Obtaining Willingness to Pay from Surveys

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Do curso por University of Virginia

Customer Value in Pricing Strategy

84 ratings

University of Virginia

Customer Value in Pricing Strategy

84 ratings

Curso 2 de 4 em Specialization Pricing Strategy Optimization

The traditional approach to pricing based on costs works to pay the bills, but it leaves revenue on the table. You can, in fact, price your products in a way that increases sales--if you know what your customers are willing to pay and can leverage psychology to create better deal and discount plans. In this course, we'll show you how to price a product based on how your customers value it and the psychology behind their purchase decisions. Led by Darden faculty and Boston Consulting Group global pricing experts, this course provides an in-depth understanding of value-based pricing and how to use it to capture more revenue. By the end of this course, you'll be able to... -- Apply knowledge of customer value to price products -- Leverage core value-based pricing techniques to inform pricing decisions -- Measure customer willingness to pay using models (surveys, conjoint analysis, other data) -- Use knowledge of consumer psychology to set prices beneficial to both consumers and sellers

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Measuring Customer Preferences

As you learned in Week 1, understanding customer willingness to pay (WTP) is critical for effective pricing. This week, we'll show you two ways to measure willingness to pay: surveys and conjoint analysis. You'll see how one company, Adios Junk Mail, used surveys to better understand WTP. Conjoint is a terrific tool, and we'll walk you through how it's used to determine product preferences and prices. You'll finish the week with a solid understanding of how to measure customer preferences and use this information in your pricing strategy.

Module Introduction & Overview of Methods to Assess WTP1:43

Obtaining Willingness to Pay from Surveys6:27

Interpreting Combined Survey Data6:19

Case Intro: Adios Junk Mail0:58

Case Debrief: Adios Junk Mail1:31

Conheça os instrutores

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

Hi, everybody.

I know Thomas talked to you about willingness to pay, and you can imagine

that something you really want to find out when you're making a pricing decision,

how much of these customer's really willing to pay for what you're selling.

It's now time in the course to get down to the hard work of measuring this, and

we're going to start with surveys.

Why surveys?

Surveys or many respects the easiest way to measure willingness to pay.

They are quick, they are relatively cheap and sometimes they can give us very good

information about that willingness to pay metrics.

So let's just dive in on this.

So one way to try to find out willingness to pay, is just to ask people directly.

How much are you willing to pay for that?

And someone would say, hey, I know how much I'm willing to pay,

I'd pay $850 for that bicycle.

And that may be true, but when they answer the question,

they may have other things on their mind as well.

Think about it for a moment.

If you thought [SOUND], whatever answer I give might influence

the price that the individuals going to charge for it.

So if I really want to buy the object, whatever they happen to be selling,

maybe I should low ball them.

Telling them I'm willing to pay less.

And says, hmmm, better tell them I'm only willing to pay $500.

And maybe they'll lower the price.

So a couple of things come into play here.

One is exactly what we're talking about here.

People might shave the price down

with the hope that they can get a better deal on whatever you're selling.

It's also true that sometimes people don't really

know exactly how much they be willing to pay when you ask them on a survey.

And only when they get to the point of making a choice do they really discover

to themselves how much they'd be willing to pay for whatever they're looking at.

We're going to try to make this a little bit better in the survey.

We're going to try to get rid of at least this problem

that we're talking about right here.

How are we going to do that?

Well, instead of asking people directly, how much would you be willing to pay.

We're going to ask them two questions, and

from those questions we're going to be able to deduce the populations or

the segments that we're after, willingness to pay.

So let's start with the question that looks like this, at what

price would this product be so inexpensive that you would doubt it's quality.

Now, what is that mean?

Well, I imagine you had shopping situations like this in your life where

you go into a store and maybe there's a wool sweater and they're charging $29 for

it and you're thinking, well, $29 for a wool sweater.

There is no way that can be a decent wool sweater.

It must be thin, there's something must be going on.

It's gotta be defective.

So there are prices at which, for some and she said,

if the number is below that, I doubt it's quality so much that really even,

I wouldn't consider buying it even though it looks like a good deal.

So you can ask that question of people and people will

give you responses on surveys and you can build up data that looks like this.

Now, what is this data tell you?

Well, over here on the left hand side we have these minimum prices.

These are prices that different people have written down in

terms of their minimum willingness to pay.

The price below which they would question the quality of the particular object.

So we have some prices sitting down here, and

then here we have the number of people who answered each of those different prices.

Obviously this is an example, so they're kind of small numbers here for

the responses, and each of these numbers represents a certain percentage

of the overall response, and that's given right over here in this column.

And then, finally,

the interesting column is this cumulative percentage column over here on the right.

What does that mean?

It means that if you charge $1 for this item only 9.1% of your

target population would do that is, acceptable.

Most people the vast majority would people say $1 is just too low, the vast majority

wrote down a larger number is that minimum for us hold on their survey.

If you get up to here, let's look at $10 and that's about 81.8%.

What does that mean?

It means for 81.8% of your target population $10 is an acceptably low price.

That means it might be cheap to them but it's okay, they wouldn't doubt the quality

and if you get all the way up here to $25 100% of

the target population says, if I see $25, I'm not going to question the quality.

So I hope this is clear to you.

This is something that you can grab the PowerPoints and

look back at this to make sure the numbers make sense.

But that's what this is, these are acceptably low prices,

now the other side of that coin is what price would this product be so

expensive that it wouldn't be worth it?

At what price is just too high?

And I don't care how much I like it,

I'm just not going to pay that amount of money.

So people write down their maximum prices.

And those were given over here in the left-hand column.

There there's frequencies again and

those percentages calculated in just like they were before.

But let's think about what this cumulative percentage column means over

here this time.

That means what percent of the population would find that number, just too high.

If you get $35, it's 45.5% of you target population.

If you're up to $55 no one is going to be willing to pay that much.

You have 100% of your population if you are all the way down at $20 it's only

18.2%.

So if you do some quick math here you know 82% of those just 100 minus 18

would view that is acceptable.

Now, what we have is, how many people think it's too low, and

how many people think a particular price is too high?

We can combine that information in a way that's really going to help us get

at the overall population's willingness to pay.

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