Conjoint Analysis is a research technique used to measure the trade-offs people make in choosing between products and service providers. It is also used to predict their choices for future products and services. Conjoint Analysis assumes that a product can be “broken down” into its component attributes. For example, a car has attributes such as color, price, size, miles-per-gallon, and model style. Using Conjoint Analysis, the value that individuals place on any product is equivalent to the sum of the utility they derive from all the attributes making up a product. Further, it assumes that the preference for a product and the likelihood to purchase it are in proportion to the utility an individual gains from the product.
There are three phases in the analysis of conjoint data: collection of trade-off data through a questionnaire, statistical analysis of the data, and market simulation. Conjoint analysis is based on the fact that the relative values of attributes considered jointly can better be measured than when considered in isolation.
Steps in Developing a Conjoint Analysis
Developing a conjoint analysis involves the following steps:
- Choose product attributes, for example, appearance, size, or price.
- Choose the values or options for each attribute. For example, for the attribute of size, one may choose the levels of 5", 10", or 20". The higher the number of options used for each attribute, the more burden that is placed on the respondents.
- Define products as a combination of attribute options. The set of combinations of attributes that will be used will be a subset of the possible universe of products.
- Choose the form in which the combinations of attributes are to be presented to the respondents. Options include verbal presentation, paragraph description, and pictorial presentation.
- Decide how responses will be aggregated. There are three choices - use individual responses, pool all responses into a single utility function, or define segments of respondents who have similar preferences.
- Select the technique to be used to analyze the collected data. The part-worth model is one of the simpler models used to express the utilities of the various attributes. There also are vector (linear) models and ideal-point (quadratic) models.
Depending upon the type of conjoint survey conducted, statistical methods like ordinary least squares regression, weighted least squares regression, and logic analysis are used to translate respondents' answers into importance values or utilities.
Group HR1
Author- Rupali Varshney
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