variety of choices to the customers from which they can choose their interests. This will help the company or the manufacturer to exactly know the preferences of the customers. This is also useful in finding the poor combination of attributes which will be a very beneficial for the company.
Conjoint analysis requires research participants to make a series of trade-offs. To make these revelations even better, the companies group the research participants into similar segments based on objectives, values and other factors.
For example, if we want to describe a mobile telephone in terms of attributes like weight, battery life and price, we give different choices to the participants in every attribute to know the preferences of the participants
.
For instance would you choose phone A or phone B?
attributes | Phone A | Phone B |
Weight | 200g | 120g |
Battery life | 21 hours | 10 hours |
Price | 7000 | 9000 |
The analysis from the above data can be as follows:
“Phone A is bulkier, but has the battery life and lower cost, but Phone B is smaller and neater yet more expensive and with lower battery life. Lighter weight is worth more than the loss of battery life, and it’s probably worth the extra 2000, so I’d choose B in this instance.”
By asking for enough choices (and with good design to minimise the number of choices you need to ask), the researcher can work out numerically how valuable each of the levels is relative to the others around it – this value is known as the utility of the level.
Posted by : Finance - 2
Author : Abhishek Reddy
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