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Redesigning product lines with conjoint analysis: A comment
0000
ed
uctLines
Audysk
A Comment
Dick R. Wittink
PaRe and Rosenbaum [Journal of Product Innovation Management 4;120137 (June 1987)J discuss the use of conjoint analy-
In a recent article,
sis at Sunbeam Corporation. Their article provides a nice illustration of a conjoint application to food processor appliance redesign decisions. In this note, Dick Wittink compares characteristics of Sunbeam’s approach to both established and more recent results reported in the conjoirtt literature.
Introduction Page and Rosenbaum [9], hereafter referred to as PR, describe in considerable detail how Sunbeam Corporation used conjoint analysis in 1983 to help management redesign product lines for food processors. There are several excellent features of the application, including the use of pictorial representations, the collection of data in four geographic locations, the use of simulations to predict market shares for alternative product lines and the adjustment in market share predictions for brand differences in consumer awareness and availability. On the other hand, some of the features of their study deserve reconsideration. In this note, I discuss why and how these features could be modified.
Study Design Consumer preferences are obtained for twentyseven (hypothetical) food processors, a subset of all possible objects selected by PR based on a fractional factorial design. Such designs are commonly used by conjoint analysts for full-profile respondent evaluations. However, respondents are unlikely to keep track of twelve attributes in their evaluation of twenty-seven objects.’ Green and Srinivasan [5, p. 108) mention that “. . , the full-profile procedure is generally confined to, at
.4ddress correspondence to Dick R. Wittink. Johnson Graduate School of Management, Cornell University, Malott Hall, Ithaca. NY 14853. 1989 Elsevier Science PublishingCo., Inc. 655 Avenue of the Americas, New York, NY lOOlO
I One might argue that in the marketplace consumers face the problem of choosing among products that may differ on more than twelve attributes. However, they certainly do not attempt to rank order twenty-seven products while keeping track of twelve or more attributes. 0737-6782/89/53.50
*xl
J PROD INNOV 1989:6:?89-92
Dick R. Wit&k
D. R. WITTINK
MANAG
is Professor of Marketing and Quantitative Methods
at the Johnson Graduate School of Management. Cornell University. He received a Ph.D. in Industrial Administration from Purdue University in 1975. Previously. he was a member of the business school Faculty at Stanford University. where he started his tirst cot ,vint research project. l%is comment was written while he was d visiting professor of marketing at the Kellogg Graduate School of Management. Northwestern University. Other conjomt papem he has written apwar in inurnal of Marlieting. Journal nai of Consumer
Research
ofMarAwing
and Marketing
Research.
Jour-
Science. He frequently
discusses conjomt applications in executive programs sponsored by universities as well as firms such as AT&T
and A.C. Nielsen (Eu-
rope).
most, five or six factors. . . .” They recommend the use of bridging procedures for problems with more attributes to avoid information overload for respondents. They cite two studies (p. 109) showing better prediction or fit for the tradeoff matrix approach (versus full profiles) when the number of attributes is large (eight or nine),? The use of pictorial representations could reduce this information oL:erIoad problem. However, four of the attributes used by PR are included only as verbal descriptions, while the other eight are also described next to the picture. It appears, therefore, that a bridging procedure should be used in this case. In order to be able to estimate a main-effects part-worth mode1 for each respondent most efficiently, PR use an orthogonal design. Green and Srinivasan [5, p. 1IO] advise caution when environmental interattribute correlations are high. For food processors, many of the features (e.g., motor power, number of speeds) must be positively correlated with price. However. the extent to which the objects are unbelievable also depends on the range of variation used in the study. For example, the first object shown on page 124 of their study has a price of $199.99 while the second object costs $49.99. At the same time. the lower-priced object has more attractive features. A comparison of thera two objects will produce substantial incredulity for respondents. AS evidence of this problem, consider the result [9, p. 1261 that cne segment prefers the !PB.W price. This result can only occur if respondents have not held all other things constant : The use of pictorial representations approach for data collection tnfehsible.
renders the tradeoff matrix
while evaluating the price information. For example, respondents IT,-v infer quality differences from the price levels and reconc’le the unbelievability of the objects. For verbal descriptions of objects, Acito [ll shows that conjoint results (e.g., derived attribute importances) may be affected by the order of the attributes. PR place the price above the picture of the food processor. The prominence of price may have increased its importance relative to what is obtained for other attributes. Johnson [7] has obtained results consistent with Acito’s findings. Green and Srinivasan [5, p. 11 l] recommend that the attribute order is randomized over respondents while keeping the order constant across the objects evaluated by a given respondent.
Measurement Scale The collection of rank order preferences is consistent with the initial use of conjoint measurement in marketing [S] and with commercial practice [2]. However, concern has been expressed about the comparability of results across attributes that are defined on an unequal number of levels. Currim et al. [3] found that all three-ievel attributes obtained systematically higher average importances than the two-level attributes. When they adjusted the results, the attribute that had achieved fourth place out of six became the most important attribute! Wittink et al. [l l] completed an experiment to show that a systematic difference is obtained for both full-profile and tradeoff matrix rank order data. They found that the relative importance of one attribute increased dramaticaiiy by adding two intermediate levels. Also, Wittink and Krishnamurthi [IO] showed that market share predictions differed systematichlly by using objects defined on common attribute levels across two experimental conditions. ? hus, both derived importances and market share predictions may be affected by differences in the ;rumber of attribute levels used. PR report that “the overall conjoint results along with those from the segmentation demonstrxted to SAC w ich features should be stressed . . . ” (p. 17.71. Thus, attribute importances played a substantial role in the identification of t!le features thought to be most relevant to consumers. interestingly, the average relative impor-
REDESIGNING PRODUCT LINES: A COMMENT
J PROD iNNOV MANAG r?@HdB-92
tance of the three-level attributes is I I .7% versus 3.0% for the two-level attributes (p. 125).
291
PR use MONANOVA [9, p. 1231 to obtain a partworth function separately for each respondent. Although a nonmetric procedure is theoretically correct given the collection of ordinal preferences, Green and Srinivasan [S, p. 1141 conclude that metric and nonmetric procedures provide comparable results. Thus, PR could have used least-squares regression co estimate part-worth functions more quickly and less costly. Also, Cattin and Wittink 123show that regression analysis had become the favored approach for parameter estimation in commercial practice.
conjoint design for all consumers, several problems can occur. One is that consumers may infer unintended quality differences from the prices. Another possible problem is that consumers may provide more careful and/or werent evaluations of the other attributes dependent upon the price. For example, a respondeI:r could sort the objects based on price and carefully evaluate only those within one or two subgroups. This suggests that there may be interaction effects (which are not estimable in a main-effects design). Alternatively, the preference data should be weighted more heavily for objects with “acceptable” price levels. In any event, the exercise of providing evaluations would be more interesting to respondents if as many objects as possible are potentially attractive/relevant to them.
New Developments
Conclusion
Since 1983
PR 191show a very interesting application of conjoint analysis. The application discussed has several excellent features. In this note, I have identified opportunities for improving the validity and usefulness of the approach. Some of these changes involve additional complexity in data collection and/or analysis. In that case management needs to judge whether the expected improvement in results is worth the additional cost. Importantly, my concern is not about the applicability of conjoint analysis to the product line redesign decision faced by Sunbeam. Indeed, the technique seems very appropriate for the problem. However, the specific manner in which the application is designed and executed may have substantial influence on the validity of results, and hence the long-run acceptance by management of this technique. It is my hope that this note contributes to the use of “best practice” for research relevant to product innovation.
Parameter Estimation
a few A respondent
a given a given
the food processor market contains three price points: less than $60.00, between $60.00 and $125.00, and over $125.00” (p. 122). Conceivably, some consumers only consider food processors at the highest price levels, while others may restrict their market choices to the lower end of the price range. in the . .
3For recent results see
et al. [4] and
attribute et al.
@I.
References Acito. Franklin. An investigation of some data collection issues in conioint measurement. 1977 Educurors’ Proceedings. Chicago: American Marketing Association 82-85. (19773. Cattin, Philippe and Wittmk, Dick R. Commercial use of conjoint analysis: a survey. Juwnal of ;2larkering 46:44-53 (Summer 1982). Currim, Imran S.. Weinberg, Charles B. and Wittink, Dick R. Design of subscription programs for a performing arts series. Journal of Consumer Research 8:67-75 (June 1981). Green, Paul E., Krieger. Abba M. and Bansal. Pradeep. Completely unacceptable levels in conjc int analysis: a cautionary note. Journal of Murkering Ressearc~ 25:293-300 (August 1988).
33
a.*
J PROD INNOV MANAG Ip$P;b:289-92
5. GQ YI Paul E. and Srini\asan. V. Conjoint analysis in consumet research: issues and outlook. Journal of ConsrtmerResear& 5:103-123 (September 1978). 6. Johnson, Richard M. Adaptive conjoint analysis. Sa~rt~)orh &&were Co&rem-e Proceedings 253-265 (1987). f. Johnson. Richard M. Attribute order effects in conjoint anaiysis. Paper presented at the Marketing Science Conference. Duke Univeaity (March 1989). 8. Mehta. Rqj B.. h1ota.c.William L. and Pavia. Teresa M. An analysis of completely unacceptable levels in conjoint analysis. Paper presented at the Marketing Science Conference, Duke University (Nuch 198%
D. R. WIllINK
9. Page, Albert L. and Rosenbaum, Harold F. Redesigning product lines with conjoint analysis: how Sunbeam does it. Journal of Product Innovation Managemen! 4:120-I37 (June 1987). IO. Wittink. Dick R. and Ktishnamurthi, Lakshman. Rank order preferences and the part-worth model. In Marketing: Measurement and Analysis. John W. Keon fed.). Providence. RI: The Institute of Management Sciences. 1981, pp. 8-20. II. Wittink. Dick R.. Krishnamurthi, Lakshman and Nutter, Julia 8. Comparing derived importance weights across attributes. Journal of Consumer Research 8:471-e (March 1982).
ed
uctLines
Audysk
A Comment
Dick R. Wittink
PaRe and Rosenbaum [Journal of Product Innovation Management 4;120137 (June 1987)J discuss the use of conjoint analy-
In a recent article,
sis at Sunbeam Corporation. Their article provides a nice illustration of a conjoint application to food processor appliance redesign decisions. In this note, Dick Wittink compares characteristics of Sunbeam’s approach to both established and more recent results reported in the conjoirtt literature.
Introduction Page and Rosenbaum [9], hereafter referred to as PR, describe in considerable detail how Sunbeam Corporation used conjoint analysis in 1983 to help management redesign product lines for food processors. There are several excellent features of the application, including the use of pictorial representations, the collection of data in four geographic locations, the use of simulations to predict market shares for alternative product lines and the adjustment in market share predictions for brand differences in consumer awareness and availability. On the other hand, some of the features of their study deserve reconsideration. In this note, I discuss why and how these features could be modified.
Study Design Consumer preferences are obtained for twentyseven (hypothetical) food processors, a subset of all possible objects selected by PR based on a fractional factorial design. Such designs are commonly used by conjoint analysts for full-profile respondent evaluations. However, respondents are unlikely to keep track of twelve attributes in their evaluation of twenty-seven objects.’ Green and Srinivasan [5, p. 108) mention that “. . , the full-profile procedure is generally confined to, at
.4ddress correspondence to Dick R. Wittink. Johnson Graduate School of Management, Cornell University, Malott Hall, Ithaca. NY 14853. 1989 Elsevier Science PublishingCo., Inc. 655 Avenue of the Americas, New York, NY lOOlO
I One might argue that in the marketplace consumers face the problem of choosing among products that may differ on more than twelve attributes. However, they certainly do not attempt to rank order twenty-seven products while keeping track of twelve or more attributes. 0737-6782/89/53.50
*xl
J PROD INNOV 1989:6:?89-92
Dick R. Wit&k
D. R. WITTINK
MANAG
is Professor of Marketing and Quantitative Methods
at the Johnson Graduate School of Management. Cornell University. He received a Ph.D. in Industrial Administration from Purdue University in 1975. Previously. he was a member of the business school Faculty at Stanford University. where he started his tirst cot ,vint research project. l%is comment was written while he was d visiting professor of marketing at the Kellogg Graduate School of Management. Northwestern University. Other conjomt papem he has written apwar in inurnal of Marlieting. Journal nai of Consumer
Research
ofMarAwing
and Marketing
Research.
Jour-
Science. He frequently
discusses conjomt applications in executive programs sponsored by universities as well as firms such as AT&T
and A.C. Nielsen (Eu-
rope).
most, five or six factors. . . .” They recommend the use of bridging procedures for problems with more attributes to avoid information overload for respondents. They cite two studies (p. 109) showing better prediction or fit for the tradeoff matrix approach (versus full profiles) when the number of attributes is large (eight or nine),? The use of pictorial representations could reduce this information oL:erIoad problem. However, four of the attributes used by PR are included only as verbal descriptions, while the other eight are also described next to the picture. It appears, therefore, that a bridging procedure should be used in this case. In order to be able to estimate a main-effects part-worth mode1 for each respondent most efficiently, PR use an orthogonal design. Green and Srinivasan [5, p. 1IO] advise caution when environmental interattribute correlations are high. For food processors, many of the features (e.g., motor power, number of speeds) must be positively correlated with price. However. the extent to which the objects are unbelievable also depends on the range of variation used in the study. For example, the first object shown on page 124 of their study has a price of $199.99 while the second object costs $49.99. At the same time. the lower-priced object has more attractive features. A comparison of thera two objects will produce substantial incredulity for respondents. AS evidence of this problem, consider the result [9, p. 1261 that cne segment prefers the !PB.W price. This result can only occur if respondents have not held all other things constant : The use of pictorial representations approach for data collection tnfehsible.
renders the tradeoff matrix
while evaluating the price information. For example, respondents IT,-v infer quality differences from the price levels and reconc’le the unbelievability of the objects. For verbal descriptions of objects, Acito [ll shows that conjoint results (e.g., derived attribute importances) may be affected by the order of the attributes. PR place the price above the picture of the food processor. The prominence of price may have increased its importance relative to what is obtained for other attributes. Johnson [7] has obtained results consistent with Acito’s findings. Green and Srinivasan [5, p. 11 l] recommend that the attribute order is randomized over respondents while keeping the order constant across the objects evaluated by a given respondent.
Measurement Scale The collection of rank order preferences is consistent with the initial use of conjoint measurement in marketing [S] and with commercial practice [2]. However, concern has been expressed about the comparability of results across attributes that are defined on an unequal number of levels. Currim et al. [3] found that all three-ievel attributes obtained systematically higher average importances than the two-level attributes. When they adjusted the results, the attribute that had achieved fourth place out of six became the most important attribute! Wittink et al. [l l] completed an experiment to show that a systematic difference is obtained for both full-profile and tradeoff matrix rank order data. They found that the relative importance of one attribute increased dramaticaiiy by adding two intermediate levels. Also, Wittink and Krishnamurthi [IO] showed that market share predictions differed systematichlly by using objects defined on common attribute levels across two experimental conditions. ? hus, both derived importances and market share predictions may be affected by differences in the ;rumber of attribute levels used. PR report that “the overall conjoint results along with those from the segmentation demonstrxted to SAC w ich features should be stressed . . . ” (p. 17.71. Thus, attribute importances played a substantial role in the identification of t!le features thought to be most relevant to consumers. interestingly, the average relative impor-
REDESIGNING PRODUCT LINES: A COMMENT
J PROD iNNOV MANAG r?@HdB-92
tance of the three-level attributes is I I .7% versus 3.0% for the two-level attributes (p. 125).
291
PR use MONANOVA [9, p. 1231 to obtain a partworth function separately for each respondent. Although a nonmetric procedure is theoretically correct given the collection of ordinal preferences, Green and Srinivasan [S, p. 1141 conclude that metric and nonmetric procedures provide comparable results. Thus, PR could have used least-squares regression co estimate part-worth functions more quickly and less costly. Also, Cattin and Wittink 123show that regression analysis had become the favored approach for parameter estimation in commercial practice.
conjoint design for all consumers, several problems can occur. One is that consumers may infer unintended quality differences from the prices. Another possible problem is that consumers may provide more careful and/or werent evaluations of the other attributes dependent upon the price. For example, a respondeI:r could sort the objects based on price and carefully evaluate only those within one or two subgroups. This suggests that there may be interaction effects (which are not estimable in a main-effects design). Alternatively, the preference data should be weighted more heavily for objects with “acceptable” price levels. In any event, the exercise of providing evaluations would be more interesting to respondents if as many objects as possible are potentially attractive/relevant to them.
New Developments
Conclusion
Since 1983
PR 191show a very interesting application of conjoint analysis. The application discussed has several excellent features. In this note, I have identified opportunities for improving the validity and usefulness of the approach. Some of these changes involve additional complexity in data collection and/or analysis. In that case management needs to judge whether the expected improvement in results is worth the additional cost. Importantly, my concern is not about the applicability of conjoint analysis to the product line redesign decision faced by Sunbeam. Indeed, the technique seems very appropriate for the problem. However, the specific manner in which the application is designed and executed may have substantial influence on the validity of results, and hence the long-run acceptance by management of this technique. It is my hope that this note contributes to the use of “best practice” for research relevant to product innovation.
Parameter Estimation
a few A respondent
a given a given
the food processor market contains three price points: less than $60.00, between $60.00 and $125.00, and over $125.00” (p. 122). Conceivably, some consumers only consider food processors at the highest price levels, while others may restrict their market choices to the lower end of the price range. in the . .
3For recent results see
et al. [4] and
attribute et al.
@I.
References Acito. Franklin. An investigation of some data collection issues in conioint measurement. 1977 Educurors’ Proceedings. Chicago: American Marketing Association 82-85. (19773. Cattin, Philippe and Wittmk, Dick R. Commercial use of conjoint analysis: a survey. Juwnal of ;2larkering 46:44-53 (Summer 1982). Currim, Imran S.. Weinberg, Charles B. and Wittink, Dick R. Design of subscription programs for a performing arts series. Journal of Consumer Research 8:67-75 (June 1981). Green, Paul E., Krieger. Abba M. and Bansal. Pradeep. Completely unacceptable levels in conjc int analysis: a cautionary note. Journal of Murkering Ressearc~ 25:293-300 (August 1988).
33
a.*
J PROD INNOV MANAG Ip$P;b:289-92
5. GQ YI Paul E. and Srini\asan. V. Conjoint analysis in consumet research: issues and outlook. Journal of ConsrtmerResear& 5:103-123 (September 1978). 6. Johnson, Richard M. Adaptive conjoint analysis. Sa~rt~)orh &&were Co&rem-e Proceedings 253-265 (1987). f. Johnson. Richard M. Attribute order effects in conjoint anaiysis. Paper presented at the Marketing Science Conference. Duke Univeaity (March 1989). 8. Mehta. Rqj B.. h1ota.c.William L. and Pavia. Teresa M. An analysis of completely unacceptable levels in conjoint analysis. Paper presented at the Marketing Science Conference, Duke University (Nuch 198%
D. R. WIllINK
9. Page, Albert L. and Rosenbaum, Harold F. Redesigning product lines with conjoint analysis: how Sunbeam does it. Journal of Product Innovation Managemen! 4:120-I37 (June 1987). IO. Wittink. Dick R. and Ktishnamurthi, Lakshman. Rank order preferences and the part-worth model. In Marketing: Measurement and Analysis. John W. Keon fed.). Providence. RI: The Institute of Management Sciences. 1981, pp. 8-20. II. Wittink. Dick R.. Krishnamurthi, Lakshman and Nutter, Julia 8. Comparing derived importance weights across attributes. Journal of Consumer Research 8:471-e (March 1982).