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

Langue : Anglais

Auteurs :

Couverture de l’ouvrage AI for Marketing and Product Innovation

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. The future is here, and we are in the thick of it; AI and ML are already in our lives every day, whether we know it or not. The technology continues to evolve and grow, but the capabilities that make these tools world-changing for marketers are already here—whether we use them or not. This book helps you lean into the curve and take advantage of AI’s unparalleled and rapidly expanding power.

More than a simple primer on the technology, this book goes beyond the “what” to show you the “how”: How do we use AI and ML in ways that speak to the human spirit? How to we translate cold technological innovation into creative tools that forge deep human connections? Written by a team of experts at the intersection of neuroscience, technology, and marketing, this book shows you the ins and outs of these groundbreaking technological tools.

  • Understand AI and ML technology in layman’s terms
  • Harness the twin technologies unparalleled power to transform marketing
  • Learn which skills and resources you need to use AI and ML effectively
  • Employ AI and ML in ways that resonate meaningfully with customers
  • Learn practical examples of how to reinvest product innovation, brand building, targeted marketing and media measurement to connect with people and enhance ROI

Discover the true impact of AI and ML from real-world examples, and learn the thinking, best practices, and metrics you need to capture this lightning and take the next massive leap in the evolution of customer connection. AI for Marketing and Product Innovation shows you everything you need to know to get on board.

  • Preface
  • Acknowledgments
  • Introduction
  1. Major Challenges Facing Marketers Today
    Living in the Age of the Algorithm
  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
  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
  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
  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
  6. Core Algorithms of Artificial Intelligence and Machine Learning Relevant for Marketing
    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning
  7. Marketing and Innovation Data Sources and Cleanup of Data
    • Data Sources
    • Workarounds to Get the Job Done
    • Cleaning Up Missing or Dummy Data
  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
  9. Applications for Pricing Dynamics
    • Key Inputs and Data for Machine-based Pricing Analysis
    • A Control Th eoretic Approach to Dynamic Pricing
    • Rule-based Heuristics Engine for Price Modifi cations
  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
  11. Applications for Customer Segmentation
    • Inputs and Data for Segmentation
    • Analytical Tools for Segmentation
  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
  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
  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
  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

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.

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.

Date de parution :

Ouvrage de 272 p.

15.4x23.1 cm

Disponible chez l'éditeur (délai d'approvisionnement : 12 jours).

Prix indicatif 31,03 €

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