Conjoint analysis is one of many techniques for dealing with situations in which a decision maker has to choose among options that simultaneously vary among two or more variables.
For businesses, understanding precisely how markets value different elements of the product and service mix means product development can be optimized and aspects such as pricing tuned to customer's willingness to pay for specific features. Conjoint analysis is both a trade-off measurement technique for analyzing preferences and intentions-to-buy responses and a method for simulating how consumers might react to changes in current product/services or the introduction of new products into an existing competitive array.
The principle behind conjoint analysis is to break a product or service down into its constituent parts then to test combinations of these parts to look at what customers prefer.
è There are three key design elements for conjoint analysis. Based on that there are different types of analysis.
· Adaptive Conjoint Analysis – ACA: ACA is one of two most common methods for carrying out conjoint analysis. The benefits of ACA are that it allows for a large number of attributes (up to 30) and levels (up to 7 per attribute) to be used.
· Choice Based Conjoint Analysis- CBC: The most common alternative to ACA is CBC. This uses the same over-arching principles as ACA. ACA has respondents selecting from products described with two or three attributes, CBC shows full descriptions using all the attributes available. In addition, CBC can show more than just two "products" at the same time, together with a none-of-these option enabling more realistic choice decisions to be evaluated.
· Discrete Choice Analysis: A more advanced form of choice-based conjoint is Discrete Choice Analysis. The main difference from CBC is the inclusion of continuous variables such as price and time.
· Full profile Conjoint Analysis: Full-profile is the original form of conjoint and is still in use, though predominantly in the US it would appear. Like CBC this uses a more limited number of attributes to describe the product or service, but sufficient cards or treatments are shown to one respondent to enable individual level utilities to be calculated. A fractional factorial design is used to specify a fixed set of profiles that need to be shown for analysis.
· These methods are rapidly used in health care sector to check any sort of variation in methods, terminology and quality across the applications.
· Conjoint has been one of the most documented methods in marketing research.
· Conjoint analysis has been applied to products and services (consumer and industrial) and to not-for-profit offerings as well.
· Today it is used in many of the social sciences and applied sciences including marketing, product management, and operations research. It is used frequently in testing customer acceptance of new product designs, in assessing the appeal of advertisements and in service design. It has been used in product positioning, but there are some who raise problems with this application of conjoint analysis.
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