The term ‘conjoint analysis’ implies that while human beings would love to eat their cake and have it too, they often settle for one or the other, or a little less of both because of money, time or other constraints. In other words, they trade one benefit for another, depending on which is more important to them.
Let us take a light-hearted example. When looking for a spouse, a man may ideally want a wife who looks gorgeous, sings beautifully, and is kind and considerate. As time goes by, he may decide that no such lady exists, or he may realise that such a lady, if indeed she exists, would not give him a second look. So, he would need to compromise on his ideal.
He may decide that kindness matters a whole lot more than appearances, or that musical talent is slightly more important to him. He may therefore get married to a lady who sings very well, and whose face hides a heart of gold. Or to someone who cannot attract an audience with her singing but is kind and considerate.
Varying trade-offs
The exact nature of the trade-off is a highly individual decision and will vary from person to person.
Let us now take a more serious example. A car buyer could trade mileage for seating capacity, or power for mileage. This could be because no one has made a car which has a mileage of 40 kmpl, 2,000 horsepower, and can seat a family of eight in comfort. Or it could be because such a car does exist, but our buyer cannot afford it.
Conjoint analysis basically tries to quantify this trade-off.
To continue with the car example, here are some attributes and their levels:
a. Mileage: 10, 12, 14, 16, 18
b. Power: 300, 400, 500, 600, 700
c. Seating capacity: 4, 5, 6, 7
There are numerous possible product configurations possible in theory from the above. Since we cannot research all these combinations with consumers, we use the conjoint analysis package to generate something called an ‘ortho-plan’.
Ortho-plan and utilities
An ortho-plan selects a manageable number from the possibilities. The manageable number could be 18, 20 or so; the software package will decide that. It is important that we use the ortho-plan package to generate this manageable number and not do it ourselves, as this may make analysis difficult later.
The respondents are shown these configurations and asked to rank or rate them in terms of preference and liking. That data is fed into the conjoint package and the output is a series of numbers called utilities and importance values; thus, we will know how important mileage is with respect to power and with respect to seating capacity. We will also know, within each attribute, which level has the maximum utility from the consumers’ point of view.
These utilities can be used directly to predict a liking score for each of the theoretically possible configurations. The manufacturer can then decide where the cost of manufacturing and the liking score have an optimum fit, and that will most probably be the configuration to hit the market.