- Email: [email protected]
Product design strategies for target-market positioning
J PROD INNOV MANAG 1991;8:189-202
189
0000
Product Design Strategies for Target-Market Positioning Paul E. Green and Abba M. Krieger
Designing optimal positioning strategies for target segments has become an area of intense research interest over the past few years. This article examines a variety of strategies that can be operationalized from conjoint analysis input data. Paul Green and Abba Krieger discuss strategies for modifying buyer perceptions, ideal-level preferences and attribute importances that are attractive for a firm's existing brand(s). They then consider longer run strategies for modifying the current brand's attribute levels and develop a case to illustrate applications of the techniques.
Address correspondence to Paul E. Green, S.S. Kresge Professor of Marketing, Marketing Department, Suite 1400, The Wharton School, University of Pennsylvania, Steinberg-Dietrich Hall, Philadelphia, PA 19104-6371. © 1991 Elsevier Science Publishing Co., Inc. 655 Avenue of the Americas, New York, NY 10010
Introduction Few marketers would dispute the central roles that product positioning and market segmentation play in today's economies, national or global. The last decade has witnessed a surge of new methodological and modeling developments for optimal positioning. Reviews of these developments are provided by Green, Carroll and Goldberg [2], Sen [9], Sudharshan, May and Shocker [12], Kohli and Krishnamurti [7], Sudharshan, May and Gruca [11], and Green and Krieger [3]. Conjoint analysis [4] has been central in the implementation of many optimal positioning models. The advent of user-friendly personal computer packages, such as those of Herman [5], Johnson [6], and, most recently, SPSS [10], has served both to define the state of conjoint practice and to make the methodology accessible to wider audiences. Helpful as they are, however, commercial conjoint packages focus largely on data collection and part-worth estimation. While all three of the packages cited above contain buyer choice simulators, none deals with product optimization and related issues. The purpose of the current article is to discuss various extensions of current product positioning practice. As in the case with commercial packages, the methodology described here collects conjoint preferences and respondent background data. Attribute-level perceptions data for each respondent's most preferred brand are also collected. (Perceptions data collection represents a departure from the commercial packages' input 0737-6782/91/$3.50
190
J PROD INNOV MANAG 1991 ;8:189-202
BIOGRAPHICAL SKETCHES Paul E. Green is the S.S. Kresge professor of marketing at the Wharton School, University of Pennsylvania. Dr. Green's research emphasizes quantitative methods and new measurement techniques in market analysis and consumer research. He has been honored for this research by the American Marketing Association, the American Statistical Association, the American Psychological Association, the Market Research Society, and the Association for Consumer Research. Dr. Green is the author or coauthor of several books, including the widely used text Research for Marketing Decisions, now in its fifth edition. He is also a prolific contributor to marketing and business journals. Abba M. Krieger is professor of statistics and operations research at the Wharton School, University of Pennsylvania. He is the author or coauthor of articles in statistical methodology and the interface between statistical methodology and optimization theory. His current research interests include theoretical and empirical analyses of the bootstrap resampling technique and application of statistical methods and operations research to problems in marketing research.
requirements; otherwise, the models we describe use the same kinds of data that are routinely collected in industry-based conjoint studies.) Our approach deemphasizes the methodology's mathematics in favor of a more managerial slant that focuses on strategic questions. We use a case format that considers a hypothetical firm, the Epsilon Company, that is in the consumer credit card business. While all attribute level descriptions, cost data, product profiles, and background variables are disguised, the case nonetheless has been designed to be realistic in terms of the kinds of strategic research questions that are raised in this industry. Study B a c k g r o u n d As described in Fortune magazine [8], the credit card industry is feeling the heat of new competitors, most notably Sears' Discover Card and, even more recently, AT&T's Universal Card. Some major players, such as Citibank, are also raising the ante by buying out credit card portfolios of smaller financial institutions; it is believed that Citibank paid the equivalent of $268 per account (one of the highest prices ever), for its purchase of Bank of New England's card portfolio. Credit card companies are also adding new consumer benefits (e.g., retail purchase insurance), cashback bonuses (e.g., the Discover Card) and other features in the battle for market
P . E . GREEN AND A. M. KRIEGER
share. With new customers getting ever harder to corral, attempts are being made to extend card usage to such establishments as fast foods, movies, and even toll booths. In the case described here, Epsilon is a relatively small player in a group of competitors that include Alpha, Beta, Gamma, and Delta. Alpha and Gamma are the giants in the field (with market shares of 28% and 45%, respectively). EpsiIon's share is only 10%. Short Run Strategies Epsilon's management is concerned with both short run and longer run competitive strategies. In terms of short run strategy, the following questions are relevant: 1. Are the characteristics of Epsilon's card profile being misperceived by its current users? If so, how can it improve its position by focusing on those particular card attributes where the correction of misperceptions is most advantageous? 2. Is it possible to change current card users' preferences for various profile levels that favor Epsilon's offering? If so, what gains might be associated with moving all card users' ideal levels closer to the specific levels provided by Epsilon's card? 3. What are the current strengths and weaknesses of Epsilon's card? Which attribute saliences (i.e., perceived attribute importances) should be stressed in order to increase the share/profits of its current product? It should be noted that all three sets of questions involve actions that could be taken with no change in the current card's actual attribute levels. Rather the intent (through advertising) is to modify consumers' perceptions, ideal levels, or attribute saliences in ways that are differentially favorable to Epsilon. Of course, the consequences of the actions (particularly changing card holders' ideal attribute levels) may be very long in coming, if at all. Longer-Run Strategies In contrast, changes in Epsilon's product offering, including features, user benefits, and price are typically longer term; even changing the card's price (annual billing) takes time. In terms
PRODUCT DESIGN STRATEGIES FOR POSITIONING
of the longer run, the following questions are relevant: 1. Assuming that the current Epsilon card's attribute levels and price could be changed, what is the optimal bundle of features/benefits/price to be offered, conditional on competitors' current profiles? 2. Assuming that Epsilon could introduce a line extension (a new card in addition to its current card), what should the new card's profile be? 3. Next, assume that Epsilon does, in fact, introduce the r e c o m m e n d e d line extension. Beta observes this event and decides to retaliate by replacing its current card with a new profile. What should Beta's new card be? Note that the preceding strategies make various assumptions about competitive behavior. In the first two questions we do not take up the issue of competitive retaliation but do consider Epsilon's competitive draw and cannibalization. In the third question, however, we consider the case in which Beta responds to the initial line extension m o v e of Epsilon.
Target Marketing Up to this point we have been assuming that Epsilon's product positioning strategy is based on total market considerations. We now focus on product design for selected target segments. The following questions are appropriate: 1. We first assume that Epsilon m a n a g e m e n t wishes to consider a line extension for a preselected target segment. What should this card's profile be? 2. We next assume that Epsilon wishes to choose two target segments and replace its current card with two new cards, one for each segment. What should their profiles be? The preceding product positioning and target marketing actions are only illustrative of the possibilities for which the proposed methodology is designed. T h e y should be sufficient, however, for illustrating the versatility of the approach.
The Survey Epsilon researchers next designed and implemented a national survey of credit card holders,
J PROD INNOV M A N A G 1991 ;8:189-202
191
using a telephone-mail-telephone interviewing procedure [4]. A total of 480 respondents, all of w h o m used at least 1 of the 5 credit cards, completed the interviews. The interview employed a hybrid conjoint procedure [1] and consisted of 12 attributes (Table 1). In addition to the conjoint data, information was also obtained on respondents' perceptions of the attribute levels of their most preferred card; this was done by simply asking them to pick the attribute levels (Table 1) that most closely matched their perceptions of the features of their most preferred card. R e s p o n d e n t background information on card usage, travel, and selected demographics was also obtained. For background purposes, Figure 1 shows part-worth summaries for each of the 12 attributes. As might be expected, the services are such that the more enhanced levels are preferred to the more basic levels that, in turn, are preferred to no services at all.
Cost Data Obtaining Epsilon's cost data at the attribute level was a m u c h more difficult job. Our interest was restricted to variable costs only; even at that, crude estimates had to suffice. Each of the " n u l l " or base levels (lowest part-worth levels) was coded at zero cost, and costs of enhanced levels were expressed as (negative) departures from this base. Annual price was translated into annual gross revenue for the base profile; costs for enhancements were deducted from their revenue estimates. The results of these estimation procedures provided Epsilon researchers with an annual contribution to overhead and profit, estimated on a percard level. (The contribution includes estimated interest charges.)
Current Competitive Profiles Based on published data, each competitor (Alpha through Epsilon) was profiled in terms of its " t r u e " attribute levels. These profiles are shown in Table 2, along with industry-based current market shares for each of the five cards. We note that Alpha's offering is a " p r e s t i g e " c a r d - - h i g h e s t priced of all competitors, with enhanced levels of most service attributes and with no end-of-year cash rebate. Beta's offering em-
192
J PROD 1NNOV M A N A G 1991 ;8:189-202
P . E . G R E E N AND A. M. K R I E G E R
Table 1. Card Attribute Levels Used in Conjont Survey Annual price
$0
$10
$20
$50
$80
$100
Cash rebate (end-of-year, on total purchases) none
1/2%
1%
800 number for message forwarding none
9-5pm weekdays
24 hours per day
Retail purchase insurance none
90 days coverage
Common carrier insurance (death, injury) none $50,000 $200,000 Rental car insurance (fire, theft, collision, vandalism) none $30,000 Baggage insurance (covers both carry-on and checked) none $2,500 depreciated cost $2,500 replacement cost Airport club admission (based on small entrance fee) no admission $5/visit $2/visit Card acceptance air, hotel, rental cars; AHC and most restaurants; AHCR and most general retailers (AHCRG); AHCR and department stores only (AHCRD) Twenty-four hour medical/legal referral network no yes Airport limousine to city destination not offered available at 20% discount 800 number for emergency car service not offered available at 20% discount
Figure 1. Average part-worth values from hybrid model
Scale Values
0.50.4-
$~""~ *'o
0.3-
~
0.2-
0.1-
~S0
J1% None 12%
,~..~ ~ - ,
, , , Price
Rebate
y
~4hr~ Nolle 9-5prn
~ i '~, Message Forwarding
,$~k~200k ays No~ne l
Purchase Insurance
I
Carrier Insurance
i
2 1
I Car Insurance
0.50.4-
Ye~
0.3-
AHCRG
~"~CRD
0.2-
/el
/,
0.1
, Baggage Insurance
Airport Clubs
, Acceptance
Medical/ Legal
Yu
Limousine
/,
Emergency Car Service
PRODUCT DESIGN STRATEGIES FOR POSITIONING
J PROD INNOV MANAG 1991 ;8:189-202
193
Table 2. Card Profiles of Five Competitors
Attribute Price Cash rebate Message forwarding Retail purchase insurance Common carrier insurance Rental car insurance Baggage insurance Airport clubs Acceptance Medical/legal Airport Limousine Emergency car serv. Current mkt. share
Alpha
Beta
Gamma
Delta
Epsilon
$80 none 9-5 weekdays 90 days $200,000 $30,000 $2,500 rep. none AHCRG yes yes 20% disc. yes 20% disc. 28%
$20 none
$20 none 24 hours daily none $50,000 none $2,500 dep. $2/visit AHCRD yes yes 20% disc. yes 20% disc. 45%
$0 0.5%
$10 none 9-5 weekdays 90 days none none
none 90 days $200,000 $30,000 $2,500 rep. none AHCRD no none none 8%
phasizes insurance c o v e r a g e (where it is head-tohead with Alpha but is m o r e m o d e s t l y priced). G a m m a ' s offering is priced the same as Beta's but its profile emphasizes c o n s u m e r a m e n i t i e s - message forwarding, airport clubs, medical/legal, airport limousine, and e m e r g e n c y car service. Delta is a " b a r g a i n - b a s e m e n t " e n t r y - - n o annual costs plus a small rebate on cumulative annual purchases. It offers v e r y few services and has s o m e w h a t less limited a c c e p t a n c e than the others. Epsilon's price is less than Alpha's, Beta's and G a m m a ' s but it offers both limited amenities and limited insurance services. As n o t e d in the last row o f Table 2, the bigger players are Alpha and G a m m a ; Beta's, Delta's and Epsilon's shares are roughly equal to each other and c o n s i d e r a b l y below those of the big two.
Buyer Choice Rule The individual c o u n t e r p a r t s to the averaged partworths o f Figure 1, along with the profile descriptions o f Table 2, are typically e n t e r e d into a conjoint choice simulator. The r e s e a r c h e r is then faced with the p r o b l e m o f what choice rule to use. T h e r e are three choice rules currently in use in c o m m e r c i a l conjoint packages: (1) the max utility choice rule; (2) the logit choice rule; and (3) the B r a d l e y - T e r r y - L u c e (share o f utility) rule.
none 90 days none none none none AHCR no yes 20% disc. yes 20% disc. 9%
none $5/visit AHCRD yes none none 10%
E a c h o f these rules can be mimicked by what we call the alpha rule. S o m e w h a t m o r e formally, assume that any specified supplier's profile can be r e p r e s e n t e d by a v e c t o r js; s -- 1, 2 . . . . . S. Given individual k's v e c t o r o f part-worths, we can c o m p u t e Uk.s = Uk(js)
(1)
as the utility of individual k for supplier s. The " m a r k e t s h a r e " (or probability o f k choosing supplier s) is ~'k,~ =
U~ (Js) s E U~ (Js)
(2)
S=I
w h e r e the e x p o n e n t a is c h o s e n to be non-negative. As alpha a p p r o a c h e s infinity the preceding choice rule mimics the m a x utility rule. Smaller values o f alpha can mimic a logit rule. If alpha is set to 1.0, we have the B r a d l e y - T e r r y - L u c e rule. A c o m p u t e r program, called A L P H , has been designed to find the value of alpha that, if applied to e a c h individual's utilities, obtains the closest overall fit to actual m a r k e t shares for each supplier. Exhibit 1 provides a brief description o f the program. W h e n applied to the current survey data, the best fitting value o f alpha was 10.4. This is the value used in all s u b s e q u e n t analyses.
194
J PROD INNOV MANAG 1991 ;8:189-202
P.E. GREEN AND A. M. KRIEGER
Exhibit 1. Characteristics of Computer Programs Used in Case Study Program
Input
What the program does
Output
ALPH
• Each individual's utilities for the profile of each supplier • Actual market share for each supplier • Individual's importance weight file
• Solves for optimal value of alpha which, if applied at the individual level, best reproduces actual market shares • Solves for implied predictions of user-supplied alpha values • Uses four alternative algorithms in solving for optimal alpha value
• Optimal value of alpha • Predictions of shares for user-supplier alpha values
PERCEPT
• E a c h individual's current brand identification • Individual's importance weight file • Individual's part-worths file • Individual's demograhics file • Individual's perception vector for current brand
• Finds s u m m a r y , by brands, of incidence of each perceived attribute level and associated average part-worth • C o m p u t e s sensitivity of average part-worths as perceived levels become increasingly similar to targeted levels • Can analyze data for the overall profile, subsets of attributes, or individual attribute perception changes • Analysis can be performed at total-market or by selected demographic level
• Size of demograhic s e g m e n t • Relative incidence of brands within s e g m e n t • Associated brand utility changes as perceptions approach targeted levels • Sensitivity analysis by individual attributes • S u m m a r y tables of misperceptions, by attribute and supplier
IDEAL
• Individual part-worths file • Targeted levels for the ideal profile • Interpolating constant: probability of changing current ideal to targeted ideal
• If r e s p o n d e n t ' s ideal level already agrees with target, no change is made • If current ideal does not equal target, targeted level is increased proportionately toward current ideal, based on user-supplied interpolation value
• N e w matrix of individual partworths • This n e w file is then p r o c e s s e d by S I M O P T for c o m p a r i s o n with original partworths file
SALIENCE
• Individual attribute importances and attribute-level desirabilities • Individual importance weight file • Demographic (background) file • Current market shares of all suppliers • E a c h supplier's profile • Value of alpha and demographic attribute weights • Attribute level cost/return data
• Program shows, attribute by attribute, how share/return for a selected supplier changes as greater importance is associated with the attribute • Program c o m p u t e s optimal distribution of effort and new set of importance weights that maximize selected supplier's share/return • Analysis can be done at the overall market or selected demographic level • Program also allows u s e r to read in efforts and obtain their implied returns
• N e w set of attribute importances • Predicted share/return if n e w importances are u s e d • N e w part-worth matrix with modified importance weights for input to S I M O P T or conventional simulators
PRODUCT DESIGN STRATEGIES FOR POSITIONING
Exhibit 1.
J PROD INNOV MANAG 1991 ;8:189-202
195
(continued)
Program
Input
What the program does
Output
SIMOPT
• Individual part-worths file • Individual's importance weight file • Demographics (background) file • Current market shares for all suppliers • Each supplier's profile • Value of alpha and demographic attribute weights • Control parameters for optimization • Attribute-level cost/return data
• For any set of competitive profiles, the program computes share/return for each supplier, • All shares/returns are automatically adjusted to base-case conditions • Sensitivity analyses can be performed at the individual attribute level • Optimization can be carried out by supplier or for groups of suppliers; attribute levels can be fixed for conditional optimization • Analyses can be conducted at the total market or selected target segment level
• Market share/return for each supplier • Individual supplier selection file • Optimal product description for total market or selected segment • Sensitivity analysis results, by level within attribute
SEGUE
• Individual part-worths file • Individual's importance weight file • Demographics (background) file • Segment attribute weights
• For any target segment composable from the background variables (with weights supplied by the user), the program computes size of segment, ideal levels, attribute importances, and attribute level desirabilities • Both additive and conjunctive segments can be created • The user can also input any trial product profile and find its total utility compared to the best profile • A respondent weights file is prepared for later use in SIMOPT
• Attribute importance, level desirabilities, and ideal levels, by selected segment • Profile utilities by selected segment • Respondent weights file summarizing each individual's relevance to the target segment (input to SIMOPT)
Short Range Strategies
3. Change consumers' perceived attribute importances in ways that increase the relative attractiveness of the firm's product.
Changing actual product attribute levels may take some time to accomplish and is associated with both technical and market uncertainty regarding successful implementation. In the short run the firm may elect to maintain its current product and attempt to:
Modifying Consumers' Perceptions
l. Correct consumers' misperceptions of its attribute levels. 2. Modify consumers' attribute level preferences in ways favorable to the product.
The PERCEPT program (Exhibit 1) has been designed to examine consumers' perceptions of product attribute levels for their current (or most preferred) brand and to find those attributes that
We discuss each of these actions, in turn.
196
J PROD INNOV MANAG 1991;8:189-202
P.E. GREEN AND A. M. KRIEGER
Table 3. Summary of Probabilities of Correct Perceptions of Alpha, Gamma and Epsilon
Alpha Gamma Epsilon Mean
1
2
3
4
5
.58 .84 .93 .78
.76 .64 .76 .72
.53 .63 .66 .61
.75 .70 .87 .77
.82 .60 .73 .72
Attributes 6 7 .73 .67 .95 .78
.90 .82 .90 .87
8
9
10
11
12
Mean
.50 .73 .90 .71
,73 .53 .80 .69
.57 .67 .78 .67
.69 .66 .88 ,74
.92 .91 .80 .88
.71 .70 .83
Comparative Average Utility gains for Correcting Misperceptions: Alpha Gamma Epsilon
9.8% 10.8 7.8
Best Attributes for Epsilon Attributes 9 (card acceptance) and 10 (medical/legal network)
offer the greatest gains in utility for moving perceptions to the " c o r r e c t " levels of the firm's brand. Input data consist of each respondent's multiple choice data representing the level within each attribute that is perceived to match most closely the consumer's perception of his/her current brand. PERCEPT plays two roles. First, it provides summary descriptions, by brand, of consumer perceptions versus "reality" levels. In this way, one can compare brand efficiencies with regard to how one's advertising messages are getting across. Alternatively, one can compare attributes (across brands) in terms of the accuracy with which their levels are perceived. Second, PERCEPT can be used to examine the differential attractiveness of correcting consumer misperceptions, so that the firm can focus on those attribute levels that carry the highest payoff in consumer utility (and, other things being equal, brand choice). For illustrative purposes, we select three of the five suppliers: Alpha, Gamma and Epsilon. Table 3 summarizes the results. We first note that, on average, Epsilon users show the highest incidence of accurate perceptions; 83% of the time they correctly perceive the levels of Epsilon's true profile. This compares to 71% for Alpha and 70% for Gamma. In all three cases, however, the perceived modal profiles corresponded to the true profiles of each supplier. We also note from Table 3 that, on average, attribute 12 (emergency card service) and attribute 7 (baggage insurance) were most often perceived correctly, while attribute 3 (message forwarding) was least often perceived correctly.
In addition, Table 3 indicates that the achievement of perfect agreement between true and perceived attribute levels would increase average utilities by only 8-11%. We also note that the two most attractive attributes for Epsilon to work on are card acceptance and medical/legal network. These two attributes alone count for almost all of Epsilon's potential utility increase of 7.8%. All in all, we conclude that perceptions are reasonably accurate, particularly for Epsilon. Moreover, the gain in average supplier utility for correcting misperceptions is relatively small (8-11%). Finally, if Epsilon's advertising emphasizes its breadth of acceptance and availability of medical/legal services, utility gains could be forthcoming in the future. Finally, it is worth noting that none of the commercial conjoint packages deals with the analysis of perceptions data. Their simulators all assume that each consumer correctly perceives each supplier's profile, as defined by the program's user.
Changing Ideal Attribute Levels In the same spirit we can examine the sensitivity of Epsilon's market share/return if modifications (through advertising, packaging, etc.) could be made to consumers' ideal (most preferred) attribute levels. For example, suppose a consumer currently does not care for either retail purchase insurance or medical/legal referral services. Suppose Epsilon could induce consumers to modify their current ideal levels (obtained from their part-worths) to agree with its card's current levels. What would be the impact of this change?
PRODUCTDESIGNSTRATEGIESFOR POSITIONING
J PRODINNOVMANAG 1991;8:189-202
I 20
0
i 60
I 40
~
Alpha
•
Gamma
~
Alpha
Gamma
~
80
I I~"
197
Base case conditions
100
IEpsll°n
IDEAL with P=0.5
t~"
IDEAL with P = 0.15
I
Alpha
III
Gamma
~
I Figure 2. Stacked bar chart showing market share changes related to various strategies (total market)
Gamma
Alpha
IV
~"
(~*
Replacement for denoted ~ *
~
~**
Addition to ~ , denoted (~**
(~,
t
/~
Alpha
Gamma
~"
I
Alpha
VI 0
i /~* 20
More realistically, suppose that one could move consumers' current ideal levels to a set of prespecified target levels with a 50% probability. What would be the result on Epsilon's share/return? The I D E A L program (Exhibit 1) implements this objective. This program solves for a new matrix of part-worths for the total sample of respondents (not just Epsilon preferred users) that reflects a user-supplied probability of movement from current to targeted ideal levels. Illustratively, we first choose a probability of 0.5 of moving each respondent's vector of ideal attribute levels from their current values to those of the targeted Epsilon profile (Table 2). Having done this, we use a computer choice simulator (with an alpha choice rule of a = 10.4) to find a new set of market shares. Figure 2 shows the results. The first stacked bar chart of Figure 2 shows the initial market shares (Table 2). The second stacked bar chart shows the effect on shares obtained from application of the I D E A L program, with P = 0.5. As noted, Epsilon's share increases
I
!
Gamma 410
~" 60
~
(~
**
80
Replacement for ~, denoted~*
100
from 10% to 31%. Of course, a probability of 0.5 is still unrealistic. A more conservative value of P = 0.15 was tried next. The third bar chart shows an increase in Epsilon's share of 14% (versus the base case share of 10%). This is much more realistic. In a similar manner, other values of P could be tried to get some feel for the sensitivity of Epsilon's share to this type of part-worth change.
Changing Attribute Importances The third avenue for redirecting Epsilon advertising is changing consumers' evaluations of attribute importances. The optimization procedure employed in this strategy is considerabl); more complex. To carry out this strategy we apply an algorithm called S A L I E N C E (Exhibit 1). The principal inputs to S A L I E N C E consist of attribute desirabilities, attribute importances (both obtained from a hybrid conjoint model), competitive profiles (shown in Table 2), and the alpha exponent (10.4, in this example). SALI-
198
J PROD INNOV MANAG 1991:8:189-202
E N C E contains a non-linear optimization routine that finds a new set of importance weights for maximizing Epsilon's share/return. In this example we first use S A L I E N C E to identify those attributes where Epsilon has a differential advantage over one or more competit o r s - t h a t is, where an increase in the salience of the attribute produces a higher return for Epsilon, other things remaining equal. Only five attributes: • Price • Card acceptance • Message forwarding • Medical/legal referral network • Retail purchase insurance met this condition. Next, we applied the optimization routine that finds the best allocation of new advertising effort across the five candidate attributes. The algorithm indicated that new (incremental) advertising effort should be allocated across only two of the five attributes: price and the medical/legal referral system. Under the current situation respondents' judged importances of the attributes, price and medical/legal, were 17.3% and 9.6% out of a total of 100%. Under the optimized allocation of new effort these importances should expand to 40.0% and 20.8%, respectively, out of a total of 100%. These two attributes constitute its principal "leverage" attributes. Some Caveats
All three of the preceding algorithms, PERCEPT, IDEAL, and S A L I E N C E are essentially sensitivity analysis approaches. They do not tell us how we should go about implementing the desired changes, how likely we will be able to accomplish the task, and what the effort will cost. Rather, they forecast the stakes (or payoffs) if the task can be accomplished. If the payoff is relatively small (as was the case for PERCEPT), the manager may elect to disregard the strategy. On the other hand, a large potential payoff could justify further study of the means and associated costs of
P . E . G R E E N AND A. M. KRIEGER
pursuing the strategic objective. Of course, further study would also consider additional factors related to payoff estimation and cost outlays.
Longer Range Strategies We now turn to longer range strategies available to Epsilon. Illustratively, we first consider various strategies in the context of the total market. Then we describe counterpart strategies at the target market level. Optimal Product Design
The SIMOPT program (Exhibit 1) is the algorithm that we use for finding optimal product profiles in the context of competitive offerings. SIMOPT [3] is a conjoint-based algorithm that solves for an attribute-level configuration that optimizes share/ return for a specified supplier. It uses the alpha choice rule (set at 10.4 in this example) and a divide-and-conquer heuristic that breaks down the overall optimization task into subset problems that are then solved iteratively. Like most heuristics, however, optimization is not guaranteed. Fortunately, if the number of possible attribute-level combination is less than a million, SIMOPT utilizes a complete enumeration routine. (In the sample problem there only 186,624 combinations.) In the present problem we first solve for the " b e s t " (in the sense of maximizing contribution to overhead and profits) product profile for EpsiIon, given the specified profiles (Table 2) of its competitors. Initial shares for all five competitors appear in the bar chart of panel I of Figure 2. Epsilon's current share is 10%. Panel IV of Figure 2 shows that the best replacement (denoted e*) for Epsilon's current product results in an increase in Epsilon's share to 22%. Its associated contribution to overhead/ profit increases 82% over that of the base case.l Table 4 shows the changes that take place in optimizing Epsilon's contribution. Note that Epsilon moves to a high price, along with a cash rebate, increased insurance coverage, inexpensive access to airport clubs, and emergency car service.
t F o r r e a s o n s o f c o r p o r a t e c o n f i d e n t i a l i t y , w e a r e u n a b l e to report actual profit contributions.
PRODUCT DESIGN STRATEGIES FOR POSITIONING
J PROD INNOV M A N A G 1991:8:189-202
199
Table 4. New Card Profiles Associated with Alternative Strategies
Current epsilon Price Cash Rebate Message Forwarding Retail Purchase Insurance Common Carrier Insurance Rental Car Insurance Baggage Insurance Airport Clubs Acceptance Medical/legal Airport Limousine Emergency Car Service
$10 none 9-5 weekdays 90 days none none none $5/visit AHCRD yes none none
In all, half of the original 12 attribute levels are changed. What happens if we keep the present Epsilon card and add a line extension (denoted e**). SIMOPT solves for the optimal extension and, somewhat surprisingly, it turns out to be the same profile as e*. Panel V of Figure 2 shows that the combined share o f e and e** is 8% + 21% = 29%. Thus, the loss due to cannibalism is only two percentage points. Most of the business picked up by e** is from competitive products. Moreover, the overhead/profit contribution for the combined offerings (e and e**) is 38% higher than the contribution of e* as a replacement for e. Hence, assuming that the costs of adding the line extension are not excessive it is better in this instance to line extend than to replace e. Suppose Epsilon does, in fact, add the new product e** to its line. The Beta Company observes this event and notes (panel V in Figure 2) that its share decreases slightly from 8% to 7%. Beta considers the prospect of replacing its current card with a new profile--one that optimizes its contribution, conditional on the changes that Epsilon has already made. Panel VI shows that Beta's best retaliatory move increases its share from 7% to 18%. EpsiIon's combined share decreases by only two percentage points. Most of Beta's new business comes at the expense of Gamma. Table 4 shows that Beta's replacement profile involves eight attribute changes. (Illustratively, this optimization was carried out, conditional on Beta's price and cash rebate policy remaining unchanged.)
Replaced or extended epsilon $100 1% 9-5 weekdays 90 days $200,000 none $2,500 dep $2/visit AHCRD yes none yes (20% disc)
Current beta
Replacement beta
$20 none none 90 days $200,000 $30,000 $2,500 rep none AHCRD no none
$20 none 9-5 weekdays none $200,000 none $2,500 dep $2/visit AHC yes none yes (20% disc)
none
Some Caveats Although SIMOPT is a very flexible algorithm for finding optimal product positionings, it should be borne in mind that the mechanism underlying the model does not consider the gestation period over which share changes take place. SIMOPT also takes into consideration existing products' shares and an opportunity to optimize, conditional on some attribute levels remaining fixed; however, it does not explicitly deal with the costs of product proliferation and the possible dynamic actions that competitors might take even before new products are introduced. Thus, in practice, forecast conditions might never be reached, due to a variety of market factors not under the firm's control. Finally, the SIMOPT model assumes that consumers' part-worths remain stable over the planning horizon, that all relevant attributes appear in the model, that costs are accurately measured at the attribute level, and that each firm enjoys a rough parity with respect to advertising and distribution levels. Clearly, these are important assumptions; their recognition underscores the need to consider SIMOPT (and our approach in general) as a set of planning models whose outputs need to be checked against other independent information sources and management judgment.
Target Market Product Design Up to this point all of our analytical excursions have entailed the total market. We now consider
200
J PROD INNOV MANAG 1991:8:18%202
P . E . GREEN AND A. M. K R I E G E R
Table 5. New Card Profiles Associated with Target-Market Product Positioning
Price Cash Rebate Message Forwarding Retail Purchase Insurance Common Carrier Insurance Rental Car Insurance Baggage Insurance Airport Clubs Acceptance Medical/legal Airport Limousine Emergency Car Service
Current epsilon
Up-scale market
$10 none 9-5 weekdays 90 days none none none $5/visit AHCRD yes
$100 none 24 hours/day 90 days $200,000 $30,000 $2,500 rep $2/visit AHCRG yes yes (20% disc) yes (20% disc)
none none
various strategies that can be directed towards specific target segments, selected by the researcher. Survey data were available for several background attributes: • Income level (moderate, higher, highest) • Psychographic clusters (four groups) • Travel frequencies (low, high) • Gender (male, female) • Multiple card use (low, high). We use a program, called S E G U E (Exhibit 1), for forming various target segments from these building blocks. After the segment is formed respondents are differentially weighted by their resemblance to the target. We can then use SIMOPT to find optimal replacement or extension products for the target segment. Illustratively, assume that Epsilon management is interested in a target segment that entails one or more of the following categories: • Highest income • Male • High travel frequency • Heavy user of multiple credit cards.
Psychographic segment 1
Psychographic segment 2
$100 none 24 hours/day 90 days $200,000 $30,000 $2,500 rep $2/visit AHCRD yes
$80 none 9-5 weekdays none $50,000 $30,000 $2,500 rep $5/visit AHC yes
none yes (20% disc)
none none
Management weights the attributes with values of 3, 2, 1, and l, respectively. An individual who respects all four categories receives the highest weight (viz, a weight of 7). Individuals who fall into none of the categories receive a zero weight, and so on. (As it turned out, 384 out of 480 respondents fell into at least one of the four categories shown above.) S E G U E first prepares a respondent weights file that summarizes the target segment specifications. SIMOPT is then run to find the best line extension that would appeal to this segment, given that the parent product, Epsilon, remained in the line. The column labeled up-scale market in Table 5 shows the best profile for the target segment just selected. As noted, this profile represents a major shift (9 out of 12 attributes) from the current Epsilon card. Market share for Epsilon in the up-scale segment increases almost 40% with the addition of this card. Next, we consider two of the four groups obtained from the psychographic clustering. Group 1 was identified as active travelers, socially oriented, and gregarious, while group 2 was characterized as active travelers, but less extroverted and socially oriented. There were 121 respondents in group 1 and 105 respondents in group 2. We used SIMOPT to find the optimal profile for each of these two psychographic segments. The columns labeled Psychographic Segments 1 and 2, respectively, show the optimal product
PRODUCT DESIGN STRATEGIES FOR POSITIONING
profiles for each cluster. Both profiles differ markedly from the current Epsilon card. We also note that the first target segment is very close to the up-scale segment (described above). Psychographic segment 2 shows less interest in some of the amenities and less concern with wider card acceptance (in restaurants and department stores). Market shares increase 30% over the current Epsilon card share in these two psychographic segments. Other target segments could also be selected by the user and various weights could be applied to attributes, and levels within attributes, to reflect target market size attractiveness, cohesiveness, reachability, and compatibility with current Epsilon offerings. In short, the combination of target market selection and product optimization within target provides Epsilon with flexibility for designing specialized products for segment needs, on either a replacement or line extension basis. Conclusions By way of a case example, we have tried to illustrate how conjoint input data (and respondent attribute level perceptions) can be analyzed from a variety of viewpoints, entailing both short term strategies (maintaining the current offering's profile) and longer term strategies (changing the current offering's profile). Emphasis centered on optimal product design and target market selection. The heart of the proposed system is the SIMOPT model for product design and segmentation. The model considers: 1. Market share and/or profit-return optimization 2. Total market and/or individual segment forecasts 3. Sensitivity analysis as well as optimal profile seeking 4. Cannibalization issues related to product complementarity and line extension strategies 5. Calibration of results to existing market conditions 6. Constrained optimization, through fixing 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) 8. Sequential competitive moves, such as line extensions or competitor actions/reactions.
J PROD INNOV MANAG 1991;8:189-202
201
Like any model, SIMOPT no doubt will be modified and extended as further information about its performance and user reception is obtained. Still, the model provides an example of how current conjoint-based simulators can be extended well beyond their traditional applications to estimating market shares for a few user-selected profiles.
Concluding Caveats Throughout, we have also discussed various caveats in the use of the proposed techniques, such as difficulties in modeling the time trends over which various predicted outcomes take place, the possible omission of important product attributes, and various aspects of competitive retaliation. Many of these limitations stem from the reliability/validity of conjoint input itself. 2 Green and Srinivasan [4] stress the importance of conjoint validation experiments and report some recent findings at the academic and industry levels. As simulators (and optimizers) routinely yield market share forecasts, there is always the danger of spurious precision. Our own experience supports the value of using calibration (e.g., current) market shares, as is done in the SIMOPT model. The model's forecasts are then anchored to these base levels, so that the researcher is, in effect, reporting relative (rather than absolute) levels. We also need ways to insure that all important attributes are measured, that costs can be accurately estimated at the attribute level, that marketing mix variables (e.g., distribution levels) can be accommodated, and that dynamic competitive effects can be estimated and included in the models. Clearly, there is much work left to be done. T h e a u t h o r s w o u l d like to a c k n o w l e d g e s u p p o r t o f the Citib a n k F e l l o w s h i p f r o m the Sol C. S n i d e r E n t r e p r e n e u r i a l C e n t e r a n d the S E I C e n t e r for A d v a n c e d Studies in M a n a g e m e n t , b o t h at t h e W h a r t o n School.
References 1. Green, P. E. Hybrid models for conjoint analysis: An expository review. Journal of Marketing Research 21 : 155-159 (May 1984). 2 To this end, researchers frequently run split-half reliability tests to check on the stability of shares, returns, etc. over changes in sample size and composition.
202
J PROD INNOV MANAG 1991;8:189-202
2. Green, P. E., Carroll, J. D. and Goldberg, S. M. A general approach to product design optimization via conjoint analysis. Journal of Marketing 45:17-37 (Summer 1981). 3. Green, P. E. and Krieger, A. M. Recent contributions to optimal product positioning and buyer segmentation. European Journal of Operational Research 41:127-141 (1989). 4. Green, P. E. and Srinivasan, V. Conjoint analysis in marketing: New developments with implications for research and practice. Journal of Marketing 54:3-19 (October 1990). 5. Herman, S. Software for full-profile conjoint analysis. Sawtooth
Conference on Perceptual Mapping, Conjoint Analysis, and Computer Interviewing,. Ketchum, ID: Sawtooth Software. pp. 117-130, 1988. 6. Johnson, R. M. Adaptive conjoint analysis. Sawtooth Confer-
ence on Perceptual Mapping, Conjoint Analysis, and Computer Interviewing. Ketchum, ID: Sawtooth Software. pp. 253-266, 1987.
P.E. GREEN AND A. M. KRIEGER
7. Kohli, R. and Krishnamurti, R. A heuristic approach to product design. Management Science 33:1523-1533 (1987). 8. Saporito, W. Who's winning the credit card war? Fortune: 6671 (July 2, 1990). 9. Sen, S. K. Issues in optimal product design. Analytical Ap-
proaches to Product and Marketing Planning: The Second Conference. Cambridge MA: The Marketing Science Institute. pp. 265-274, 1982. 10. SPSS/Categories. Chicago, IL:
SPSS, Inc., 1990.
11. Sudharshan, D., May, J. H. and Gruca, T. DIFFSTRAT: An analytical procedure for generating optimal new product concepts for a differentiated-type strategy. European Journal of Operational Research 36:50-65 (1988). 12. Sudharshan, D., May, J. H. and Shocker, A. A simulation comparison of methods for new product location. Marketing Science 6:182-201 (Spring 1987).
189
0000
Product Design Strategies for Target-Market Positioning Paul E. Green and Abba M. Krieger
Designing optimal positioning strategies for target segments has become an area of intense research interest over the past few years. This article examines a variety of strategies that can be operationalized from conjoint analysis input data. Paul Green and Abba Krieger discuss strategies for modifying buyer perceptions, ideal-level preferences and attribute importances that are attractive for a firm's existing brand(s). They then consider longer run strategies for modifying the current brand's attribute levels and develop a case to illustrate applications of the techniques.
Address correspondence to Paul E. Green, S.S. Kresge Professor of Marketing, Marketing Department, Suite 1400, The Wharton School, University of Pennsylvania, Steinberg-Dietrich Hall, Philadelphia, PA 19104-6371. © 1991 Elsevier Science Publishing Co., Inc. 655 Avenue of the Americas, New York, NY 10010
Introduction Few marketers would dispute the central roles that product positioning and market segmentation play in today's economies, national or global. The last decade has witnessed a surge of new methodological and modeling developments for optimal positioning. Reviews of these developments are provided by Green, Carroll and Goldberg [2], Sen [9], Sudharshan, May and Shocker [12], Kohli and Krishnamurti [7], Sudharshan, May and Gruca [11], and Green and Krieger [3]. Conjoint analysis [4] has been central in the implementation of many optimal positioning models. The advent of user-friendly personal computer packages, such as those of Herman [5], Johnson [6], and, most recently, SPSS [10], has served both to define the state of conjoint practice and to make the methodology accessible to wider audiences. Helpful as they are, however, commercial conjoint packages focus largely on data collection and part-worth estimation. While all three of the packages cited above contain buyer choice simulators, none deals with product optimization and related issues. The purpose of the current article is to discuss various extensions of current product positioning practice. As in the case with commercial packages, the methodology described here collects conjoint preferences and respondent background data. Attribute-level perceptions data for each respondent's most preferred brand are also collected. (Perceptions data collection represents a departure from the commercial packages' input 0737-6782/91/$3.50
190
J PROD INNOV MANAG 1991 ;8:189-202
BIOGRAPHICAL SKETCHES Paul E. Green is the S.S. Kresge professor of marketing at the Wharton School, University of Pennsylvania. Dr. Green's research emphasizes quantitative methods and new measurement techniques in market analysis and consumer research. He has been honored for this research by the American Marketing Association, the American Statistical Association, the American Psychological Association, the Market Research Society, and the Association for Consumer Research. Dr. Green is the author or coauthor of several books, including the widely used text Research for Marketing Decisions, now in its fifth edition. He is also a prolific contributor to marketing and business journals. Abba M. Krieger is professor of statistics and operations research at the Wharton School, University of Pennsylvania. He is the author or coauthor of articles in statistical methodology and the interface between statistical methodology and optimization theory. His current research interests include theoretical and empirical analyses of the bootstrap resampling technique and application of statistical methods and operations research to problems in marketing research.
requirements; otherwise, the models we describe use the same kinds of data that are routinely collected in industry-based conjoint studies.) Our approach deemphasizes the methodology's mathematics in favor of a more managerial slant that focuses on strategic questions. We use a case format that considers a hypothetical firm, the Epsilon Company, that is in the consumer credit card business. While all attribute level descriptions, cost data, product profiles, and background variables are disguised, the case nonetheless has been designed to be realistic in terms of the kinds of strategic research questions that are raised in this industry. Study B a c k g r o u n d As described in Fortune magazine [8], the credit card industry is feeling the heat of new competitors, most notably Sears' Discover Card and, even more recently, AT&T's Universal Card. Some major players, such as Citibank, are also raising the ante by buying out credit card portfolios of smaller financial institutions; it is believed that Citibank paid the equivalent of $268 per account (one of the highest prices ever), for its purchase of Bank of New England's card portfolio. Credit card companies are also adding new consumer benefits (e.g., retail purchase insurance), cashback bonuses (e.g., the Discover Card) and other features in the battle for market
P . E . GREEN AND A. M. KRIEGER
share. With new customers getting ever harder to corral, attempts are being made to extend card usage to such establishments as fast foods, movies, and even toll booths. In the case described here, Epsilon is a relatively small player in a group of competitors that include Alpha, Beta, Gamma, and Delta. Alpha and Gamma are the giants in the field (with market shares of 28% and 45%, respectively). EpsiIon's share is only 10%. Short Run Strategies Epsilon's management is concerned with both short run and longer run competitive strategies. In terms of short run strategy, the following questions are relevant: 1. Are the characteristics of Epsilon's card profile being misperceived by its current users? If so, how can it improve its position by focusing on those particular card attributes where the correction of misperceptions is most advantageous? 2. Is it possible to change current card users' preferences for various profile levels that favor Epsilon's offering? If so, what gains might be associated with moving all card users' ideal levels closer to the specific levels provided by Epsilon's card? 3. What are the current strengths and weaknesses of Epsilon's card? Which attribute saliences (i.e., perceived attribute importances) should be stressed in order to increase the share/profits of its current product? It should be noted that all three sets of questions involve actions that could be taken with no change in the current card's actual attribute levels. Rather the intent (through advertising) is to modify consumers' perceptions, ideal levels, or attribute saliences in ways that are differentially favorable to Epsilon. Of course, the consequences of the actions (particularly changing card holders' ideal attribute levels) may be very long in coming, if at all. Longer-Run Strategies In contrast, changes in Epsilon's product offering, including features, user benefits, and price are typically longer term; even changing the card's price (annual billing) takes time. In terms
PRODUCT DESIGN STRATEGIES FOR POSITIONING
of the longer run, the following questions are relevant: 1. Assuming that the current Epsilon card's attribute levels and price could be changed, what is the optimal bundle of features/benefits/price to be offered, conditional on competitors' current profiles? 2. Assuming that Epsilon could introduce a line extension (a new card in addition to its current card), what should the new card's profile be? 3. Next, assume that Epsilon does, in fact, introduce the r e c o m m e n d e d line extension. Beta observes this event and decides to retaliate by replacing its current card with a new profile. What should Beta's new card be? Note that the preceding strategies make various assumptions about competitive behavior. In the first two questions we do not take up the issue of competitive retaliation but do consider Epsilon's competitive draw and cannibalization. In the third question, however, we consider the case in which Beta responds to the initial line extension m o v e of Epsilon.
Target Marketing Up to this point we have been assuming that Epsilon's product positioning strategy is based on total market considerations. We now focus on product design for selected target segments. The following questions are appropriate: 1. We first assume that Epsilon m a n a g e m e n t wishes to consider a line extension for a preselected target segment. What should this card's profile be? 2. We next assume that Epsilon wishes to choose two target segments and replace its current card with two new cards, one for each segment. What should their profiles be? The preceding product positioning and target marketing actions are only illustrative of the possibilities for which the proposed methodology is designed. T h e y should be sufficient, however, for illustrating the versatility of the approach.
The Survey Epsilon researchers next designed and implemented a national survey of credit card holders,
J PROD INNOV M A N A G 1991 ;8:189-202
191
using a telephone-mail-telephone interviewing procedure [4]. A total of 480 respondents, all of w h o m used at least 1 of the 5 credit cards, completed the interviews. The interview employed a hybrid conjoint procedure [1] and consisted of 12 attributes (Table 1). In addition to the conjoint data, information was also obtained on respondents' perceptions of the attribute levels of their most preferred card; this was done by simply asking them to pick the attribute levels (Table 1) that most closely matched their perceptions of the features of their most preferred card. R e s p o n d e n t background information on card usage, travel, and selected demographics was also obtained. For background purposes, Figure 1 shows part-worth summaries for each of the 12 attributes. As might be expected, the services are such that the more enhanced levels are preferred to the more basic levels that, in turn, are preferred to no services at all.
Cost Data Obtaining Epsilon's cost data at the attribute level was a m u c h more difficult job. Our interest was restricted to variable costs only; even at that, crude estimates had to suffice. Each of the " n u l l " or base levels (lowest part-worth levels) was coded at zero cost, and costs of enhanced levels were expressed as (negative) departures from this base. Annual price was translated into annual gross revenue for the base profile; costs for enhancements were deducted from their revenue estimates. The results of these estimation procedures provided Epsilon researchers with an annual contribution to overhead and profit, estimated on a percard level. (The contribution includes estimated interest charges.)
Current Competitive Profiles Based on published data, each competitor (Alpha through Epsilon) was profiled in terms of its " t r u e " attribute levels. These profiles are shown in Table 2, along with industry-based current market shares for each of the five cards. We note that Alpha's offering is a " p r e s t i g e " c a r d - - h i g h e s t priced of all competitors, with enhanced levels of most service attributes and with no end-of-year cash rebate. Beta's offering em-
192
J PROD 1NNOV M A N A G 1991 ;8:189-202
P . E . G R E E N AND A. M. K R I E G E R
Table 1. Card Attribute Levels Used in Conjont Survey Annual price
$0
$10
$20
$50
$80
$100
Cash rebate (end-of-year, on total purchases) none
1/2%
1%
800 number for message forwarding none
9-5pm weekdays
24 hours per day
Retail purchase insurance none
90 days coverage
Common carrier insurance (death, injury) none $50,000 $200,000 Rental car insurance (fire, theft, collision, vandalism) none $30,000 Baggage insurance (covers both carry-on and checked) none $2,500 depreciated cost $2,500 replacement cost Airport club admission (based on small entrance fee) no admission $5/visit $2/visit Card acceptance air, hotel, rental cars; AHC and most restaurants; AHCR and most general retailers (AHCRG); AHCR and department stores only (AHCRD) Twenty-four hour medical/legal referral network no yes Airport limousine to city destination not offered available at 20% discount 800 number for emergency car service not offered available at 20% discount
Figure 1. Average part-worth values from hybrid model
Scale Values
0.50.4-
$~""~ *'o
0.3-
~
0.2-
0.1-
~S0
J1% None 12%
,~..~ ~ - ,
, , , Price
Rebate
y
~4hr~ Nolle 9-5prn
~ i '~, Message Forwarding
,$~k~200k ays No~ne l
Purchase Insurance
I
Carrier Insurance
i
2 1
I Car Insurance
0.50.4-
Ye~
0.3-
AHCRG
~"~CRD
0.2-
/el
/,
0.1
, Baggage Insurance
Airport Clubs
, Acceptance
Medical/ Legal
Yu
Limousine
/,
Emergency Car Service
PRODUCT DESIGN STRATEGIES FOR POSITIONING
J PROD INNOV MANAG 1991 ;8:189-202
193
Table 2. Card Profiles of Five Competitors
Attribute Price Cash rebate Message forwarding Retail purchase insurance Common carrier insurance Rental car insurance Baggage insurance Airport clubs Acceptance Medical/legal Airport Limousine Emergency car serv. Current mkt. share
Alpha
Beta
Gamma
Delta
Epsilon
$80 none 9-5 weekdays 90 days $200,000 $30,000 $2,500 rep. none AHCRG yes yes 20% disc. yes 20% disc. 28%
$20 none
$20 none 24 hours daily none $50,000 none $2,500 dep. $2/visit AHCRD yes yes 20% disc. yes 20% disc. 45%
$0 0.5%
$10 none 9-5 weekdays 90 days none none
none 90 days $200,000 $30,000 $2,500 rep. none AHCRD no none none 8%
phasizes insurance c o v e r a g e (where it is head-tohead with Alpha but is m o r e m o d e s t l y priced). G a m m a ' s offering is priced the same as Beta's but its profile emphasizes c o n s u m e r a m e n i t i e s - message forwarding, airport clubs, medical/legal, airport limousine, and e m e r g e n c y car service. Delta is a " b a r g a i n - b a s e m e n t " e n t r y - - n o annual costs plus a small rebate on cumulative annual purchases. It offers v e r y few services and has s o m e w h a t less limited a c c e p t a n c e than the others. Epsilon's price is less than Alpha's, Beta's and G a m m a ' s but it offers both limited amenities and limited insurance services. As n o t e d in the last row o f Table 2, the bigger players are Alpha and G a m m a ; Beta's, Delta's and Epsilon's shares are roughly equal to each other and c o n s i d e r a b l y below those of the big two.
Buyer Choice Rule The individual c o u n t e r p a r t s to the averaged partworths o f Figure 1, along with the profile descriptions o f Table 2, are typically e n t e r e d into a conjoint choice simulator. The r e s e a r c h e r is then faced with the p r o b l e m o f what choice rule to use. T h e r e are three choice rules currently in use in c o m m e r c i a l conjoint packages: (1) the max utility choice rule; (2) the logit choice rule; and (3) the B r a d l e y - T e r r y - L u c e (share o f utility) rule.
none 90 days none none none none AHCR no yes 20% disc. yes 20% disc. 9%
none $5/visit AHCRD yes none none 10%
E a c h o f these rules can be mimicked by what we call the alpha rule. S o m e w h a t m o r e formally, assume that any specified supplier's profile can be r e p r e s e n t e d by a v e c t o r js; s -- 1, 2 . . . . . S. Given individual k's v e c t o r o f part-worths, we can c o m p u t e Uk.s = Uk(js)
(1)
as the utility of individual k for supplier s. The " m a r k e t s h a r e " (or probability o f k choosing supplier s) is ~'k,~ =
U~ (Js) s E U~ (Js)
(2)
S=I
w h e r e the e x p o n e n t a is c h o s e n to be non-negative. As alpha a p p r o a c h e s infinity the preceding choice rule mimics the m a x utility rule. Smaller values o f alpha can mimic a logit rule. If alpha is set to 1.0, we have the B r a d l e y - T e r r y - L u c e rule. A c o m p u t e r program, called A L P H , has been designed to find the value of alpha that, if applied to e a c h individual's utilities, obtains the closest overall fit to actual m a r k e t shares for each supplier. Exhibit 1 provides a brief description o f the program. W h e n applied to the current survey data, the best fitting value o f alpha was 10.4. This is the value used in all s u b s e q u e n t analyses.
194
J PROD INNOV MANAG 1991 ;8:189-202
P.E. GREEN AND A. M. KRIEGER
Exhibit 1. Characteristics of Computer Programs Used in Case Study Program
Input
What the program does
Output
ALPH
• Each individual's utilities for the profile of each supplier • Actual market share for each supplier • Individual's importance weight file
• Solves for optimal value of alpha which, if applied at the individual level, best reproduces actual market shares • Solves for implied predictions of user-supplied alpha values • Uses four alternative algorithms in solving for optimal alpha value
• Optimal value of alpha • Predictions of shares for user-supplier alpha values
PERCEPT
• E a c h individual's current brand identification • Individual's importance weight file • Individual's part-worths file • Individual's demograhics file • Individual's perception vector for current brand
• Finds s u m m a r y , by brands, of incidence of each perceived attribute level and associated average part-worth • C o m p u t e s sensitivity of average part-worths as perceived levels become increasingly similar to targeted levels • Can analyze data for the overall profile, subsets of attributes, or individual attribute perception changes • Analysis can be performed at total-market or by selected demographic level
• Size of demograhic s e g m e n t • Relative incidence of brands within s e g m e n t • Associated brand utility changes as perceptions approach targeted levels • Sensitivity analysis by individual attributes • S u m m a r y tables of misperceptions, by attribute and supplier
IDEAL
• Individual part-worths file • Targeted levels for the ideal profile • Interpolating constant: probability of changing current ideal to targeted ideal
• If r e s p o n d e n t ' s ideal level already agrees with target, no change is made • If current ideal does not equal target, targeted level is increased proportionately toward current ideal, based on user-supplied interpolation value
• N e w matrix of individual partworths • This n e w file is then p r o c e s s e d by S I M O P T for c o m p a r i s o n with original partworths file
SALIENCE
• Individual attribute importances and attribute-level desirabilities • Individual importance weight file • Demographic (background) file • Current market shares of all suppliers • E a c h supplier's profile • Value of alpha and demographic attribute weights • Attribute level cost/return data
• Program shows, attribute by attribute, how share/return for a selected supplier changes as greater importance is associated with the attribute • Program c o m p u t e s optimal distribution of effort and new set of importance weights that maximize selected supplier's share/return • Analysis can be done at the overall market or selected demographic level • Program also allows u s e r to read in efforts and obtain their implied returns
• N e w set of attribute importances • Predicted share/return if n e w importances are u s e d • N e w part-worth matrix with modified importance weights for input to S I M O P T or conventional simulators
PRODUCT DESIGN STRATEGIES FOR POSITIONING
Exhibit 1.
J PROD INNOV MANAG 1991 ;8:189-202
195
(continued)
Program
Input
What the program does
Output
SIMOPT
• Individual part-worths file • Individual's importance weight file • Demographics (background) file • Current market shares for all suppliers • Each supplier's profile • Value of alpha and demographic attribute weights • Control parameters for optimization • Attribute-level cost/return data
• For any set of competitive profiles, the program computes share/return for each supplier, • All shares/returns are automatically adjusted to base-case conditions • Sensitivity analyses can be performed at the individual attribute level • Optimization can be carried out by supplier or for groups of suppliers; attribute levels can be fixed for conditional optimization • Analyses can be conducted at the total market or selected target segment level
• Market share/return for each supplier • Individual supplier selection file • Optimal product description for total market or selected segment • Sensitivity analysis results, by level within attribute
SEGUE
• Individual part-worths file • Individual's importance weight file • Demographics (background) file • Segment attribute weights
• For any target segment composable from the background variables (with weights supplied by the user), the program computes size of segment, ideal levels, attribute importances, and attribute level desirabilities • Both additive and conjunctive segments can be created • The user can also input any trial product profile and find its total utility compared to the best profile • A respondent weights file is prepared for later use in SIMOPT
• Attribute importance, level desirabilities, and ideal levels, by selected segment • Profile utilities by selected segment • Respondent weights file summarizing each individual's relevance to the target segment (input to SIMOPT)
Short Range Strategies
3. Change consumers' perceived attribute importances in ways that increase the relative attractiveness of the firm's product.
Changing actual product attribute levels may take some time to accomplish and is associated with both technical and market uncertainty regarding successful implementation. In the short run the firm may elect to maintain its current product and attempt to:
Modifying Consumers' Perceptions
l. Correct consumers' misperceptions of its attribute levels. 2. Modify consumers' attribute level preferences in ways favorable to the product.
The PERCEPT program (Exhibit 1) has been designed to examine consumers' perceptions of product attribute levels for their current (or most preferred) brand and to find those attributes that
We discuss each of these actions, in turn.
196
J PROD INNOV MANAG 1991;8:189-202
P.E. GREEN AND A. M. KRIEGER
Table 3. Summary of Probabilities of Correct Perceptions of Alpha, Gamma and Epsilon
Alpha Gamma Epsilon Mean
1
2
3
4
5
.58 .84 .93 .78
.76 .64 .76 .72
.53 .63 .66 .61
.75 .70 .87 .77
.82 .60 .73 .72
Attributes 6 7 .73 .67 .95 .78
.90 .82 .90 .87
8
9
10
11
12
Mean
.50 .73 .90 .71
,73 .53 .80 .69
.57 .67 .78 .67
.69 .66 .88 ,74
.92 .91 .80 .88
.71 .70 .83
Comparative Average Utility gains for Correcting Misperceptions: Alpha Gamma Epsilon
9.8% 10.8 7.8
Best Attributes for Epsilon Attributes 9 (card acceptance) and 10 (medical/legal network)
offer the greatest gains in utility for moving perceptions to the " c o r r e c t " levels of the firm's brand. Input data consist of each respondent's multiple choice data representing the level within each attribute that is perceived to match most closely the consumer's perception of his/her current brand. PERCEPT plays two roles. First, it provides summary descriptions, by brand, of consumer perceptions versus "reality" levels. In this way, one can compare brand efficiencies with regard to how one's advertising messages are getting across. Alternatively, one can compare attributes (across brands) in terms of the accuracy with which their levels are perceived. Second, PERCEPT can be used to examine the differential attractiveness of correcting consumer misperceptions, so that the firm can focus on those attribute levels that carry the highest payoff in consumer utility (and, other things being equal, brand choice). For illustrative purposes, we select three of the five suppliers: Alpha, Gamma and Epsilon. Table 3 summarizes the results. We first note that, on average, Epsilon users show the highest incidence of accurate perceptions; 83% of the time they correctly perceive the levels of Epsilon's true profile. This compares to 71% for Alpha and 70% for Gamma. In all three cases, however, the perceived modal profiles corresponded to the true profiles of each supplier. We also note from Table 3 that, on average, attribute 12 (emergency card service) and attribute 7 (baggage insurance) were most often perceived correctly, while attribute 3 (message forwarding) was least often perceived correctly.
In addition, Table 3 indicates that the achievement of perfect agreement between true and perceived attribute levels would increase average utilities by only 8-11%. We also note that the two most attractive attributes for Epsilon to work on are card acceptance and medical/legal network. These two attributes alone count for almost all of Epsilon's potential utility increase of 7.8%. All in all, we conclude that perceptions are reasonably accurate, particularly for Epsilon. Moreover, the gain in average supplier utility for correcting misperceptions is relatively small (8-11%). Finally, if Epsilon's advertising emphasizes its breadth of acceptance and availability of medical/legal services, utility gains could be forthcoming in the future. Finally, it is worth noting that none of the commercial conjoint packages deals with the analysis of perceptions data. Their simulators all assume that each consumer correctly perceives each supplier's profile, as defined by the program's user.
Changing Ideal Attribute Levels In the same spirit we can examine the sensitivity of Epsilon's market share/return if modifications (through advertising, packaging, etc.) could be made to consumers' ideal (most preferred) attribute levels. For example, suppose a consumer currently does not care for either retail purchase insurance or medical/legal referral services. Suppose Epsilon could induce consumers to modify their current ideal levels (obtained from their part-worths) to agree with its card's current levels. What would be the impact of this change?
PRODUCTDESIGNSTRATEGIESFOR POSITIONING
J PRODINNOVMANAG 1991;8:189-202
I 20
0
i 60
I 40
~
Alpha
•
Gamma
~
Alpha
Gamma
~
80
I I~"
197
Base case conditions
100
IEpsll°n
IDEAL with P=0.5
t~"
IDEAL with P = 0.15
I
Alpha
III
Gamma
~
I Figure 2. Stacked bar chart showing market share changes related to various strategies (total market)
Gamma
Alpha
IV
~"
(~*
Replacement for denoted ~ *
~
~**
Addition to ~ , denoted (~**
(~,
t
/~
Alpha
Gamma
~"
I
Alpha
VI 0
i /~* 20
More realistically, suppose that one could move consumers' current ideal levels to a set of prespecified target levels with a 50% probability. What would be the result on Epsilon's share/return? The I D E A L program (Exhibit 1) implements this objective. This program solves for a new matrix of part-worths for the total sample of respondents (not just Epsilon preferred users) that reflects a user-supplied probability of movement from current to targeted ideal levels. Illustratively, we first choose a probability of 0.5 of moving each respondent's vector of ideal attribute levels from their current values to those of the targeted Epsilon profile (Table 2). Having done this, we use a computer choice simulator (with an alpha choice rule of a = 10.4) to find a new set of market shares. Figure 2 shows the results. The first stacked bar chart of Figure 2 shows the initial market shares (Table 2). The second stacked bar chart shows the effect on shares obtained from application of the I D E A L program, with P = 0.5. As noted, Epsilon's share increases
I
!
Gamma 410
~" 60
~
(~
**
80
Replacement for ~, denoted~*
100
from 10% to 31%. Of course, a probability of 0.5 is still unrealistic. A more conservative value of P = 0.15 was tried next. The third bar chart shows an increase in Epsilon's share of 14% (versus the base case share of 10%). This is much more realistic. In a similar manner, other values of P could be tried to get some feel for the sensitivity of Epsilon's share to this type of part-worth change.
Changing Attribute Importances The third avenue for redirecting Epsilon advertising is changing consumers' evaluations of attribute importances. The optimization procedure employed in this strategy is considerabl); more complex. To carry out this strategy we apply an algorithm called S A L I E N C E (Exhibit 1). The principal inputs to S A L I E N C E consist of attribute desirabilities, attribute importances (both obtained from a hybrid conjoint model), competitive profiles (shown in Table 2), and the alpha exponent (10.4, in this example). SALI-
198
J PROD INNOV MANAG 1991:8:189-202
E N C E contains a non-linear optimization routine that finds a new set of importance weights for maximizing Epsilon's share/return. In this example we first use S A L I E N C E to identify those attributes where Epsilon has a differential advantage over one or more competit o r s - t h a t is, where an increase in the salience of the attribute produces a higher return for Epsilon, other things remaining equal. Only five attributes: • Price • Card acceptance • Message forwarding • Medical/legal referral network • Retail purchase insurance met this condition. Next, we applied the optimization routine that finds the best allocation of new advertising effort across the five candidate attributes. The algorithm indicated that new (incremental) advertising effort should be allocated across only two of the five attributes: price and the medical/legal referral system. Under the current situation respondents' judged importances of the attributes, price and medical/legal, were 17.3% and 9.6% out of a total of 100%. Under the optimized allocation of new effort these importances should expand to 40.0% and 20.8%, respectively, out of a total of 100%. These two attributes constitute its principal "leverage" attributes. Some Caveats
All three of the preceding algorithms, PERCEPT, IDEAL, and S A L I E N C E are essentially sensitivity analysis approaches. They do not tell us how we should go about implementing the desired changes, how likely we will be able to accomplish the task, and what the effort will cost. Rather, they forecast the stakes (or payoffs) if the task can be accomplished. If the payoff is relatively small (as was the case for PERCEPT), the manager may elect to disregard the strategy. On the other hand, a large potential payoff could justify further study of the means and associated costs of
P . E . G R E E N AND A. M. KRIEGER
pursuing the strategic objective. Of course, further study would also consider additional factors related to payoff estimation and cost outlays.
Longer Range Strategies We now turn to longer range strategies available to Epsilon. Illustratively, we first consider various strategies in the context of the total market. Then we describe counterpart strategies at the target market level. Optimal Product Design
The SIMOPT program (Exhibit 1) is the algorithm that we use for finding optimal product profiles in the context of competitive offerings. SIMOPT [3] is a conjoint-based algorithm that solves for an attribute-level configuration that optimizes share/ return for a specified supplier. It uses the alpha choice rule (set at 10.4 in this example) and a divide-and-conquer heuristic that breaks down the overall optimization task into subset problems that are then solved iteratively. Like most heuristics, however, optimization is not guaranteed. Fortunately, if the number of possible attribute-level combination is less than a million, SIMOPT utilizes a complete enumeration routine. (In the sample problem there only 186,624 combinations.) In the present problem we first solve for the " b e s t " (in the sense of maximizing contribution to overhead and profits) product profile for EpsiIon, given the specified profiles (Table 2) of its competitors. Initial shares for all five competitors appear in the bar chart of panel I of Figure 2. Epsilon's current share is 10%. Panel IV of Figure 2 shows that the best replacement (denoted e*) for Epsilon's current product results in an increase in Epsilon's share to 22%. Its associated contribution to overhead/ profit increases 82% over that of the base case.l Table 4 shows the changes that take place in optimizing Epsilon's contribution. Note that Epsilon moves to a high price, along with a cash rebate, increased insurance coverage, inexpensive access to airport clubs, and emergency car service.
t F o r r e a s o n s o f c o r p o r a t e c o n f i d e n t i a l i t y , w e a r e u n a b l e to report actual profit contributions.
PRODUCT DESIGN STRATEGIES FOR POSITIONING
J PROD INNOV M A N A G 1991:8:189-202
199
Table 4. New Card Profiles Associated with Alternative Strategies
Current epsilon Price Cash Rebate Message Forwarding Retail Purchase Insurance Common Carrier Insurance Rental Car Insurance Baggage Insurance Airport Clubs Acceptance Medical/legal Airport Limousine Emergency Car Service
$10 none 9-5 weekdays 90 days none none none $5/visit AHCRD yes none none
In all, half of the original 12 attribute levels are changed. What happens if we keep the present Epsilon card and add a line extension (denoted e**). SIMOPT solves for the optimal extension and, somewhat surprisingly, it turns out to be the same profile as e*. Panel V of Figure 2 shows that the combined share o f e and e** is 8% + 21% = 29%. Thus, the loss due to cannibalism is only two percentage points. Most of the business picked up by e** is from competitive products. Moreover, the overhead/profit contribution for the combined offerings (e and e**) is 38% higher than the contribution of e* as a replacement for e. Hence, assuming that the costs of adding the line extension are not excessive it is better in this instance to line extend than to replace e. Suppose Epsilon does, in fact, add the new product e** to its line. The Beta Company observes this event and notes (panel V in Figure 2) that its share decreases slightly from 8% to 7%. Beta considers the prospect of replacing its current card with a new profile--one that optimizes its contribution, conditional on the changes that Epsilon has already made. Panel VI shows that Beta's best retaliatory move increases its share from 7% to 18%. EpsiIon's combined share decreases by only two percentage points. Most of Beta's new business comes at the expense of Gamma. Table 4 shows that Beta's replacement profile involves eight attribute changes. (Illustratively, this optimization was carried out, conditional on Beta's price and cash rebate policy remaining unchanged.)
Replaced or extended epsilon $100 1% 9-5 weekdays 90 days $200,000 none $2,500 dep $2/visit AHCRD yes none yes (20% disc)
Current beta
Replacement beta
$20 none none 90 days $200,000 $30,000 $2,500 rep none AHCRD no none
$20 none 9-5 weekdays none $200,000 none $2,500 dep $2/visit AHC yes none yes (20% disc)
none
Some Caveats Although SIMOPT is a very flexible algorithm for finding optimal product positionings, it should be borne in mind that the mechanism underlying the model does not consider the gestation period over which share changes take place. SIMOPT also takes into consideration existing products' shares and an opportunity to optimize, conditional on some attribute levels remaining fixed; however, it does not explicitly deal with the costs of product proliferation and the possible dynamic actions that competitors might take even before new products are introduced. Thus, in practice, forecast conditions might never be reached, due to a variety of market factors not under the firm's control. Finally, the SIMOPT model assumes that consumers' part-worths remain stable over the planning horizon, that all relevant attributes appear in the model, that costs are accurately measured at the attribute level, and that each firm enjoys a rough parity with respect to advertising and distribution levels. Clearly, these are important assumptions; their recognition underscores the need to consider SIMOPT (and our approach in general) as a set of planning models whose outputs need to be checked against other independent information sources and management judgment.
Target Market Product Design Up to this point all of our analytical excursions have entailed the total market. We now consider
200
J PROD INNOV MANAG 1991:8:18%202
P . E . GREEN AND A. M. K R I E G E R
Table 5. New Card Profiles Associated with Target-Market Product Positioning
Price Cash Rebate Message Forwarding Retail Purchase Insurance Common Carrier Insurance Rental Car Insurance Baggage Insurance Airport Clubs Acceptance Medical/legal Airport Limousine Emergency Car Service
Current epsilon
Up-scale market
$10 none 9-5 weekdays 90 days none none none $5/visit AHCRD yes
$100 none 24 hours/day 90 days $200,000 $30,000 $2,500 rep $2/visit AHCRG yes yes (20% disc) yes (20% disc)
none none
various strategies that can be directed towards specific target segments, selected by the researcher. Survey data were available for several background attributes: • Income level (moderate, higher, highest) • Psychographic clusters (four groups) • Travel frequencies (low, high) • Gender (male, female) • Multiple card use (low, high). We use a program, called S E G U E (Exhibit 1), for forming various target segments from these building blocks. After the segment is formed respondents are differentially weighted by their resemblance to the target. We can then use SIMOPT to find optimal replacement or extension products for the target segment. Illustratively, assume that Epsilon management is interested in a target segment that entails one or more of the following categories: • Highest income • Male • High travel frequency • Heavy user of multiple credit cards.
Psychographic segment 1
Psychographic segment 2
$100 none 24 hours/day 90 days $200,000 $30,000 $2,500 rep $2/visit AHCRD yes
$80 none 9-5 weekdays none $50,000 $30,000 $2,500 rep $5/visit AHC yes
none yes (20% disc)
none none
Management weights the attributes with values of 3, 2, 1, and l, respectively. An individual who respects all four categories receives the highest weight (viz, a weight of 7). Individuals who fall into none of the categories receive a zero weight, and so on. (As it turned out, 384 out of 480 respondents fell into at least one of the four categories shown above.) S E G U E first prepares a respondent weights file that summarizes the target segment specifications. SIMOPT is then run to find the best line extension that would appeal to this segment, given that the parent product, Epsilon, remained in the line. The column labeled up-scale market in Table 5 shows the best profile for the target segment just selected. As noted, this profile represents a major shift (9 out of 12 attributes) from the current Epsilon card. Market share for Epsilon in the up-scale segment increases almost 40% with the addition of this card. Next, we consider two of the four groups obtained from the psychographic clustering. Group 1 was identified as active travelers, socially oriented, and gregarious, while group 2 was characterized as active travelers, but less extroverted and socially oriented. There were 121 respondents in group 1 and 105 respondents in group 2. We used SIMOPT to find the optimal profile for each of these two psychographic segments. The columns labeled Psychographic Segments 1 and 2, respectively, show the optimal product
PRODUCT DESIGN STRATEGIES FOR POSITIONING
profiles for each cluster. Both profiles differ markedly from the current Epsilon card. We also note that the first target segment is very close to the up-scale segment (described above). Psychographic segment 2 shows less interest in some of the amenities and less concern with wider card acceptance (in restaurants and department stores). Market shares increase 30% over the current Epsilon card share in these two psychographic segments. Other target segments could also be selected by the user and various weights could be applied to attributes, and levels within attributes, to reflect target market size attractiveness, cohesiveness, reachability, and compatibility with current Epsilon offerings. In short, the combination of target market selection and product optimization within target provides Epsilon with flexibility for designing specialized products for segment needs, on either a replacement or line extension basis. Conclusions By way of a case example, we have tried to illustrate how conjoint input data (and respondent attribute level perceptions) can be analyzed from a variety of viewpoints, entailing both short term strategies (maintaining the current offering's profile) and longer term strategies (changing the current offering's profile). Emphasis centered on optimal product design and target market selection. The heart of the proposed system is the SIMOPT model for product design and segmentation. The model considers: 1. Market share and/or profit-return optimization 2. Total market and/or individual segment forecasts 3. Sensitivity analysis as well as optimal profile seeking 4. Cannibalization issues related to product complementarity and line extension strategies 5. Calibration of results to existing market conditions 6. Constrained optimization, through fixing 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) 8. Sequential competitive moves, such as line extensions or competitor actions/reactions.
J PROD INNOV MANAG 1991;8:189-202
201
Like any model, SIMOPT no doubt will be modified and extended as further information about its performance and user reception is obtained. Still, the model provides an example of how current conjoint-based simulators can be extended well beyond their traditional applications to estimating market shares for a few user-selected profiles.
Concluding Caveats Throughout, we have also discussed various caveats in the use of the proposed techniques, such as difficulties in modeling the time trends over which various predicted outcomes take place, the possible omission of important product attributes, and various aspects of competitive retaliation. Many of these limitations stem from the reliability/validity of conjoint input itself. 2 Green and Srinivasan [4] stress the importance of conjoint validation experiments and report some recent findings at the academic and industry levels. As simulators (and optimizers) routinely yield market share forecasts, there is always the danger of spurious precision. Our own experience supports the value of using calibration (e.g., current) market shares, as is done in the SIMOPT model. The model's forecasts are then anchored to these base levels, so that the researcher is, in effect, reporting relative (rather than absolute) levels. We also need ways to insure that all important attributes are measured, that costs can be accurately estimated at the attribute level, that marketing mix variables (e.g., distribution levels) can be accommodated, and that dynamic competitive effects can be estimated and included in the models. Clearly, there is much work left to be done. T h e a u t h o r s w o u l d like to a c k n o w l e d g e s u p p o r t o f the Citib a n k F e l l o w s h i p f r o m the Sol C. S n i d e r E n t r e p r e n e u r i a l C e n t e r a n d the S E I C e n t e r for A d v a n c e d Studies in M a n a g e m e n t , b o t h at t h e W h a r t o n School.
References 1. Green, P. E. Hybrid models for conjoint analysis: An expository review. Journal of Marketing Research 21 : 155-159 (May 1984). 2 To this end, researchers frequently run split-half reliability tests to check on the stability of shares, returns, etc. over changes in sample size and composition.
202
J PROD INNOV MANAG 1991;8:189-202
2. Green, P. E., Carroll, J. D. and Goldberg, S. M. A general approach to product design optimization via conjoint analysis. Journal of Marketing 45:17-37 (Summer 1981). 3. Green, P. E. and Krieger, A. M. Recent contributions to optimal product positioning and buyer segmentation. European Journal of Operational Research 41:127-141 (1989). 4. Green, P. E. and Srinivasan, V. Conjoint analysis in marketing: New developments with implications for research and practice. Journal of Marketing 54:3-19 (October 1990). 5. Herman, S. Software for full-profile conjoint analysis. Sawtooth
Conference on Perceptual Mapping, Conjoint Analysis, and Computer Interviewing,. Ketchum, ID: Sawtooth Software. pp. 117-130, 1988. 6. Johnson, R. M. Adaptive conjoint analysis. Sawtooth Confer-
ence on Perceptual Mapping, Conjoint Analysis, and Computer Interviewing. Ketchum, ID: Sawtooth Software. pp. 253-266, 1987.
P.E. GREEN AND A. M. KRIEGER
7. Kohli, R. and Krishnamurti, R. A heuristic approach to product design. Management Science 33:1523-1533 (1987). 8. Saporito, W. Who's winning the credit card war? Fortune: 6671 (July 2, 1990). 9. Sen, S. K. Issues in optimal product design. Analytical Ap-
proaches to Product and Marketing Planning: The Second Conference. Cambridge MA: The Marketing Science Institute. pp. 265-274, 1982. 10. SPSS/Categories. Chicago, IL:
SPSS, Inc., 1990.
11. Sudharshan, D., May, J. H. and Gruca, T. DIFFSTRAT: An analytical procedure for generating optimal new product concepts for a differentiated-type strategy. European Journal of Operational Research 36:50-65 (1988). 12. Sudharshan, D., May, J. H. and Shocker, A. A simulation comparison of methods for new product location. Marketing Science 6:182-201 (Spring 1987).