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. 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 preferences for a product and the likelihood to purchase it are in proportion to the utility and individual gains from the product.
There are three phases in the analysis of conjoint data:
1. 1.Collection of trade-off data through a questionnaire.
2. 2.Statistical analysis of the data, and
3. 3.Market simulation.
Data collection the participants are first asked to rank order their preference for the various levels within each attribute. This is especially important when the preference for various levels may not be “linear”—rising steadily from the lowest to the highest level within an attribute. Next, they are presented with different levels within the same attribute and asked how important the difference between the levels is to them.Respondents are asked which of the two company descriptions they would be more likely to prefer. In the next step of the questionnaire, survey participants are presented with pairs of market research company profiles (conjoint tasks).Finally, participants are presented with five composite market research companies containing all four attributes and they are asked to express the likelihood they would hire such a company on a 100-point scale.
Statistical analysis of data:
Conjoint analysis applies a complex form of analysis of variance to a respondent’s choice task data to calculate a utility for each level of each attribute. These are basically index numbers which measure how valuable or desirable a particular feature is to the respondent. The idea is each respondent’s choice tasks reveal something about the relative utility that he or she has for each feature. Features which a respondent is reluctant to give up from one choice task to another are judged to be of higher utility to that respondent than features which are quickly given up.
In the above project, the attribute “Price” showed the greatest range with the resulting average importance score of 5.1, while the attribute “Location” showed the smallest range with the resulting average importance score of 1.9. This does not mean that “Price” is more important than “Location.”
This observation is better interpreted as meaning that, on average, respondents perceived that the difference between a price of “10% more than you’d expect to pay” and a price of “10% less than you’d expect to pay” was more important than, with regard to location, the difference between working with a “Midtown Manhattan—Concrete & Cabs” company or working with a “Rural Idaho—Mountains & Elk” company. The table below illustrates the utility values for these two attributes and the increased utility associated with “Price” compared with “Location.”
Market simulation consists of describing each market research company profile in terms of its attributes, adding up the respondent’s value for all of a company’s attributes and using thisinformation to determine the relative value of each company to each respondent.
To illustrate such an attribute sensitivity simulation, assume a base case was established which reflected a market research company with an “average” likelihood of acceptance across allrespondents based upon their average preferences and utilities. A simulation could be run in which the sensitivity of the various levels of the attribute “Location” might be measured, resulting in the above breakout.
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Siva Tejaswi Vepuri (13173)
Operations Grp 1