Joseph Curry is a vice president of Sawtooth Software, a company that writes and markets microcomputer software for marketing research. Since 1978, Curry has been involved in the development of microcomputer software systems for interactive interviewing and data 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 concept testing.

The basics of conjoint analysis are not hard to understand. I'll attempt to acquaint you with these basics in the next 15 minutes so that you can appreciate what conjoint analysis has to offer. A simple example is all that's required. Suppose we want to market a new golf ball. We know from experience and from talking with golfers that there are three important product features:

  • Average Driving Distance
  • Average Ball Life
  • Price

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

Obviously, the market's "ideal" ball would be:

and the "ideal" ball from a cost of manufacturing perspective would be:

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 our shirts 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.

A traditional research project might start by considering the rankings for distance and ball life in Figure I.

This type of information doesn't tell us anything that we didn't already know about which ball to produce.

Now consider the same two features taken conjointly. Figures 2a and 2b show the rankings of the 9 possible products for two buyers assuming price is the same for all combinations.

Both buyers agree on the most and least preferred ball. But as we can see from their other choices, Buyer 1 tends to tradeoff ball life for distance whereas Buyer 2 makes the opposite trade-off.

The knowledge we gain in going from Figure 1 to Figures 2a and 2b is the essence of conjoint analysis. If you understand this, you understand the power behind this technique.

Next, let's figure out a set of values for driving distance and a second set for ball life for Buyer 1 so that when we add these values together for each ball they reproduce Buyer l's rank orders.

Figure 3 shows one possible scheme.

Notice that we could have picked many other sets of numbers that would have worked, so there is some arbitrariness in the magnitudes of these numbers even though their relationships to each other are fixed.

Next suppose that Figure 4a represents the trade-offs Buyer 1 is willing to make between ball life and price. Starting with the values we just derived for ball life, Figure 4b shows a set of values for price that when added to those for ball life reproduce the rankings for Buyer I in Figure 4a.

We now have in Figure 5 a complete set of values (referred to as "utilities" or "part-worths") that capture Buyer 1's tradeoffs.

Let's see how we would use this information to determine which ball to produce. Suppose we were considering one of two golf balls shown in Figure 6.

The values for Buyer 1 in Figure 5 when added together give us an estimate of his preferences. Applying these to the two golf balls we're considering, we get the results in Figure 7.

We'd expect Buyer 1 to prefer the long-life ball over the distance ball since it has the larger total value. It's easy to see how this can be generalized to several different balls and to a representative sample of buyers.

These three steps—collecting trade-offs, estimating buyer value systems, and making choice predictions—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.