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Index
Cover Title Page Copyright Preface Acknowledgments Introduction Chapter 1: Major Challenges Facing Marketers Today
Living in the Age of the Algorithm
Chapter 2: Introductory Concepts for Artificial Intelligence and Machine Learning for Marketing
Concept 1: Rule-based Systems Concept 2: Inference Engines Concept 3: Heuristics Concept 4: Hierarchical Learning Concept 5: Expert Systems Concept 6: Big Data Concept 7: Data Cleansing Concept 8: Filling Gaps in Data Concept 9: A Fast Snapshot of Machine Learning Areas of Opportunity for Machine Learning
Chapter 3: Predicting Using Big Data – Intuition Behind Neural Networks and Deep Learning
Intuition Behind Neural Networks and Deep Learning Algorithms Let It Go: How Google Showed Us That Knowing How to Do It Is Easier Than Knowing How You Know It
Chapter 4: Segmenting Customers and Markets – Intuition Behind Clustering, Classification, and Language Analysis
Intuition Behind Clustering and Classification Algorithms Intuition Behind Forecasting and Prediction Algorithms Intuition Behind Natural Language Processing Algorithms and Word2Vec Intuition Behind Data and Normalization Methods
Chapter 5: Identifying What Matters Most – Intuition Behind Principal Components, Factors, and Optimization
Principal Component Analysis and Its Applications Intuition Behind Rule-based and Fuzzy Inference Engines Intuition Behind Genetic Algorithms and Optimization Intuition Behind Programming Tools
Chapter 6: Core Algorithms of Artificial Intelligence and Machine Learning Relevant for Marketing
Supervised Learning Unsupervised Learning Reinforcement Learning
Chapter 7: Marketing and Innovation Data Sources and Cleanup of Data
Data Sources Workarounds to Get the Job Done Cleaning Up Missing or Dummy Data
Chapter 8: Applications for Product Innovation
Inputs and Data for Product Innovation Analytical Tools for Product Innovation Step 1: Identify Metaphors – The Language of the Non-conscious Mind Step 2: Separate Dominant, Emergent, Fading, and Past Codes from Metaphors Step 3: Identify Product Contexts in the Non-conscious Mind Step 4: Algorithmically Parse Non-conscious Contexts to Extract Concepts Step 5: Generate Millions of Product Concept Ideas Based on Combinations Step 6: Validate and Prioritize Product Concepts Based on Conscious Consumer Data Step 7: Create Algorithmic Feature and Bundling Options Step 8: Category Extensions and Adjacency Expansion Step 9: Premiumize and Luxury Extension Identification
Chapter 9: Applications for Pricing Dynamics
Key Inputs and Data for Machine-based Pricing Analysis A Control Theoretic Approach to Dynamic Pricing Rule-based Heuristics Engine for Price Modifications
Chapter 10: Applications for Promotions and Offers
Timing of a Promotion Templates of Promotion and Real Time Optimization Convert Free to Paying, Upgrade, Upsell Language and Neurological Codes Promotions Driven by Loyalty Card Data Personality Extraction from Loyalty Data – Expanded Use Charity and the Inverse Hierarchy of Needs from Loyalty Data Planogram and Store Brand, and Store-Within-a-Store Launch from Loyalty Data Switching Algorithms
Chapter 11: Applications for Customer Segmentation
Inputs and Data for Segmentation Analytical Tools for Segmentation
Chapter 12: Applications for Brand Development, Tracking, and Naming
Brand Personality Machine-based Brand Tracking and Correlation to Performance Machine-based Brand Leadership Assessment Machine-based Brand Celebrity Spokesperson Selection Machine-based Mergers and Acquisitions Portfolio Creation Machine-based Product Name Creation
Chapter 13: Applications for Creative Storytelling and Advertising
Compression of Time – The Real Budget Savings Weighing the Worth of Programmatic Buying Neuroscience Rule-based Expert Systems for Copy Testing Capitalizing on Fading Fads and Micro Trends That Appear and Then Disappear Capitalizing on Past Trends and Blasts from the Past RFP Response and B2B Blending News and Trends with Stories Sales and Relationship Management Programmatic Creative Storytelling
Chapter 14: The Future of AI-enabled Marketing, and Planning for It
What Does This Mean for Strategy? What to Do In-house and What to Outsource What Kind of Partnerships and the Shifting Landscapes What Are Implications for Hiring and Talent Retention, and HR? What Does Human Supervision Mean in the Age of the Algorithm and Machine Learning? How to Question the Algorithm and Know When to Pull the Plug Next Generation of Marketers – Who Are They, and How to Spot Them How Budgets and Planning Will Change
Chapter 15: Next-Generation Creative and Research Agency Models
What Does an ML- and AI-enabled Market Research or Marketing Services Agency Look Like? What an ML- and AI-enabled Research Agency or Marketing Services Company Can Do That Traditional Agencies Cannot Do The New Nature of Partnership Is There a Role for a CES or Cannes-like Event for AI and ML Algorithms and Artificial Intelligence Programs? Challenges and Solutions Big Data AI- and ML-powered Strategic Development Creative Execution Beam Me Up Will Retail Be a Remnant? Getting Real It Begins – and Ends – with an “A” Word
About the Authors Index Wiley End User License Agreement
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