Attribute Importances

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来自 University of Virginia 的课程

Customer Value in Pricing Strategy

84 个评分

University of Virginia

Customer Value in Pricing Strategy

84 个评分

课程 2（共 4 门，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

从本节课中

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

与讲师见面

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 the next thing we're going to examine with conjoint analysis is determining

how important each of the attributes are in the overall decision process.

So is price a very important attribute, or

sort of just a minor attribute when people make decisions?

Now, look at this formula.

This is big and ugly, but it's actually quite intuitive.

I bet you figured out by now, that the difference between the effects or

the utility levels tells you something about how much more, or how much less

people prefer a particular level of a given attribute, that's true.

It's also true that you can look at the spreads between the highest and

the lowest preferred level of any different attribute.

And those spreads will give you an indication

of how important the attribute is all in the choice process.

So for example, if I looked at brand, the highest preferred level is 0.56,

and that's with the high-flyer pro brand.

The least preferred is the long shot at negative 0.60.

Suppose instead of 0.56 and 0.60, all those numbers were very close to zero.

What that would mean is it doesn't make much difference one way or the other,

which brand that you choose.

People don't rely care.

It doesn't add a lot to utility and it doesn't subtract a lot from utility.

So intuitively, that tells you that the higher the spread between the most

preferred and least preferred level, the more important the attribute is.

So how do we do that mathematically?

Typically in conjoint analysis, the way that's done

is to take any individual attribute, and let's call that attribute k.

It could be any attribute.

And look at the value of the most preferred level, so for

brand that will be 0.56.

And subtract from that the value from the least preferred level.

Now the least preferred level, because these always add up to zero,

is typically going to be a negative number.

So, it's going to be minus, minus a number, right?

It's minus a negative number, so that this whole numerator will be a positive number.

That's the numerator.

What's this in the denominator, this big, ugly summation?

That's just the sum of the spreads of all the different attributes in your

conjoint analysis, including the attribute that was included in the numerator.

So, in our output this was include brand, it will include the spread for

distance, and would also include this brand for price.

So let's take this big, ugly, messy looking thing and apply to some data.

So, in our golf ball example, the attribute importance of distance is.

Okay, where did I get that?

For distance, the least preferred level is 0.48, the most preferred level is 0.36,

and that is the difference between that positive and negative number.

Remember, that turns into an addition.

And then in the denominator, you have the spread for the distance,

you also have the spread for the brand, is setting here, and

you have the utility spread for the price, and that gives you .24.

And mathematically, I suspect a lot of you can intuitively understand,

that that number always has to be between zero and one.

Because whatever is included in the numerator is also

always included in the denominator.

And that bounds that number between zero and one.

And because of that, the typical way that this is interpreted is,

this is the percent decision weight of a particular attribute.

So distance in our example, receives 24% of the decision weight.

So it's not real high, it's not real low.

It's just kind of somewhere in the middle in terms of attribute importance.

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