Conjoint Analysis: Other Ways to Interpret Data

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Customer Value in Pricing Strategy

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University of Virginia

Customer Value in Pricing Strategy

124 ratings

Course 2 of 4 in the 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

From the lesson

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.

Conjoint Analysis Applications4:28

Why Conjoint?4:21

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

Conjoint Analysis: Other Ways to Interpret Data3:57

Conjoint Analysis: Propensity Modeling7:58

Meet the Instructors

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

So what else can we do with the conjoint analysis?

Well, we can do conjoint on different pre-specified groups of people.

Now if you want to do that, I need to remind you,

you have to ask questions of people upfront in the conjoint analysis

about themselves, in order to use those variables for segmentation later.

If you get to the end of the conjoint analysis without asking these questions,

then it's very difficult to do segment level analysis.

We can also get individual level estimates.

This is a relatively new phenomena, some improvements and

algorithms allow us to do that.

But really what it means at the most basic level,

is that Ron Wilcox can go through a conjoint analysis, answer some questions,

and the software can provide my utilities for the attributes.

And you can go through a conjoint analysis, and

it'll provide individual level estimates for you.

And sometimes that proves to be very valuable.

And finally, we can do something Propensity Modeling, which is trying to

predict what probability that someone will buy one product verses another product.

Now I want to show you what individual-level output looks like.

This is some real individual-level output from my research.

So you have respondents on your left-and side, just 1 through 14.

And I'm showing you just a small sub-sample of the overall data that I had.

And then over here, BMW Sedan, that's the utility for BMW Sedan.

BMW Coupe, Ford Focus Sedan, you can see that this respondent number

1 really liked BMWs and didn't like Fords very well.

But you can see that the utilities are all over the place,

depending on which particular respondent is taking the conjoint analysis.

You can use this information to

look at attribute importances at the individual level as well.

So the system will calculate that for you.

And sometimes this provides very interesting insight.

If you go down to respondent 13,

you can see they put a 30% decision weight on price.

Whereas if you look at respondent 11, they have more than

double the decision weight than respondent 13 just on price.

So price is a lot more important to them.

Now what was I doing with this kind of individual-level analysis?

Well, I'm just going to give you a brief overview of some research that I

was doing.

I was looking at student loan debt, to see if student loan debt increased or

decreased people's price sensitivity for a major purchase like an automobile.

Now, in a rational world, if you take on a lot of debt,

what you might expect is that you would have increased price sensitivity.

You have a lot of debt,

therefore you have to watch the amount of money that you're spending on a car.

In reality, what I found was just the opposite.

If someone takes on a lot of debt, even controlling for other factors,

this causes them to be less price sensitive with major purchases.

There's something in the psychology literature,

literally called the what the hell effect.

And what it means in this context is, once I'm in so

much debt, why don't I just go ahead and spend more money.

Yeah, I know the BMW is only $10,000 more than the Toyota,

but that's not a lot of money.

I already have $100,000 worth of student loan debt.

So that's what I was doing with my research, and

I also showed that if I took people's student loan debt and showed them

the monthly payments that were implied by that debt, that I reversed the effect.

That the increases in debt made people pay more attention to their car purchases,

and made them be more price sensitive.

Now what else can we do?

Well, those are pre-specified groups of people, the individual-level estimation,

and now what I want to move on to is propensity modeling.

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