AI for Marketing and Product Innovation: Powerful New Tools for Predicting Trends, Connecting with Customers, and Closing Sales

AI for Marketing and Product Innovation: Powerful New Tools for Predicting Trends, Connecting with Customers, and Closing Sales

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Product Details

ISBN-13: 9781119484066
Publisher: Wiley
Publication date: 12/06/2018
Pages: 272
Sales rank: 740,783
Product dimensions: 6.10(w) x 9.10(h) x 1.00(d)

About the Author

DR. A.K. PRADEEP is the Founder/CEO of machineVantage, a startup applying AI and Machine Learning to some of the most challenging marketing problems. Dr. Pradeep's clients during his career have ranged from Unilever to Coca-Cola, Nissan, Google, Facebook, Mondelez, Pepsi-Cola, Clorox, and dozens more. He is the author of The Buying Brain, also from Wiley.

ANDREW APPEL is President and CEO of IRI, a global leader in technology solutions and services for consumer, retail and media companies and was previously a McKinsey senior partner. IRI works with some of the world's leading brands, retailers and media organizations including Anheuser-Busch InBev, Conagra, PepsiCo, Kroger, Costco and Walgreens as well as Google, Facebook and OmniCom Group, among other global companies.

STAN STHANUNATHAN is the Global EVP of Consumer and Market Insights for Unilever, one of the world's largest and most successful consumer packaged goods companies.

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Table of Contents

Preface xiii

Acknowledgments xvii

Introduction xix

1 Major Challenges Facing Marketers Today 1

Living in the Age of the Algorithm 3

2 Introductory Concepts for Artificial Intelligence and Machine Learning for Marketing 7

Concept 1: Rule-based Systems 8

Concept 2: Inference Engines 10

Concept 3: Heuristics 11

Concept 4: Hierarchical Learning 12

Concept 5: Expert Systems 14

Concept 6: Big Data 16

Concept 7: Data Cleansing 18

Concept 8: Filling Gaps in Data 19

Concept 9: A Fast Snapshot of Machine Learning 19

Areas of Opportunity for Machine Learning 22

3 Predicting Using Big Data – Intuition Behind Neural Networks and Deep Learning 29

Intuition Behind Neural Networks and Deep Learning Algorithms 29

Let It Go: How Google Showed Us That Knowing How to Do It Is Easier Than Knowing How You Know It 37

4 Segmenting Customers and Markets – Intuition Behind Clustering, Classification, and Language Analysis 45

Intuition Behind Clustering and Classification Algorithms 45

Intuition Behind Forecasting and Prediction Algorithms 54

Intuition Behind Natural Language Processing Algorithms and Word2Vec 61

Intuition Behind Data and Normalization Methods 70

5 Identifying What Matters Most – Intuition Behind Principal Components, Factors, and Optimization 77

Principal Component Analysis and Its Applications 78

Intuition Behind Rule-based and Fuzzy Inference Engines 83

Intuition Behind Genetic Algorithms and Optimization 87

Intuition Behind Programming Tools 92

6 Core Algorithms of Artificial Intelligence and Machine Learning Relevant for Marketing 99

Supervised Learning 100

Unsupervised Learning 102

Reinforcement Learning 105

7 Marketing and Innovation Data Sources and Cleanup of Data 107

Data Sources 108

Workarounds to Get the Job Done 112

Cleaning Up Missing or Dummy Data 113

8 Applications for Product Innovation 119

Inputs and Data for Product Innovation 120

Analytical Tools for Product Innovation 122

Step 1: Identify Metaphors – The Language of the Non-conscious Mind 123

Step 2: Separate Dominant, Emergent, Fading, and Past Codes from Metaphors 124

Step 3: Identify Product Contexts in the Non-conscious Mind 125

Step 4: Algorithmically Parse Non-conscious Contexts to Extract Concepts 126

Step 5: Generate Millions of Product Concept Ideas Based on Combinations 126

Step 6: Validate and Prioritize Product Concepts Based on Conscious Consumer Data 127

Step 7: Create Algorithmic Feature and Bundling Options 128

Step 8: Category Extensions and Adjacency Expansion 129

Step 9: Premiumize and Luxury Extension Identification 130

9 Applications for Pricing Dynamics 131

Key Inputs and Data for Machine-based Pricing Analysis 132

A Control Th eoretic Approach to Dynamic Pricing 135

Rule-based Heuristics Engine for Price Modifi cations 136

10 Applications for Promotions and Offers 139

Timing of a Promotion 141

Templates of Promotion and Real Time Optimization 143

Convert Free to Paying, Upgrade, Upsell 144

Language and Neurological Codes 145

Promotions Driven by Loyalty Card Data 147

Personality Extraction from Loyalty Data – Expanded Use 148

Charity and the Inverse Hierarchy of Needs from Loyalty Data 149

Planogram and Store Brand, and Store-Within-a-Store Launch from Loyalty Data 150

Switching Algorithms 151

11 Applications for Customer Segmentation 153

Inputs and Data for Segmentation 154

Analytical Tools for Segmentation 156

12 Applications for Brand Development, Tracking, and Naming 161

Brand Personality 162

Machine-based Brand Tracking and Correlation to Performance 169

Machine-based Brand Leadership Assessment 170

Machine-based Brand Celebrity Spokesperson Selection 171

Machine-based Mergers and Acquisitions Portfolio Creation 172

Machine-based Product Name Creation 173

13 Applications for Creative Storytelling and Advertising 177

Compression of Time – The Real Budget Savings 178

Weighing the Worth of Programmatic Buying 183

Neuroscience Rule-based Expert Systems for Copy Testing 185

Capitalizing on Fading Fads and Micro Trends That Appear and Then Disappear 188

Capitalizing on Past Trends and Blasts from the Past 189

RFP Response and B2B Blending News and Trends with Stories 189

Sales and Relationship Management 190

Programmatic Creative Storytelling 191

14 The Future of AI-enabled Marketing, and Planning for It 193

What Does This Mean for Strategy? 194

What to Do In-house and What to Outsource 195

What Kind of Partnerships and the Shifting Landscapes 195

What Are Implications for Hiring and Talent Retention, and HR? 196

What Does Human Supervision Mean in the Age of the Algorithm and Machine Learning? 199

How to Question the Algorithm and Know When to Pull the Plug 200

Next Generation of Marketers – Who Are They, and How to Spot Them 201

How Budgets and Planning Will Change 201

15 Next-Generation Creative and Research Agency Models 203

What Does an ML- and AI-enabled Market Research or Marketing Services Agency Look Like? 206

What an ML- and AI-enabled Research Agency or Marketing Services Company Can Do That

Traditional Agencies Cannot Do 207

The New Nature of Partnership 208

Is There a Role for a CES or Cannes-like Event for AI and ML Algorithms and Artificial Intelligence Programs? 209

Challenges and Solutions 210

Big Data 215

AI- and ML-powered Strategic Development 215

Creative Execution 217

Beam Me Up 218

Will Retail Be a Remnant? 219

Getting Real 220

It Begins – and Ends – with an “A” Word 221

About the Authors 225

Index 229

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