Market Research with Conjoint Analysis

Market research is frequently concerned with finding out which characteristics of a product or service is most important to consumers. The ideal product or service, of course, would have all the best characteristics, but realistically, tradeoffs have to be made. The product with the most expensive features, for example, cannot have the lowest price.

Conjoint analysis is a technique for measuring consumer preferences about the attributes of a product or service. There are two general approaches to collecting data for conjoint analysis—the two-factor-at-a-time tradeoff method and the multiple factor full-concept method. With the tradeoff method, respondents are asked to rank the cells of a series of matrices, each matrix crossing the levels of one factor with the levels of another.

Why Use Conjoint Analysis?

• Effective market research is integral to the design, manufacture, and sale of successful products. It identifies the needs and wants of target markets, ensuring that products will sell because they meet the needs of buyers.
• Conjoint analysis is a market research tool for developing effective product design.
• Using conjoint analysis, the researcher can answer questions such as: What product attributes is important or unimportant to the consumer? What levels of product attributes are the most or least desirable ones in the consumer’s mind? What is the market share of preference for leading competitors’ products versus our existing or proposed product? Answers to these questions are of crucial importance in the design and launch of a successful product.
• The virtue of conjoint analysis is that it asks the respondent to make choices in the same fashion as the consumer presumably does—by trading off features, one against another.

For example, suppose that you want to book an airline flight. You have the choice of sitting in a cramped seat or a spacious seat. If this were the only consideration, your choice would be clear. You would probably prefer a spacious seat. Or suppose you have a choice of ticket prices: $225 or $800. On price alone, taking nothing else into consideration, the lower price would be preferable. Finally, suppose you can take either a direct flight, which takes two hours, or a flight with one layover, which takes five hours. Most people would choose the direct flight.

Steps In The Application Of Conjoint Analysis

The main steps involved in the application of Conjoint Analysis are following:

1. Determination of the salient attributes for the given product from the points of view of the consumers

2. Assigning a set of discrete levels or a range of continuous values to each of the attributes.

3. Utilizing Fractional Factorial Design of Experiment for designing the stimuli for experiment.

4. Physically designing the stimuli

5. Ranking or Rating data collection

6. Conjoint analysis and determination of part worth utilities.

7. Applying conjoint analysis output for different marketing decisions

How does Conjoint Analysis Work?
Conjoint analysis involves the measurement of consumer preferences, or acceptability between choice alternatives. The name "Conjoint Analysis" implies the study of the joint effects. In marketing applications, we study the joint effects of multiple product attributes on product choice. When asked to do so outright, many consumers are unable to determine the relative importance that they place on product attributes. For example, when asked which attributes are the more important ones, the response may be that “they all are important”.

It is difficult for a survey respondent to take a list of attributes and mentally construct the preferred combinations of them. The task is easier if the respondent is presented with combinations of attributes that can be visualized as different product offerings. Fortunately, conjoint analysis can facilitate the process. Conjoint analysis is a tool that allows a subset of the possible combinations of product features to be used to determine the relative importance of each feature in the purchasing decision; the relative values of attributes considered jointly can better be measured than when considered in isolation.


Author: Juhi Priyanka Kachhap (13136)

Group: Marketing - Group 4