Conjoint analysis is a statistical technique used in market research to determine how people value
different features that make up an individual product or service.
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-worths) can be used to
create market models that estimate market share, revenue and even profitability of new designs.
As for the example taken up in the class, regarding purchase of a laptop, what are the factors
that affect the buying decision.
File-New-Data
Data-Orthogonal Design-Generate
8 combinations are generated
Orthogonal Design helps reduce number of combinations
different features that make up an individual product or service.
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-worths) can be used to
create market models that estimate market share, revenue and even profitability of new designs.
As for the example taken up in the class, regarding purchase of a laptop, what are the factors
that affect the buying decision.
- Price
- Battery Life
- Weight
What are the different levels of the factors?
- Price
- 15000
- 20000
- Battery Life
- 2hrs
- 4hrs
- Weight
- 3kg
- 5kg
That leaves us with the total combination of 8
Rank them from 1-8, from least preferred to most preferred
Rank them from 1-8, from least preferred to most preferred
File-New-Data
Data-Orthogonal Design-Generate
8 combinations are generated
Orthogonal Design helps reduce number of combinations
Utility was calculated to ascertain the most important factors to least important factors for a subject.
This would help the marketers to understand which type of laptop would suit which group of people.
Another example taken up was that of Choice of Job, what you want most in a job?
Following factors were identified by the class:
Another example taken up was that of Choice of Job, what you want most in a job?
Following factors were identified by the class:
- Industry
- Company
- Salary
- Satisfaction
- Growth
- Work Life Balance
- Job Security
Based on several levels, the total number of combinations came out to be 2304, further reducing
the levels and using orthogonal design, the number of combinations finally came down to be 128.
the levels and using orthogonal design, the number of combinations finally came down to be 128.
Based on the ranking, the utility value was computed and percentage was calculated.
Final summary was computed for the class as a whole - Importance Summary
Importance summary generated several graphs for individual subjects and the class as a whole for individual factors.
This summarized information would help us in knowing what kind of a job offer will be most interesting for a subject.
This summarized information would help us in knowing what kind of a job offer will be most interesting for a subject.
Supriya Suman
14171, Operations
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