Monday, 5 September 2011

CONJOINT ANALYSIS

Note on Conjoint Analysis

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. For example, a car has attributes such as color, price, size, miles-per-gallon, and model style. 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 preference for a product and the likelihood to purchase it are in proportion to the utility an individual gains from the product.

There are three phases in the analysis of conjoint data: collection of trade-off data through a questionnaire, statistical analysis of the data, and market simulation. For purposes of this methodological description, imagine that you are researching perceptions of market research companies.

Statistical Analysis of the 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.

A respondent’s “utility” is a measurement of his or her relative strength of preference for each level of each attribute of the research company. The respondent’s utilities are estimated using a “least squares updating” algorithm. Initial estimates of utilities are based on the respondent’s rank orders of preference and his or her ratings of attribute importance. Estimates are updated following each trade-off task, and the initial estimates have decreasing influence as the interview progresses. The final estimates are true least squares, with the same weight being applied to each of the respondent’s answers. For the purposes of modeling, the final estimates are scaled so that the sum of each individual’s utilities predict most accurately that person’s likelihood of hiring a particular company. Utilities scaled in this way are ideal for predicting the likelihood of acceptance; they can be very misleading when reported in the aggregate or for comparing segments. For these purposes, utilities are re-scaled in such a way that the sum of the differences between the maximum and minimum level of each attribute equals the number of attributes times 100. This method assures that all survey respondents’ utilities are equally rated in reports and analyses.

The best way to interpret utilities involves analysis of the gaps between utility levels within an attribute. This “gap” or range between utility levels within an attribute indicates that the survey participants see greater importance between certain attribute levels than between other attribute levels. As a result, these attributes with greater ranges are used to differentiate or discriminate between different market research companies.

Conjoint Analysis has been a standard market research technique regularly employed since 1971. It is generally inappropriate for products which are evaluated by consumers on the basis of their “image”, such as beer or cigarettes, rather than on the basis of their constituent attributes. The technique has been successfully employed in hundreds of studies to predict preference for transportation services, financial services, automobiles, consumer durables, and many other industrial and consumer products and services.

Name : Varun Deshpande

Marketing Group 6