Sunday, 4 September 2011

Conjoint Analysis

Conjoint Analysis is a popular marketing research technique that marketers use to determine what features a new product should have and how it should be priced. Conjoint analysis became popular because it was a far less expensive and more flexible way to address these issues than the regular concept testing.
Simple Example:
Suppose we want to market a new golf ball. We know that there are three important product features:

Average Driving Distance
Average Ball Life

We further know that there is a range of feasible alternatives for each of these features, for instance:

Average Driving Distance Average Ball Life Price

275 yards 54 holes Rs 100

250 yards 36 holes Rs 250

225 yards 18 holes Rs 500

Obviously, the market’s “ideal” ball would be:

Average Driving Distance Average Ball Life Price

275 yards 54 holes Rs 100

However, the “ideal” ball from the manufacturing perspective would be:

Average Driving Distance Average Ball Life Price

225 yards 18 holes Rs 500

assuming that it costs less to produce a ball that travels a shorter distance and has a shorter life.

Here’s the basic marketing issue: We’d lose all our profits selling the first ball and the market wouldn’t buy the second. The most viable product is somewhere in between, but where?

Conjoint analysis lets us find out where.

We first provide the rankings for distance and ball life as shown:

Rank Average Driving Distance Rank Average Ball Life

1 275 yards 1 54 holes
2 250 yards 2 36 holes
3 225 yards 3 18 holes

Depending upon the rankings given to each of the different attributes, we can calculate the total utility that a buyer can derive from a ball that has those attributes. Thus a rational buyer would be one who would seek to maximize his utility. Same goes for the person who is responsible for manufacturing the golf ball. He would manufacture one which maximizes his utility. Also, a buyer (or manufacturer) will also calculate the utility difference between the various options that are available to him. This gives him a fair idea as to what is he sacrificing, to get the other.

These steps form the basics of conjoint analysis. Although trade-off matrices are useful for explaining conjoint analysis as in this example, not many researchers use them nowadays. It’s easier to collect conjoint data by having respondents rank or rate concept statements or by using PC-based interviewing software that decides what questions to ask each respondent, based on his previous answers.

As you may expect there is more to applying conjoint analysis than is presented here. But if you understand this example, you understand what conjoint analysis is and what it can do for you as a marketer.

Posted by
Saurabh Agarwal

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