AI for Marketing and Product Innovation

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