Conjoint analysis
From DDL Wiki
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. Conjoint analysis is often used today in new product design and advertising.
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.
- 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.
- This information is then incorporated into a new or existing product.
Data Collection
Instead of asking respondents what they prefer in a product directly, conjoint analysis presents to them potential product profiles that have specific combinations of attributes. These profiles can be evaluated 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?)
An example of a product profile using rating is shown below:
An example of a product profile using choice is shown below:
The conjoint analysis is usually done using stated choice, which is data collected using a survey. The advantage of this method is that it is a controlled experiment. However, the product profiles in the survey are not always the same as products in the marketplace and are also not always desired. Another disadvantage is that it measures what people say and not what they do; hypothetical results are not always true in real life.
An alternative to stated choice data is revealed preference data. This consists of using data that is collected from actual performance in the marketplace. The advantage of this is that the data is a real measure of how consumers act. However, there are problems with multi-collinearity - it is difficult to tell exactly why someone is choosing one product over another.
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 balanced and 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.
Simulation Analysis
There are many different simulator models available for use in estimating the utility function of the product attributes. These include the first choice model, the average choice model, and the logit model.
First choice model
For each respondent, the product with the highest utility is the product of choice and receives a value of 1. If there is a tie for the highest utility, both products get a value of 0.5. In the end the most votes for a product evaluated as a proportion of the number of respondents is the optimum product.
Average choice model
Also known as the Bradley-Terry-Luce model. The choice probability for a product is based on the utility of that product divided by the sum of all products in the simulated market.
Logit model
Uses an assigned choice probability computed by dividing the logit value of one product by the sum for all other products in the simulation. The individual choice probabilities are averaged across respondents.
Other models include the probit model, heirarchical bayesian, and random-utility models
External links
- SAS Software Software for designing full-factorial and fractional factorial surveys.
References
- What is conjoint analysis? By Sawtooth Software.
- Conjoint Analysis Tutorial From Bringham Young University's Institute of Marketing.
- 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.
