CHOICE MODELING/CONJOINT/TRADE OFFS


ARTIFICIAL INTELLIGENCE MARKETING AND PREDICTING CONSUMER CHOICE

Learn about the ways that artificial intelligence and machine learning methods work to enhance predictive models. Everything is explained in clear language and without recourse to equations or advanced notation. Many methods are discussed, including Bayes Nets, different ensemble methods, classification trees, and neural networks. We also review old favorites that have been greatly expanded by machine learning, such as conjoint, discrete choice modeling and MaxDiff (or maximum difference scaling). This is  a book for professionals who must apply these methods and students who want to learn about them. It Includes several downloadable market simulator programs and two bonus online chapters.


Available for purchase at Amazon.com (direct link to purchase).
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SAMPLE SIMULATOR

This simulator is fully interactive and runs in Microsoft Excel. It is described in Chapter 4 of Artificial Intelligence Marketing and Predicting Consumer Choice.

Click here to download this interactive simulator. If Excel asks if it is OK to run the content after you download, say “Yes.”

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TRADE-OFF METHODS BASICS

118 slides, a broad overview of discrete choice modeling, conjoint, MaxDiff and Q-Sort, including several presentations in one handy spot, with an index and many factual or fun illustrations

 Please click here for the PDF FILE trade-off methods basics

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Trade off Methods Primer

31 slides, the basics of discrete choice modeling, conjoint, MaxDiff and Q-Sort, comprising the first sections of the "Trade-off methods basics" presentation

 Please click here for the PDF FILE trade-off primer

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MAXDIFF AND Q-SORT

10 slides, explaining these methods for prioritizing items

Please click here for the PDF FILE Maxdiff and Q-Sort

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INSIDE EXPERIMENTAL DESIGNS

15 slides, an overview of the designs underpinning discrete choice modeling

 Please click here for the PDF FILE Inside Experimental designs

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CONJOINT VS. DISCRETE CHOICE

11 slides, with an examination of the relative strengths of and best applications for each method. Reports of conjoint’s demise are greatly exaggerated, but what are its best applications?

Please click here for the PDF FILE Conjoint vs discrete choice

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CONJOINT BASED MESSAGE OPTIMIZATION

6 slides--How to vary many message elements at once and see the impact of changes clearly

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