Conjoint Analysis

Itsubo N, Sakagami M, Washida T, Kokubo K, Inaba A (2004) Weighting across safeguard subjects for LCIA through the application of conjoint analysis. Int J LCA 9(3) 196—205... 

Angela Dean is Professor in the Statistics Department at The Ohio State University. Her research interests are in group screening, saturated and supersaturated designs for factorial experiments, and designs for conjoint analysis experiments, in marketing. 

After processes are documented, they have to become as fast, efficient, and flawless as possible. This means you optimize the processes that generate all the value for your new solution. Several techniques will help you do this, but you should start with Measurement Systems Analysis, because it ensures the validity of any data you use in optimization studies (see the Design of Experiments, and Conjoint Analysis techniques). Then use Work Cell Design and Mistake Proofing to optimize the layout of people, machines, materials, and other factors in an office or factory. 

Conjoint Analysis is a simplified experimental technique for determining the best combination of attributes to include in a product or service design—Abased on the tradeoffs customers are willing to make. For example, you could have a new laptop computer that gives more benefits and costs less than what competitors offer. But before you release it you might want to find out what customers prefer in terms of the product s attributes, and what price they are willing to pay for them. 

Originating in mathematical psychology, Conjoint Analysis was developed by marketing professor Paul Green at the Wharton School of the University of Pennsylvania. 

To elegantly present the results of a Conjoint Analysis, the values derived from respondents can be converted to a market simulator what-if tool). Some software programs worthy of investigation are SAS, SPSS, and Sawtooth. 

Try this supplemental paper if you need more on Conjoint Analysis ... 

InteUiQuest (1990), Conjoint Analysis A Guide for Designing and Integrating Conjoint Studies, Marketing Research Technique Series Studies, American Marketing Association, Market Research Division, TX. 

Conjoint analysis (CA) is one of the most powerful techniques available for qualifying patient or physician preference and satisfaction (Ryan and Farrar 2000 Ryan et al. 2008). It provides a structured framework to elicit preferences that consists of the following steps ... 

Johnson FR, Lancsar E, Marshall D, Kilambi V, Miihlbacher A, Regier DA, et al. Constructing exp>eriinental designs for discrete-choice experiments Report of the ISPOR conjoint analysis experimental design good research practices task force. Value Health 2013 January 16(1) 3-13. 

Ryan M, Farrar S. Eliciting preference for healthcare using conjoint analysis. BMJ... 

Ryan M, Hughes J. Using Conjoint analysis to assess women s preference for miscarriage management. Health Economics 1997 6(3) 261-273. 

Gofman, A. and Moskowitz, H. R. (2010). Application of isomorphic permuted experimental designs in conjoint analysis. Journal of Sensory Studies, 25 (1), 127-145. 

Green, P. E. and Srinivasan, V. (1981). A general approach to product design optimization via conjoint analysis. Journal of Marketing, 45, 17—37. 

Conjoint analysis and related techniques are directly relevant to the marketing-operations interface because (1) they address the trade-off between customer satisfaction, which determines an upper-bound on product price, and the pro-duction/distribution cost of the product, and (2) they facilitate acquisition of empirical evidence to evaluate operational strategies. For instance, Lindsley et al., 1991 used conjoint analysis to quantify the tradeoffs that retailers are willing to make among price discoimt, speed of delivery, delivery reliability, number of titles carried in inventory, offered by book distributors. The customer satisfaction equation, estimated by the authors, suggests that a decrease in the discount rate of 0.38% offsets a one-day reduction in delivery time and an increase of 1% title filling rate saves 0.16% discount rate. The equation... 

In addition to collecting information in advance of a product launch, via conjoint analysis or other survey methods, it is possible to make use of postlaunch demand data to further understand customer preferences. The Internet, by allowing many options to be displayed and choices to be recorded, greatly facilitates this process. Since allowing customers to choose some features of the product to fit their needs best requires dramatic changes of the traditional design process, we will discuss this topic in the next section. 

Green, P.E., A.M. Krieger, Y. Wind. 2001. Thirty Years of Conjoint Analysis Reflections and Prospects. Interfaces 31(3) 56-73. 

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