Conjoint analysis
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==Basic Process== | ==Basic Process== | ||
The basic process for a conjoint analysis is as follows: | The basic process for a conjoint analysis is as follows: | ||
| - | * The product features that will be | + | * The product features that will be analyzed are determined |
| - | * Potential customers are shown numerous different sets of products. Each set includes several product profiles that have | + | * Potential customers are shown numerous different sets of products. Each set includes several product profiles that have multiple conjoined product features. |
| - | * For each set of products, they | + | * For each set of products, they select (by ranking, rating, or choosing) the profiles according to some overall criterion, such as preference, acceptability, or likelihood of purchase. |
* In the analysis of the data, part-worths are identified for the factor levels such that each specific combination of part-worths equals the total utility of any given profile. A set of part-worths are derived for each respondent. | * In the analysis of the data, part-worths are identified for the factor levels such that each specific combination of part-worths equals the total utility of any given profile. A set of part-worths are derived for each respondent. | ||
* The goodness-of-fit criterion relates the derived ranking or rating of stimulus profiles to the original ranking or rating data. | * The goodness-of-fit criterion relates the derived ranking or rating of stimulus profiles to the original ranking or rating data. | ||
* A set of objects are defined for the choice simulator. Based on previously determined part-worths for each respondent, each simulator computes an utility value for each of the objects defined as part of the simulation. | * A set of objects are defined for the choice simulator. Based on previously determined part-worths for each respondent, each simulator computes an utility value for each of the objects defined as part of the simulation. | ||
| - | * Choice simulator models | + | * Choice simulator models such as the first choice model, average probability model, and the [[logit model]] are used to estimate the utility of each product feature. |
==Data Collection Methods== | ==Data Collection Methods== | ||
Revision as of 22:47, 28 November 2006
Conjoint analysis, also known as multiattribute compositional models, is a statistical technique that was introduced into the marketing research community by University of Pennsylvania, Wharton professor Paul Green in the late 1960s. Since then, conjoint analysis has become the most popular multiattribute choice model in marketing. It can be used as a trade-off measurement technique to determine consumer preferences and buying intentions, and to also predict how consumers might react to changes in a current product or new product introduction.
Contents |
Basic Process
The basic process for a conjoint analysis is as follows:
- The product features that will be analyzed are determined
- Potential customers are shown numerous different sets of products. Each set includes several product profiles that have multiple conjoined product features.
- For each set of products, they select (by ranking, rating, or choosing) the profiles according to some overall criterion, such as preference, acceptability, or likelihood of purchase.
- In the analysis of the data, part-worths are identified for the factor levels such that each specific combination of part-worths equals the total utility of any given profile. A set of part-worths are derived for each respondent.
- The goodness-of-fit criterion relates the derived ranking or rating of stimulus profiles to the original ranking or rating data.
- A set of objects are defined for the choice simulator. Based on previously determined part-worths for each respondent, each simulator computes an utility value for each of the objects defined as part of the simulation.
- Choice simulator models such as the first choice model, average probability model, and the logit model are used to estimate the utility of each product feature.
Data Collection Methods
A full-factorial survey could be generated that tests every possible combination of attributes. However, in most cases this is not feasible due to the great amount of questions needed. It is also not even necessary, since orthogonal arrays can be used to reduce the number to a much smaller fractional-factorial design. This design can be generated using a program such as SAS Intstitute's SAS System.
The best combinations of attributes can be determined in several ways, including:
- Rating (e.g. rating with a scale from 1-10)
- Ranking (e.g. rank best as 1st, 2nd, 3rd, etc.)
- Choice (e.g. four options, which do you choose?)
The conjoint analysis can be done with different types of data, which include stated choice and revealed preference. The differences between the two are outlined below:
- Stated choice - Using data from a survey
Pros - controlled experiment Cons - survey item choices are not always the same as what is desired or already in the marketplace
- Revealed Preference - Using data collected from actual results in the marketplace
Pros:
Simulation Analysis
The most common simulator models include the first choice model, the average choice (Bradley-Terry-Luce) model, and the Logit model. The First choice model identifies the product with the highest utility as the product of choice. This product is selected and receives a value of 1. Ties receive a .5 value. After the process is repeated for each respondent's utility set, the cumulative "votes" for each product are evaluated as a proportion of the votes or respondents in the sample. The Bradley-Terry-Luce model estimates choice probability in a different fashion. The choice probability for a given product is based on the utility for that product divided by the sum of all products in the simulated market. The logit model uses an assigned choice probability that is proportional to an increasing monotonic function of the alternative's utility. The choice probabilities are computed by dividing the logit value for one product by the sum for all other products in the simulation. These individual choice probabilities are averaged across respondents. In summary, while the literature shows the maximum utility (first choice model) to provide the best overall validation, choice behavior has a strong probabilistic component. We have not measured this component adequately, but instead attributed lack of validity to "noise", our inability to model information search and overload effects, and measurement error.
Example
External links
- What is conjoint analysis? A useful conjoint software site that explains conjoint analysis in detail.
- SAS Software Software for designing full-factorial and fractional factorial surveys.
Reference
- Green, P. E., V. Rao, and J. Wind, Conjoint Analysis: Methods and Applications, 1999, Knowledge@Wharton.
- Green, P. E. and V. Srinivasan (1978), "Conjoint Analysis in Consumer Research" Issues and Outlook", Journal of Consumer Research, Vol. 5, (September), pp 103-123.
- Luce, R. D. and J. W. Tukey. "Simultaneous Conjoint Measurement: A New Type of Fundamental Measurement," Journal of Mathematical Psychology, 1 (February 1964), pp 1-27.
