Conjoint analysis, also called Multi-attribute Compositional Models or Stated Preference Analysis, is a statistical technique that originated in mathematical psychology. Today it is used in many of the social sciences and applied sciences including marketing, product management, and operations research. It is not to be confused with the theory of conjoint measurement.
In conjoint analysis, the respondent may be asked to arrange a list of combinations of product attributes in decreasing order of preference. Once this ranking is obtained, a computer is used to find the utilities of different values of each attribute that would result in the respondent’s order of preference. This method is efficient in the sense that the survey does not need to be conducted using every possible combination of attributes. The utilities can be determined using a subset of possible attribute combinations. From these results one can predict the desirability of the combinations that were not tested.
The objective of conjoint analysis is to determine what combination of a limited number of attributes is most influential on respondent choice or decision making. A controlled set of potential products or services is shown to respondents and by analyzing how they make preferences between these products, the implicit valuation of the individual elements making up the product or service can be determined. These implicit valuations (utilities or part-worth’s) can be used to create market models that estimate market share, revenue and even profitability of new designs.
Conjoint analysis requires research participants to make a series of trade-offs. Analysis of these trade-offs will reveal the relative importance of component attributes. To improve the predictive ability of this analysis, research participants should be grouped into similar segments based on objectives, values and/or other factors.
It has become common practice to present the trade-offs as a choice exercise (where the respondent simply chooses the most preferred alternative from a selection of competing alternatives - particularly common when simulating consumer choices) or as a constant sum allocation exercise (particularly common in pharmaceutical market research, where physicians indicate likely shares of prescribing, and each alternative in the trade-off is the description a real or hypothetical therapy).
When there are many attributes, experiments with Conjoint Analysis include problems of information overload that affect the validity of such experiments. The impact of these problems can be avoided or reduced by using Hierarchical Information Integration.
§ Estimates psychological tradeoffs that consumers make when evaluating several attributes together
§ Measures preferences at the individual level
§ Uncovers real or hidden drivers which may not be apparent to the respondent themselves
§ Realistic choice or shopping task
§ Able to use physical objects
§ If appropriately designed, the ability to model interactions between attributes can be used to develop needs based segmentation
§ Designing conjoint studies can be complex
§ With too many options, respondents resort to simplification strategies
§ Difficult to use for product positioning research because there is no procedure for converting perceptions about actual features to perceptions about a reduced set of underlying features
§ Respondents are unable to articulate attitudes toward new categories, or may feel forced to think about issues they would otherwise not give much thought to
§ Poorly designed studies may over-value emotional/preference variables and undervalue concrete variables
§ Does not take into account the number items per purchase so it can give a poor reading of market share
Author: Krunal Patel
Group: Marketing - Group 4