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The ability to predict consumer choice is a fundamental aspect to success for any business. In the context of artificial intelligence marketing, there are a wide array of predictive analytic techniques available to achieve this purpose, each with its own unique advantages and disadvantages. Artificial Intelligence Marketing and Predicting Consumer Choice serves to integrate these widely disparate approaches, and show the strengths, weaknesses, and best applications of each. It provides a bridge between the person who must apply or learn these problem-solving methods and the community of experts who do the actual analysis. It is also a practical and accessible guide to the many remarkable advances that have been recently made in this fascinating field.
Online resources: bonus chapters on AI, ensembles and neural nets, and finishing experiments, plus single and multiple product simulators.
List of contents
- Section - 01: Who Should Read this Book and Why?;
- Section - 02: Getting the Project Going;
- Section - 03: Conjoint, Discrete Choice and Other Trade-offs: Let's Do an Experiment;
- Section - 04: Creating the Best, Newest Thing: Discrete Choice Modelling;
- Section - 05: Conjoint Analysis and its Uses;
- Section - 06: Predictive Models: Via Classifications that Grow on Trees;
- Section - 07: Remarkable Predictive Models with Bayes Nets;
- Section - 08: Putting it Together: What to Use When;
About the author
Dr. Steven Struhl PhD, MBA, MA has more than 25 years' experience in consulting and research, specialising in practical solutions based on statistical models of decision-making and behaviour. In addition to text analytics and data mining, his work addresses how buying decisions are made, optimising service delivery and product configurations and finding the meaningful differences among products and services.
Steven Struhl also has taught graduate courses on statistical methods and data analysis. He speaks at conferences and has given numerous seminars on pricing, choice modelling, market segmentation and presenting data.
Summary
The ability to predict consumer choice is a fundamental aspect to success for any business. In the context of artificial intelligence marketing, there are a wide array of predictive analytic techniques available to achieve this purpose, each with its own unique advantages and disadvantages. Artificial Intelligence Marketing and Predicting Consumer Choice serves to integrate these widely disparate approaches, and show the strengths, weaknesses, and best applications of each. It provides a bridge between the person who must apply or learn these problem-solving methods and the community of experts who do the actual analysis. It is also a practical and accessible guide to the many remarkable advances that have been recently made in this fascinating field.
Online resources: bonus chapters on AI, ensembles and neural nets, and finishing experiments, plus single and multiple product simulators.
Report
"Full of hard-won practical wisdom, this is a comprehensive guide to navigating the complexity of market forecasting. Foregoing the hyperbole that so often characterizes discussions of artificial intelligence, Dr. Struhl thoroughly explains a wide range of methods, where their difficulties lie, and how to get the best insights from each." Peter Goldstein, Software Engineer, Google
