Conjoint Analysis: Willingness to Pay

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Pricing Strategy in Practice

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University of Virginia

Pricing Strategy in Practice

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Курс 4 из 4 — Specialization Pricing Strategy Optimization

In this project-centered course, Darden's Ron Wilcox and BCG's Thomas Kohler will walk you through a real-world case, from problem statement to detailed analyses. You'll use all three lenses (cost, customer value, and competition) to recommend an optimal price—and then adjust to market disruptions. Utilizing the concepts, tools and techniques taught in previous Specialization courses—from basic techniques of economics to knowledge of customer segments, willingness to pay, and customer decision making to analysis of market prices, share, and industry dynamics—you will practice setting profit maximizing prices to improve price realization. You'll finish the course with a portfolio-building project that demonstrates your pricing prowess.

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Customer Value and Conjoint Analysis

This week, we will dig deeper into customer value using conjoint analysis to determine the price sensitivity of consumers and businesses. Thomas and Ron will show you how to graph the conjoint data to easily compare these two markets--and you'll do additional analysis of the conjoint data to learn more about what consumers value. Using your analysis, you'll hone your pricing recommendation.

Conjoint Analysis: Steps 1 – 38:01

Conjoint Analysis: Step 4 and Product Preferences7:24

Attribute Trade-offs2:45

Attribute Importances4:03

Conjoint Analysis: Willingness to Pay5:38

Познакомьтесь с преподавателями

Jean Manuel Izaret
Senior Partner and Managing Director, Leader of BCG’s Global Pricing Practice
Ronald T. Wilcox
NewMarket Corporation Professor of Business Administration & Senior Associate Dean for Degree Programs
Marketing
Thomas Kohler
Associate Director, Pricing
Marketing, Sales & Pricing Practice

So now we've come to the point in our analysis that we're going to

look at willingness to pay, right.

We've had a lot of build up to this point.

We've looked at willingness to pay with surveys,

and I've shown you a lot of other things that a conjoint analysis can do.

And I promised you can give you willingness to pay and it can.

So we're going to do an example of that right now.

So, let's look at a particular golf that we're considering we've got the magnum

force, ten yards further the average, 8.99 by now I think you know where these

utilities are coming from and that this is just the sum of all those utilities.

And we're going to ask ourself a question.

How much more would the average golfer be willing to pay for

an increase in distance of five yards?

Okay, so there it is.

It's a little more complicated than what we have done before, but

it's not a lot more complicated than what we have done before.

Here are the basic steps.

The first thing we need to do is calculate the utility gain for

the increase in distance.

So we're going to give the ball five more yards, our engineer said they can do that.

We need to figure out how much happier people will be with that new ball and

calculate it.

Then, we're going to find the new price.

And it's going to be a bigger price, right?

We're going to give them a better ball, we're going to charge a bigger price.

We need to find that new price such that we take back from the consumer the exact

amount of utility that we gave them, when we gave them that improved ball.

So, in that sense it's likely to break even, we give them some utility and

whatever that utility is we come over here to price and we take it back from them and

the difference between that new, price that we calculate, and

the old price is in really that increase in willingness-to-pay.

So, let's apply this to the data.

First of all, we're going to calculate the utility gain for the increase in distance.

And that's 0.36- 0.12, where does that come from?

Well if you look at distance 0.36 is that 15 yards further,

that's the new ball, right?

because our current ball is 10 yards further,

the new ball would be 15 yards further, that would give them a utility of 0.36 and

then we subtract the original utility which is that 10 yards further ball which

is 0.12, so we have a difference of 0.24.

So now what are we going to do?

We're going to take that 0.24 back in utility from the price.

And how do we do that?

Well we know we're going to take back 0.24, but

0.24 that difference is not exactly the same as the difference

between any of the price points that I've got in front of me, right?

In particular, we're charging $8.99 per ball and

we're asking how much more would people be willing to pay.

It would be very nice if the $10.99, the utility for

that was just 0.24 less than the 8.99, right.

And then we could just say it's $2 and it's very easy,

because we give them 0.24 in distance, we take 0.24 in utility on price, and

those numbers are just in front of us.

But usually it doesn't work out that way.

And in fact, I bet you can tell by looking at the data,

the new price is going to have to be somewhere between 8.99 and

10.99 because the spread of the utilities, which is the difference

between negative 0.83 and negative 0.08 is greater than 0.24.

So here's how we handle that.

We know the new prices going to be up in a 899-1099 range, we can tell

that because the .24 is smaller than the overall range between 8.99 and 10.99.

What we need to determine is exactly how far is that range we need to go.

And in an order to do that,

we essentially convert it into a percent along that range.

We do that by taking 0.24 and

dividing that by the spread in the two price points that we're interested in.

In this particular example that's 8.99 and 10.99 because we know that

price is going up that's why we're moving up to the 10.99 price point.

That's given right down here.

So the resulting calculation gives you 32%,

that means we're moving 32% up in that price range.

That's what we need to do In order to break even and

take back from consumers the exact dollar amount that's equivalent

to the utility gain that they get on the distance side.

I then take that 32% and multiply it by the difference between the two

price points, 10.99- 8.99, so $2.

And when I make that calculation I get $0.64, and how do I interpret $0.64?

That is the increased willingness to pay for

the increase in distance and you can do that for different segments.

You can do that for different balls that you might be considering.

Not just increase in distance.

You can even do it for decreases in attribute.

Suppose you take something away from consumers.

The exact same math will tell you how much less they would be willing to pay.

This is a very common and

very useful set of metrics that comes out of a conjoined analysis.

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