Conjoint Analysis: Step 4 and Product Preferences

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From the course by University of Virginia

Customer Value in Pricing Strategy

92 ratings

University of Virginia

Customer Value in Pricing Strategy

92 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 Data3:56

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

All right, let's now take a look at some conjoint output.

What does the software actually give you after people have

made all of these choices?

Well, it gives you something that looks like that.

Wow!

So that's real conjoint analysis output.

That's generally the format that the output comes out in.

And I'd like to take this and interpret it a little bit and

find out what's useful here and maybe what's less useful.

The most important column in this output is right over here on the left-hand side,

it's called Effect.

Another word for that would be utility.

But in effect, each one of these numbers is a utility level for

each of the levels of the attributes listed on the right-hand side.

So here, we have a golf ball example.

And one of the attributes is brand.

And then I have these different brand names,

High-Flyer Pro, Magnum Force, Eclipse+ and Long Shot.

Those are the levels of the attribute brand.

And their associated effect, or utility, is right over here on the left-hand side.

And you can really interpret that as a happiness level.

So, larger positive numbers mean more happy.

And then negative numbers mean less happy.

Now, in most software output that does conjoint analysis,

the data has to be scaled.

And I'm just going to point out one issue with respect to scaling

because it's important as we interpret the data.

The software will usually scale conjoint analysis output such that the sum

of the utilities within any given attributes sum to zero.

Perhaps, you can see that just by looking at this.

Now, if you have trouble seeing the data on the screen, we've also put a paper

version of their electronic version of this in the resources.

So you can grab that and

look at that while you're looking at this particular segment of the course.

And that's actually a very easy way to do it.

Then you can look down at the data and look at the example that I'm presenting,

and kind of square the two, make sure they make sense to you.

But if you look at these effects over here for brand,

you see 0.56, 0.429, -0.38, -0.608.

If you add those up they will add up to zero.

And that is true for each of the other attributes.

This is a distance attribute, right?

How far does the ball go?

And finally, down here, we have a price attribute.

That's what we're really interested in.

And those effects or utilities add to zero.

Now some of you taking this course may have done some advanced work in statistics

or done a lot in terms of regression analysis.

And you would be familiar with the term, t ratios and standard errors.

If you're not familiar with them, don't worry.

All the examples that I'm going to do use only the effect, or only the utility, and

that's all you need to focus in on.

Conjoint analysis generally does give us output that tells something about t ratio,

which is a measure of whether this effects

are something called statistically significant.

Now, why do why not I think that is important here?

Every output from every conjoint analysis that I have ever seen,

that has been done in any kind of reasonable way,

has statistically significant output, okay?

In fact, if you had statistically insignificant output,

that would mean that the attributes that you put in the conjoint analysis were in

no way related to people's choices that they made.

That's very unlikely, okay?

Especially by people that have done a lot of conjoint analysis.

So, the practical reality here is while we have all of this output,

just by focusing in on the descriptors on the right-hand side,

the descriptions of the attributes and the levels.

And by focusing in on these numbers right here, which are those happiness levels,

we can really do the kind of interpretation we want to do.

Now, so what can we do with that kind of output?

We can do a number of different things.

We can determine which product people prefer.

And we can do that both for the population and for different segments for

the population.

If you've asked those segmentation questions as part of conjoint analysis.

You can look at trade-offs among different possible features.

You can determine the rank order of attributes in determining choice.

Which attributes are very important?

And can we quantify that?

And which attributes are less so in determining choice?

We can also, very important for this course, compute willingness to pay for

different design changes that we might be thinking about for our product.

And finally, we can do something called propensity modeling.

For each one of these items that I've listed here, I'm going to do an example.

And I'm going to start out with determining which products people prefer.

So here's how this is done in a conjoint analysis.

If you have two products and you ask yourself,

which product would people prefer, the nice easy part of conjoint analysis.

And this is one of the reasons it's become so popular.

Is that it is very easy to take the data that you have available to use,

those effects or utilities, and determine that.

And the way you do it, is by listing out the levels of each of the attributes for

the product that you're considering and look at its associated utility.

So, for High-Flyer, if you look at the data, you will see 0.56.

I'm having to round it, okay?

Being the utility level for High-Flyer.

5 yards further.

If you look down, you've got -.48.

Again, I'm rounding.

And $6.99 per pack, we have about .22.

And it says .21, but it says .216, so I'm just writing down .22.

And to figure out the overall utility someone would have in this population for

this product all you have to do is add together the utilities of the different

levels that define it.

That's beautiful, right?

We all can do addition, and that's all you have to do to interpret.

Addition, subtraction and once in a while a little bit of multiplication and

division, but I mean a little bit,

is all you really have to do to interpret this kind of output.

So, if we stack that product against a long shot 15 yards further, 8.99 per pack,

and these are the associated utilities that you can see right in front of you.

Those utilities associated with the levels.

We can add that together, and we get a total utility of -.33.

And that -.33, of course, is lower than .30 so that we can say,

on average, in the population, people prefer this product to this product.

When this really gets interesting is when you do the same kind of analysis I am

doing right here but you look at different segments.

You can find out which segments prefer which different designs of your product,

and which segments prefer other designs of your product.

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