Description
Book SynopsisTable of ContentsPreface 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
