So we just discussed using conjoint analysis to determine

which products that people like and

whether different segments might like different products from each other.

We can also use conjoint analysis to analyze the trade-offs

that people are willing to make among the different attributes.

Let's consider the example that I have on the screen in front of me.

We have a golf ball, its magnum force that equals 0.43.

Now recall where I get that, I get that from that data.

All right, so if you look at magnum force and over in the effect column,

it says 0.429.

So I am rounding that to 0.43.

Then it goes 10 yards further than the average ball.

That is 0.12.

And it's currently priced at $6.99 per pack.

And that is, if you look down at $6.99, 0.216 or 0.22.

To get the overall utility of the product I simply add those together and

that equals 0.77.

Suppose I were to ask the question,

would the average golfer rather have a ball that drives an additional 5 yards,

so that would be going from 10 years further to 15 yards further or

a price reduction to $4.99 a pack?

Here's how I do that.

First I look at the utility associated with the 10 yards further and

the 15 yard further.

And if you look at you data that equals 0.12 and

the 15 yards further is 0.36.

I'm then going to compare that difference,

that's that additional happiness a person gets from the ball going a little

bit further, to the happiness that they would get if you reduce the price by $2.

In order to calculate that, I'm going to look at $6.99 a pack which is 0.22.

And then, if we drop it to $4.99 a pack, that's going to equal 0.696 or about 0.70.

What I then do is look at which difference is larger.

And in this case this is the larger difference, the 0.70-0.22.

In fact it's twice as big as this difference.

And recall this first difference 0.70-0.22

is coming from the spread of the utilities of the effects from the price attribute.

So what we can say is, most golfers would prefer a $2 decrease

in a price relative to a ball that goes a little farther.

And again I'll just emphasize that in each of these examples I give you,

you can calculate this at the population level.

And you can also calculate it for

different segments you might be interested in.