

patient mix, type of insurance coverage, and co-payment plans). See Table 5 for the

34

variables used and their descriptions. Drugs in this therapeutic category belong to one of

two drug classes based on the mechanism of action, labeled A and B. Brands 1, 4 and 5

belong to drug class A. All other brands belong to drug class B.

For each segment, we performed a regression analysis using the appropriate log-

odds ratios (i.e.,

− is

is

ππ

1log ) as the dependent variables and the variables described above

(including interaction terms) as regressors. None of the interactions terms was

Variable Seg 1 Seg 2 Seg 3 Seg 4 Seg 5 Variable descriptionSPECTY -0.079 -0.194 0.227 -0.044 -0.139 Physician specialty (0/1 dummy)CLASS 0.4411 -0.145 -0.248 0.221 0.18 Number of brands from drug class B prescribedLOY1 0.032 0.166 -0.183 0.075 0.089 Physician's loyalty towards brand 1LOY2 0.172 0.067 -0.353 0.132 0.38 Physician's loyalty towards brand 2LOY3 0.135 0.105 -0.323 0.164 0.265 Physician's loyalty towards the leaderLOY4 0.083 2 0.093 -0.152 0.077 0.057 Physician's loyalty towards brand 4LOY6 0.233 0.091 -0.291 0.133 0.175 Physician's loyalty towards brand 6YRS1 -0.064 -0.011 0.052 -0.017 0.003 Physician has '10 - 15 years' ExperienceYRS2 -0.112 -0.033 0.097 -0.063 -0.007 Physician '16 - 20 years' ExperienceYRS3 -0.078 0.024 0.102 -0.115 -0.033 Physician has '21 or more years' ExperiencePT1 0 0.133 -0.085 0.044 0.013 % of patients under 17 years oldPT2 -0.049 0.161 -0.133 0.198 -0.1 % of patients 18-34 years oldPT3 -0.013 0.056 -0.076 0.058 0.034 % of patients 35-49 years oldPT4 -0.014 0.1 -0.027 0.029 -0.054 % of patients 50-65 years oldPT5 -0.081 0.095 -0.073 0.141 -0.055 % of patients 66-74 years oldHMO1 -0.048 -0.008 0.035 0.036 -0.062 26% - 50% HMO patientsHMO2 0.005 -0.048 0.047 -0.043 -0.014 51% or more HMO patientsNOCOV1 0.105 0.067 -0.051 -0.106 0.039 1% - 5% No coverage patientsNOCOV2 0.051 0.049 -0.074 -0.097 0.07 6% or more No coverage patientsMDCAID1 0.066 -0.042 0.008 -0.034 0.017 1% - 10% Medicaid patientsMDCAID2 0.104 -0.032 -0.024 0.047 0.024 11% or more Medicaid patientsCOPAY1 0.052 0.067 -0.048 -0.044 0.007 51% - 80% Co-pay patientsCOPAY2 -0.016 0.09 -0.054 -0.03 0.039 80% or more Co-pay patientsVOLUME 0.049 -0.013 -0.08 0.063 0.069 Physician total prescription volumeR-Square 27.33% 9.10% 17.71% 10.06% 13.90%SHARE 1.89% 12.40% 4.93% 5.57% 1.86% Entrant's predicted share3

Table 5: Posterior Analysis Results--Standardized Regression Coefficients

3 Entrant's predicted share assuming leader's current marketing mix policy.

1 Parameter estimates in bold are significant at p < 0.05 level.2 Parameter estimates in italics are significant at p < 0.1 level.

35

significant. Table 5 reports the standardized regression coefficients for the main-effects

model for each segment. All regression models are significant at p < 0.05.

Segment membership is significantly related to all the classificatory variables:

whether or not the physician is a specialist (coded 1 for specialist and 0 for generalist),

the drug class, the physician’s prescription volume, brand loyalty, and the number of

years the physician has been in practice.

Recall that the entrant is predicted to capture 12.40% market share in Segment 2.

Thus this segment contains a very high proportion of adopters of the entrant’s brand.

The posterior analysis shows that physicians in this segment tend to be non-specialists

and non-prescribers of the leader’s drug class B, are loyal to Brands 1 and 4, and cater to

younger patients with at least 80% co-pay. This result is good news for the leader

because most of the entrant’s share gain comes from outside the drug class; furthermore,

the entrant’s new brand will expand category sales.

The next most likely segment to adopt the entrant’s brand is Segment 4 (5.57%

share). This segment consists of relatively younger physicians who prescribe the leader’s

drug class B and who are loyal to Brands 2, 6 and the leader. The patients of these

physicians are more likely to be young and are covered by insurance. Note that the

leader’s share loss to the entrant is highest in this segment (see Table 4). Hence the

leader is extremely vulnerable in Segment 4. Similar analysis can be performed to

describe physicians in the other segments.

Optimizing Message Strategy

Recall that an important research goal for the leader was to determine the best set

of additional medical indications for which to seek FDA approval. To answer this

36

managerial question, we used the parameter estimates to measure the simulated share

impact of a change in message strategy. Specifically, we computed the leader’s market

share under each of the four message strategies (Detail Messages 0 to 3 inclusive)

assuming that the leader will introduce the new dosage strength. Table 6 reports the

leader’s expected market share changes for both the aggregate and the heterogeneity

models assuming that the leader introduces a new message strategy (i.e., Messages 1

through 3 inclusive).

The aggregate model predicts that the market leader will gain 12% market share

by using Message 1. See Table 6. Because market share is a good proxy for profitability

in this industry, the aggregate model implies that the optimal policy for the leader is to

use Message 1 across the market. In contrast, the heterogeneity model shows that the

leader’s message strategy has considerable differential effects on its market share across

segments. Consequently, the leader should use a customized message strategy across

segments. Specifically, Message 1 is best for Segments 1,2, and 4 and Message 3 is best

for Segments 3 and 5. By using this customized segment-specific marketing plan

instead of a uniform marketing strategy across segments, the firm will gain an additional

1.6% (i.e., 9.8% - 8.2%) share points and significantly increase its profits.

Seg 1 Seg 2 Seg 3 Seg 4 Seg 5 TotalDetail Message 1 12.0% 2% 15% 14% 9% 0% 8.2%Detail Message 2 5.0% -1% 11% 11% -2% 2% 5.6%Detail Message 3 1.0% 1% -6% 16% 8% 4% 5.5%Details 1 & 3 2% 15% 16% 9% 4% 9.8%

Table 6: Simulated Share Changes from Current Detail for Market Leader

DetailAggregate Model

Heteregeneity Model

37

The aggregate and heterogeneity models provide considerably different

predictions at the market level (see Table 6). Furthermore, the directions and magnitudes

of the biases by using the aggregate model vary across marketing policies. Thus, the

aggregate model predicts that Message 1 will increase the leader’s market share by 12%.

The heterogeneity model implies that the gain is much smaller (8.2%). The aggregate

model implies that Message 3 will increase the leader’s market share by 1%. However,

the heterogeneity model implies that the leader’s share gain is considerably higher

(5.5%).

7. SUMMARY AND CONCLUSIONS

This paper develops and tests a new multibrand intentions model for concept

testing and new product forecasting. Our methodology addresses several problems in the

literature. In contrast to single-brand intentions measures, it explicitly filters

measurement error (an endemic problem with self-stated intentions measures). Thus, the

method provides unbiased parameter estimates. Importantly, the methodology allows for

differential and unequal biases in self-stated intentions scores across brands and

segments. For example, the intention measures for a well-known brand may be biased

upward and some segments may be more uncertain about their brand purchase behavior

than others. The method is well suited for analyzing new products because it can be used

even if heterogeneity is unobservable. Thus, the firm can determine the segment-level

effects of a new product concept on its own product line (i.e., cannibalization effects) and

competitive brands. In particular, the method can be used to formulate strategy because

it allows the firm to estimate the differential segment-level impacts of competitive

retaliation following the introduction of the new product concept in the marketplace.

38

We tested the model using data from a major multibillion-dollar therapeutic class

in the pharmaceutical industry. Approximately four years ago, a new entrant had just

announced its imminent entry into the marketplace. Soon thereafter, the market leader

conducted a large-scale experimental study to measure how the new brand would affect

the industry. A major focus of the study was to determine how the leader should revise its

marketing mix in view of the new product announcement.

We checked internal validity using the cross-validation method and assessed

external validity by comparing model predictions with actual market behavior one year

after the experiment was conducted. The results suggest that the methodology is robust.

Both the internal and external validity checks show that the heterogeneity model has

good predictive accuracy; the aggregate model does not. For example, the external

validity checks for the leader’s brand show that the model predicted reasonably well for

the heterogeneity model (actual market share= 32.97%, predicted share = 30.16%). In

contrast, the aggregate model significantly overpredicted the leader’s market share

(39.83%).

The adoption rates for the new entrant vary considerably across segments. The

most likely adopters are Segments 2 and 4. In particular, the entrant’s expected market

shares are 12.40% (Segment 2) and 5.57% (Segment 4). The entrant gains most of its

market share from Brand 6 in Segment 2 (4.25%) and from the leader in Segment 4

(2.78%). Over all, the market-level prediction for the entrant’s market share using the

heterogeneity model (5.13%) is similar to that for the aggregate model (4.97%).

However, the two models imply very different sources of gain in market share. Thus, the

heterogeneity model implies that the leader’s loss of market share to the entrant is 1.26%.

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The aggregate model implies that the corresponding share loss is higher (2.08%). This

difference in predictions (0.84 share points) is considerable for the multibillion-dollar

pharmaceutical market analyzed.

The posterior analysis shows that the classification variables analyzed (e.g., the

drug class, the physician’s prescription volume, and brand loyalty) are useful in

predicting segment membership. The profiles of adopters and the competitive structure

also vary significantly across segments. For example, physicians in Segment 2 tend to be

non-specialists and non-prescribers of the leader’s drug class B, are loyal to Brands 1 and

4, and cater to younger patients with at least 80% co-pay. This result is good news for

the leader because most of the entrant’s share gain comes from outside the drug class;

furthermore, the entrant’s new brand will expand category sales. In contrast, Segment 4

consists of relatively younger physicians who prescribe the leader’s drug class and who

are loyal to Brands 2, 6, and the leader. The leader is extremely vulnerable in this

segment since its share loss to the entrant is highest.

The results also show that the leader’s marketing mix has considerable differential

effects across segments. Consequently, the leader should use a customized message

strategy across segments. Specifically, Message 1 is best for Segments 1, 2, and 4 and

Message 3 is best for Segments 3 and 5. By using this customized segment-specific

marketing plan instead of a uniform marketing strategy across segments, the firm will

gain an additional 1.6% share points and significantly increase its profits.

Finally, our study analyzed the effects of introducing a new product concept in an

established product category. Future research should further examine model robustness.

Thus, the methodology should be applied to a wide range of product categories including

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durables, consumer packaged goods, and industrial products. Furthermore, future

empirical studies should examine product categories that are new to the marketplace.

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