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Artificial Intelligence Marketing and Predicting Consumer Choice: An Overview of Tools and Techniques [Pehme köide]

  • Formaat: Paperback / softback, 272 pages, kõrgus x laius x paksus: 234x154x15 mm, kaal: 419 g
  • Ilmumisaeg: 03-Apr-2017
  • Kirjastus: Kogan Page Ltd
  • ISBN-10: 0749479558
  • ISBN-13: 9780749479558
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  • Formaat: Paperback / softback, 272 pages, kõrgus x laius x paksus: 234x154x15 mm, kaal: 419 g
  • Ilmumisaeg: 03-Apr-2017
  • Kirjastus: Kogan Page Ltd
  • ISBN-10: 0749479558
  • ISBN-13: 9780749479558
Teised raamatud teemal:
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 presently available to achieve this purpose, each with their own unique advantages and disadvantages. Artificial Intelligence Marketing 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.

The goal of Artificial Intelligence Marketing is to explain and contrast the widely differing approaches to predictive analytics and predicting consumer choice, in practical terms that are grounded in business reality.

Arvustused

"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 * "Dr. Struhl has written another highly informative book -it offers an easy to understand way of thinking about how to best use data to answer bigger marketing questions. His explanations are clear and relatable, making this book an invaluable tool to anyone involved in commercial decision making, especially marketers and researchers." * Katie Szelc, Manager Customer Insights | Global Business Insights, Johnson & Johnson Medical Devices * "This book covers lucidly a number of research methodologies commonly support very important new product development and marketing strategy decisions. The author, Dr. Steven Struhl, should be commended for making the materials accessible to a wide range of audiences by emphasizing the practicality, appropriateness, and pros and cons of the various methodologies." * Jehoshua Eliashberg, Sebastian S. Kresge Professor of Marketing and, Professor of Operations, Information, and Decisions, The Wharton School * "An excellent all-in-one primer for today's marketer and researcher. This is clear, to the point and a comprehensive guide to this complex field." * Louis A. Tucci, Ph.D., Associate Professor Of Marketing, The College Of New Jersey * "Dr. Struhl does an excellent job of explaining the strengths and weaknesses of methods of predicting consumer behavior. This book is thoughtful, well-written, and also a practical book for marketers, marketing researchers and business consultants. If you help organizations make decisions, the best decision you can make right now is to read this book." * David F. Harris, author of The Complete Guide to Writing Questionnaires: How to Get Better Information for Better Decisions * "Artificial Intelligence Marketing and Predicting Consumer Choice clearly explains the tools that drive sophisticated market research. I heartily recommend this book for anyone looking for greater insight and success with cutting-edge techniques." * Robert Kaminsky, President, MedSpan Research *

Preface xi
1 Who should read this book and why?
1(26)
What we cover in this book
1(2)
What can you expect in this book?
3(3)
Data versus information
6(2)
What is important?
8(2)
The methods we will be discussing
10(1)
Implicit views of people and biases
11(2)
One way of comparing these methods
13(2)
Sense and sensibility with predictions
15(5)
Where we will not be going
20(1)
Summary of key points
21(6)
2 Getting the project going
27(28)
At the beginning
27(1)
Know who you are talking about or talking to
28(3)
What is the most you can expect from each method?
31(4)
How do you judge the result?
35(1)
What is significant?
36(7)
On to correlations
43(2)
How do I plan to evaluate the results?
45(5)
Know what sensible goals might look like
50(1)
Summary of key points
51(4)
3 Conjoint, discrete choice and other trade-offs: let's do an experiment
55(30)
The reasons we need these methods
55(4)
The basic thinking behind the experimentally designed methods
59(1)
What the methods ask - and get
60(6)
What is a designed experiment?
66(4)
The great measurement power of experiments
70(1)
Getting more from experiments: HB to the rescue
71(3)
A brief talk about origins
74(4)
Applications in brief
78(2)
Summary of key points
80(5)
4 Creating the best, newest thing: discrete choice modelling
85(42)
Key features
85(5)
Thinking through and setting up the problem
90(13)
How many people you need
103(2)
Utility and share
105(2)
Market simulations
107(7)
Making more than one choice: allocating purchases
114(1)
Using the simulator program in the online resources
114(4)
Rounding out the picture
118(2)
Summary of key points
120(7)
5 Conjoint analysis and its uses
127(38)
Thinking in conjoint versus thinking in choices
127(5)
Conjoint analysis for single-product optimization
132(1)
Using the single product simulator in the online resources
133(3)
Conjoint remains an excellent method for messages
136(11)
Conjoint analysis for the best service delivery
147(5)
Using the message optimization simulator in the online resources
152(2)
Conjoint analysis and interactions
154(2)
Variants of conjoint analysis
156(3)
Summary of key points
159(6)
6 Predictive models: via classifications that grow on trees
165(32)
Classification trees: understanding an amazing analytical method
165(1)
Seeing how trees work, step by step
166(7)
Strong, yet weak
173(1)
A case study: let's take a cruise
174(17)
CHAID and CART (and CRT, C&RT, QUEST, J48 and others)
191(3)
Summary: applications and cautions
194(3)
7 Remarkable predictive models with Bayes Nets
197(32)
What are Bayes Nets and how do they compare with other methods?
197(8)
Let's make a deal
205(8)
Our first example: Bayes Nets linking survey questions and behaviour
213(5)
Bayes Nets confirm a theoretical model, mostly
218(5)
What is important to buyers of children's apparel
223(3)
Summary and conclusions
226(3)
8 Putting it together: what to use when
229(8)
The tasks the methods do
230(5)
Thinking about thinking
235(2)
Bibliography 237(12)
Index 249
Dr. Steven Struhl PhD, MBA, MA has more than 25 years' experience in consulting and research, specializing in practical solutions based on statistical models of decision-making and behavior. In addition to text analytics and data mining, his work addresses how buying decisions are made, optimizing service delivery and product configurations and finding the meaningful differences among products and services. Steven 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.