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. 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 preferences for a product and the likelihood to purchase it are in proportion to the utility and individual gains from the product.

There are three phases in the analysis of conjoint data:

1.       1.Collection of trade-off data through a questionnaire.

2.       2.Statistical analysis of the data, and

3.       3.Market simulation.

Data collection the participants are first asked to rank order their preference for the various levels within each attribute. This is especially important when the preference for various levels may not be “linear”—rising steadily from the lowest to the highest level within an attribute. Next, they are presented with different levels within the same attribute and asked how important the difference between the levels is to them.Respondents are asked which of the two company descriptions they would be more likely to prefer. In the next step of the questionnaire, survey participants are presented with pairs of market research company profiles (conjoint tasks).Finally, participants are presented with five composite market research companies containing all four attributes and they are asked to express the likelihood they would hire such a company on a 100-point scale.

Statistical analysis of 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.

 

In the above project, the attribute “Price” showed the greatest range with the resulting average importance score of 5.1, while the attribute “Location” showed the smallest range with the resulting average importance score of 1.9. This does not mean that “Price” is  more important than “Location.”

This observation is better interpreted as meaning that, on average, respondents perceived that the difference between a price of “10% more than you’d expect to pay” and a price of “10% less than you’d expect to pay” was more important than, with regard to location, the difference between working with a “Midtown Manhattan—Concrete & Cabs” company or working with a “Rural Idaho—Mountains & Elk” company. The table below illustrates the utility values for these two attributes and the increased utility associated with “Price” compared with “Location.”

Market Simulation:

Market simulation consists of describing each market research company profile in terms of its attributes, adding up the respondent’s value for all of a company’s attributes and using thisinformation to determine the relative value of each company to each respondent.

To illustrate such an attribute sensitivity simulation, assume a base case was established which reflected a market research company with an “average” likelihood of acceptance across allrespondents based upon their average preferences and utilities. A simulation could be run in which the sensitivity of the various levels of the attribute “Location” might be measured, resulting in the above breakout.

Posted By : - 

Siva Tejaswi Vepuri (13173)

Operations Grp 1

Which Conjoint Should I Use?

Conjoint analysis has become one of the most widely used quantitative tools in marketing research. According to recent surveys, the estimate is that around 8,000 to 10,000 conjoint studies are conducted each year by customers of sawtooth software (which produces conjoint analysis software) alone. When used properly, it provides reliable and useful results. There are multiple conjoint methods. As seen in one of the previous blogs, various types of conjoint are as follows:

1) CSC – Card Sort conjoint (a traditional conjoint technique)

2) ACA – Adaptive conjoint analysis

3) CBC – choice based conjoint

4) ACBC – Advanced Choice based conjoint

It is not an easy task to choose one of the methods of conjoint that adequately reflects how buyers make decisions in the actual marketplace. This includes not only the competitive context, but the way in which products are described (text), displayed (multi-media or physical prototypes), and considered.

Some key decision areas and how they affect choice of conjoint method are as follows:

1) Number of Attributes: If you need to study many attributes (especially eight or more), ACA historically was considered a solid approach. More recently, ACBC seems more effective—especially for projects involving price as an attribute. Three or fewer attributes would favor CBC.

2) Mode of Interviewing: In many cases, survey populations don't have access to computers. If your study must be administered paper-and-pencil, first consider using CBC; with CSC also being a option under conditions of very small sample size (see below).

3) Sample Size: If you are dealing with relatively small sample sizes (especially less than 100), you should be cautious about using CBC, unless respondents are able to answer more than the usual number of choice tasks. ACBC and the older ratings-based approaches (such as ACA and CSC) are able to stabilize estimates using relatively smaller samples than CBC. If interviewing must be done on paper, and very small sample sizes are the norm, consider CSC.

4) Interview Time: If you only have a few minutes to use in conjoint questions, CBC is a good alternative, though you may need to compensate for the limited information from each individual by sharply increasing the sample size. With about eight or more minutes available, ACBC is feasible.

5) Pricing Research: If studying price, CBC and ACBC are generally preferred.

By:

M Aditya kiran (13064),

Operations group 3