## Tuesday, 6 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 is a less expensive and more flexible way to address these issues.
Let’s understand Conjoint Analysis with the help of a simple example.
Suppose we want to buy a laptop. We know from experience that it has 3 important product features – Weight, Battery Life and Price. We further know that there is a range of feasible alternatives for each of these features, for instance
Weight Battery Life Price
3kg 5 years Rs 20,000/-
5kg 4 years Rs 35,000/-
6kg 2 years Rs 40,000/-

Obviously, the market’s “ideal” laptop would be
Weight Battery Life Price
3kg 5 years Rs 20,000/-
and the “ideal” laptop from a cost of manufacturing perspective would be:
Weight Battery Life Price
6kg 2 years Rs 40,000/-
Now the problem here would be that it would be quite easy to sell the first laptop whereas no one would buy the second one. The most viable product would lie somewhere in between. This is what Conjoint Analysis lets us find out.
A traditional research project might start by considering the rankings for Weight and Battery life
Fig 1
Rank Weight Rank Battery Life
1 3 1 2
2 5 2 4
3 6 3 5
This type of information doesn’t tell us anything that we didn’t already know about which laptop to buy.
Now consider the same two features taken conjointly. The two figures below show the rankings of
the 9 possible products for two buyers assuming price is the same for all combinations

Weight Battery Life
5 years 4 years 2 years
3 kg 1 2 4
5 kg 3 5 6
6 kg 7 8 9

Weight Battery Life
5 years 4 years 2 years
3 kg 1 3 6
5 kg 2 5 8
6 kg 4 7 9

Both buyers agree on the most and least preferred laptop but as we can see from their other choices, Buyer 1 tends to trade-off Battery life for Weight, whereas Buyer 2 makes the opposite trade-off.
The knowledge we gain in going from Figure 1 to Figures 2 and 3 is the essence of conjoint analysis
Next, we figure out a set of values for Weight and a second set for Battery Life so that when we add these values together for each laptop they reproduce Buyer 1's rank orders. Now, we figure out the trade-offs Buyer 1 is willing to make between Battery life and price.
Finally, we get a complete set of values (referred to as “utilities”) that capture Buyer 1's trade-offs.
We use this information to determine which laptop to buy based on the estimates of preferences of the buyers.