AI for Marketing and Product Innovation

Table of contents

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