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
Average Driving Distance Average Ball Life Price
275 yards 54 holes $1.25
250 yards 36 holes $1.50
225 yards 18 holes $1.75
Obviously, the market’s “ideal” ball would be:
Average Driving Distance Average Ball Life Price
275 yards 54 holes $1.25
and the “ideal” ball from a cost of manufacturing perspective would be:
Average Driving Distance Average Ball Life Price
225 yards 18 holes $1.75
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 1.
Figure 1
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
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.
Figure 2a
Average Ball Life
| BUYER 1 | 54 holes | 36 holes | 18holes |
| 275 yards | 1 | 2 | 4 |
| 250 yards | 3 | 5 | 6 |
| 225 yards | 7 | 8 | 9 |
Average
Driving
Distance
Figure 2a
Average Ball Life
| BUYER 2 | 54 holes | 36 holes | 18 holes |
| 275 yards | | | |
| 250 yards | | | |
| 225 yards | | | |
Average
Driving
Distance
Both buyers agree on the most and least preferred ball. But as we can see from their other
choices, Buyer 1 tends to trade-off 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 1's rank orders.
Figure 3 shows one possible scheme.
Figure 3
Buyer 1 Average Ball Life
| BUYER 1 | 54 holes 50 | 36 holes 25 | 18 holes 0 |
| 275 yards 100 | (1) 150 | (2) 125 | (4) 100 |
| 250 yards 60 | (3) 110 | (5) 85 | (6) 60 |
| 225 yards 0 | (7) 50 | (8) 25 | (9) 0 |
Average
Driving
Distance
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 ball life reproduce the rankings for Buyer 1 in Figure 4a.
Figure 4a
Average Ball Life
| Buyer 1 | 54 hole | 36 holes | 18 holes |
| $1.25 | 1 | 4 | 7 |
| $1.50 | 2 | 5 | 8 |
| $1.75 | 3 | 6 | 9 |
Price
Figure 4b
Average Ball Life
| Buyer 1 | 54 holes 50 | 36 holes 25 | 18 holes 0 |
| $1.25 20 | (1) 70 | (4) 45 | (7) 20 |
| $1.50 5 | (2) 55 | (5) 30 | (8) 5 |
| $1.75 0 | (3) 50 | (6) 25 | (9) 0 |
Price
We now have in Figure 5 a complete set of values (referred to as “utilities” or “part-worths”) that
capture Buyer 1's trade-offs.
Figure 5
Average Driving Distance Average Ball Life Price
275 yards 100 54 holes 50 $1.25 20
250 yards 60 36 holes 25 $1.50 5
225 yards 0 18 holes 0 $1.75 0
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.
Figure 6
Distance Ball Long-Life Ball
Distance 275 250
Life 18 54
Price $1.50 $1.75
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.
Figure 7
Buyer 1 Distance Ball Long-Life Ball
Distance 275 100 250 60
Life 18 0 54 50
Price $1.50 5 $1.75 0
Total Utility 105 110
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.
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