Conjoint Analysis is a statistical method used to create market models that estimate market share, revenue and even profitability of new designs. In this case a set of attributes are given to respondents and they are asked to rate or rank these variables as per their preference. On the basis of these preferences the implicit valuation of the individual elements making up the product or service can be determined.
So one of the most important application of conjoint analysis is mapping customer demand. Conjoint analysis consists of generating and conducting specific experiments among customers with the purpose of modeling their purchasing decision. Attributes of a product or service that create value to customers can be divided into:
1. Factors that enhance customer’s benefits or help to satisfy his needs.
2. Factors that decrease customer’s costs.
When we ask any respondent about his prefernces, Customers usually name many factors as needs. It is reasonable to organize them into a hierarchic structure — as the first order, secondary and if necessary also the third level needs. For example if one would ask a customer to estimate the importance of “petrol consumption” and “rapid acceleration,” customers might state that both factors are extremely important to them. (And they are not lying, it is just the fallacy of the research method). As a result the car company would not be able to make a reasonable trade-off along these factors in designing its value proposal. That is why more innovative companies are beginning to use more sophisticated methods, like conjoint analysis for studying customer needs.
Many studies confirm, that compared to other wide-spread customer needs research methods the results obtained with conjoint method are more detailed, reliable and easier to understand.
Estimates psychological trade offs 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
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
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
Posted by: T M Prakash
Operations _ Group 2