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melinda_thielba

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May 27, 2014

Coming in JMP 12: Probability and Multiple Choice Profilers in the Choice platform

The JMP Profiler is a powerful tool for visualizing your model. With one click, you can see what the model predicts when you change a product’s features or adjust one of your assumptions. It’s also a powerful communication tool. Your audience doesn’t need a statistics background to understand the model’s message.

In JMP 11 and earlier, the Profiler in the Choice platform was a Utility Profiler — it showed how your product’s utility changed with changes in features. To an economist or a marketer, utility is a pretty straightforward concept. Higher utility means a more desirable product that people are more likely to buy.

“How much more likely?” and “More likely compared to what?” your marketing manager might ask. Until JMP 12, your answer might have been, “Let me get back to you.”

In JMP 12 you have the Probability Profiler and the Multiple Choice Profiler, two new tools to help visualize comparisons between competing products and predict market share for proposed new products.

Let's look at a quick example. Maybe I’m planning to offer a thick crust pizza with mozzarella cheese and pepperoni to compete with my competitor’s pizza, which has a thick crust with jack cheese and pepperoni.

Probability Profiler--New in JMP 12

The Probability Profiler transforms the Utility Profiler into a comparison between two products.

The Probability Profiler makes it easy to see that my proposed product is a good idea. When choosing between my pizza and my competitor’s, there’s a 92 percent chance that the “Female” market segment will choose mine.

 

Subject effects are listed with the Baseline product. Comparisons are made for the same subject.

Subject effects are listed with the Baseline product. Comparisons are made for the same subject.

There aren’t many markets where consumers have only two choices. You might know about multiple competitor products and want to design the pizza with maximum choice probability against all of them. That’s a job for the Multiple Choice Profiler.

Instead of having one baseline model and one alternative, the Multiple Choice Profiler produces a set of linked Profilers — one for each alternative (the one below shows three, but you can specify more or fewer). The selectors at the top set the market segment, and the profile sliders set the properties of the different products. There’s also a chart just below the header to visually show which product would have the highest predicted market share.

The Multiple Choice Profiler

The Multiple Choice Profiler lets you compare several products with different attributes.

Now it’s easy to see that a thin crust pizza with mozzarella cheese and no toppings is a clear winner against the other alternatives.

Look for more in-depth posts about how to use these new Profilers after the software becomes available in March.

Editor's note: This post is part of a series of previews of JMP 12 written by the people who develop the software.

2 Comments
Community Member

Ronald J. Murray wrote:

Melinda:

I am interested in using choice modeling to define digitization specifications here at the Library of Congress. I want to relate digital image properties that I can specify or measure to library users' judgments of image quality and mode of delivery (onscreen, print). The experimental method seems like something we could get library visitors and staff to do.

I looked at the existing JMP Choice platform, but could not quite understand the statistics employed. I've been able to find textbooks on Design of Experiments that explained DOE stats et. well enough, but have been able to find comparably comprehensible works on choice modeling.

Can you help?

Melinda Thielbar wrote:

Hi, Ronald:

A lot of people use Choice design to evaluate settings for delivering digital content. I'm guessing you will want to show pairs of images with different properties and ask users to choose which of the pair is the best image. You'll need a designed experiment so you're getting as much information as possible per response.

The Design of Experiments guide in the JMP documentation has an example that shows how to set up a choice experiment in JMP: http://www.jmp.com/support/help/Discrete_Choice_Designs.shtml

You can get to the same book from JMP by going to Help->Books->Design of Experiments Chapter 10).

The Consumer Research book has a chapter on how to analyze the data once it's collected: hhttp://www.jmp.com/support/help/Choice_Modeling_Platform_Overview.shtml

You can also post questions to the JMP Community https://community.jmp.com/welcome