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

ISBN-13: 9781119484066 / Angielski / Twarda / 2018 / 272 str.

A. K. Pradeep; Andrew Appel; Stan Sthanunathan

Opis

Get on board the next massive marketing revolution AI for Marketing and Product Innovation offers creatives and marketing professionals a non-tech guide to artificial intelligence (AI) and machine learning (ML) twin technologies that stand poised to revolutionize the way we sell.

Spis treści

Preface xiiiAcknowledgments xviiIntroduction xix1 Major Challenges Facing Marketers Today 1Living in the Age of the Algorithm 32 Introductory Concepts for Artificial Intelligence and Machine Learning for Marketing 7Concept 1: Rule-based Systems 8Concept 2: Inference Engines 10Concept 3: Heuristics 11Concept 4: Hierarchical Learning 12Concept 5: Expert Systems 14Concept 6: Big Data 16Concept 7: Data Cleansing 18Concept 8: Filling Gaps in Data 19Concept 9: A Fast Snapshot of Machine Learning 19Areas of Opportunity for Machine Learning 223 Predicting Using Big Data - Intuition Behind Neural Networks and Deep Learning 29Intuition Behind Neural Networks and Deep Learning Algorithms 29Let It Go: How Google Showed Us That Knowing How to Do It Is Easier Than Knowing How You Know It 374 Segmenting Customers and Markets - Intuition Behind Clustering, Classification, and Language Analysis 45Intuition Behind Clustering and Classification Algorithms 45Intuition Behind Forecasting and Prediction Algorithms 54Intuition Behind Natural Language Processing Algorithms and Word2Vec 61Intuition Behind Data and Normalization Methods 705 Identifying What Matters Most - Intuition Behind Principal Components, Factors, and Optimization 77Principal Component Analysis and Its Applications 78Intuition Behind Rule-based and Fuzzy Inference Engines 83Intuition Behind Genetic Algorithms and Optimization 87Intuition Behind Programming Tools 926 Core Algorithms of Artificial Intelligence and Machine Learning Relevant for Marketing 99Supervised Learning 100Unsupervised Learning 102Reinforcement Learning 1057 Marketing and Innovation Data Sources and Cleanup of Data 107Data Sources 108Workarounds to Get the Job Done 112Cleaning Up Missing or Dummy Data 1138 Applications for Product Innovation 119Inputs and Data for Product Innovation 120Analytical Tools for Product Innovation 122Step 1: Identify Metaphors - The Language of the Non-conscious Mind 123Step 2: Separate Dominant, Emergent, Fading, and Past Codes from Metaphors 124Step 3: Identify Product Contexts in the Non-conscious Mind 125Step 4: Algorithmically Parse Non-conscious Contexts to Extract Concepts 126Step 5: Generate Millions of Product Concept Ideas Based on Combinations 126Step 6: Validate and Prioritize Product Concepts Based on Conscious Consumer Data 127Step 7: Create Algorithmic Feature and Bundling Options 128Step 8: Category Extensions and Adjacency Expansion 129Step 9: Premiumize and Luxury Extension Identification 1309 Applications for Pricing Dynamics 131Key Inputs and Data for Machine-based Pricing Analysis 132A Control Th eoretic Approach to Dynamic Pricing 135Rule-based Heuristics Engine for Price Modifi cations 13610 Applications for Promotions and Offers 139Timing of a Promotion 141Templates of Promotion and Real Time Optimization 143Convert Free to Paying, Upgrade, Upsell 144Language and Neurological Codes 145Promotions Driven by Loyalty Card Data 147Personality Extraction from Loyalty Data - Expanded Use 148Charity and the Inverse Hierarchy of Needs from Loyalty Data 149Planogram and Store Brand, and Store-Within-a-Store Launch from Loyalty Data 150Switching Algorithms 15111 Applications for Customer Segmentation 153Inputs and Data for Segmentation 154Analytical Tools for Segmentation 15612 Applications for Brand Development, Tracking, and Naming 161Brand Personality 162Machine-based Brand Tracking and Correlation to Performance 169Machine-based Brand Leadership Assessment 170Machine-based Brand Celebrity Spokesperson Selection 171Machine-based Mergers and Acquisitions Portfolio Creation 172Machine-based Product Name Creation 17313 Applications for Creative Storytelling and Advertising 177Compression of Time - The Real Budget Savings 178Weighing the Worth of Programmatic Buying 183Neuroscience Rule-based Expert Systems for Copy Testing 185Capitalizing on Fading Fads and Micro Trends That Appear and Then Disappear 188Capitalizing on Past Trends and Blasts from the Past 189RFP Response and B2B Blending News and Trends with Stories 189Sales and Relationship Management 190Programmatic Creative Storytelling 19114 The Future of AI-enabled Marketing, and Planning for It 193What Does This Mean for Strategy? 194What to Do In-house and What to Outsource 195What Kind of Partnerships and the Shifting Landscapes 195What Are Implications for Hiring and Talent Retention, and HR? 196What Does Human Supervision Mean in the Age of the Algorithm and Machine Learning? 199How to Question the Algorithm and Know When to Pull the Plug 200Next Generation of Marketers - Who Are They, and How to Spot Them 201How Budgets and Planning Will Change 20115 Next-Generation Creative and Research Agency Models 203What Does an ML- and AI-enabled Market Research or Marketing Services Agency Look Like? 206What an ML- and AI-enabled Research Agency or Marketing Services Company Can Do ThatTraditional Agencies Cannot Do 207The New Nature of Partnership 208Is There a Role for a CES or Cannes-like Event for AI and ML Algorithms and Artificial Intelligence Programs? 209Challenges and Solutions 210Big Data 215AI- and ML-powered Strategic Development 215Creative Execution 217Beam Me Up 218Will Retail Be a Remnant? 219Getting Real 220It Begins - and Ends - with an "A" Word 221About the Authors 225Index 229

O autorze

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.