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


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. 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.

AI for Marketing and Product Innovation: Powerful New Tools for Predicting Trends, Connecting with Customers, and Closing Sales | Andrew Appel | Stan Sthanunathan

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Table of Contents:

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

Rule-based Systems, Inference Engines, Heuristics, Expert Systems, Big Data, Data Cleansing, Filling Gaps in Data, A Fast Snapshot of 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 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

Chapter 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

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?






Written by Venkadesh Narayanan

Venkadesh is a Mechanical Engineer and an MBA with 30 years of experience in the domains of supply chain management, business analysis, new product development, business plan and standard operating procedures. He is currently working as Principal Consultant at Fhyzics Business Consultants. He is also serving as President, PDMA-India (an Indian affiliate of PDMA, USA) and Recognised Instructor of APICS, USA and CIPS, UK. He is a former member of Indian Civil Services (IRAS). Fhyzics offers consulting, certification, and executive development programs in the domains of supply chain management, business analysis and new product development.

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