AI for Marketing and Product Innovation: Powerful New Tools for Predicting Trends, Connecting with Customers, and Closing Sales
- Length: 272 pages
- Edition: 1
- Language: English
- Publisher: Wiley
- Publication Date: 2018-12-06
- ISBN-10: 1119484065
- ISBN-13: 9781119484066
- Sales Rank: #984491 (See Top 100 Books)
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 Contents 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 Application 1: Localization and Local Brands Application 2: Value and Rationalization of Social Media Cost Application 3: Rationalization of Advertising Cost Application 4: Merging of Innovation and Marketing and R&D Application 5: Co-creation 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 Association Clustering Dimensionality Reduction 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 Completing Consumer Purchase Data Filling In Geospatial Data Normalizing Temporal Scales Across Data Eliminating Seasonality from Data Normalizing Data Across Different Ranges Detecting Anomalies and Outliers Integrating Qualitative and Quantitative Data Weather and Environmental 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 Step 1 : PCA and Clustering Techniques Step 2 : Metaphor-based Segmentation Step 3 : Algorithmic Facet-based Segmentation Step 4 : Segment Fusion Based on Plurality of Approaches Step 5 : Segment-specific Offerings Chapter 12: Applications for Brand Development, Tracking, and Naming Brand Personality Brand Personality Type 1: New Experiences and Openness Brand Personality Type 2: Orderly Progression and Conscientiousness Brand Personality Type 3: Positivity, Talkability, and Extraversion Brand Personality Type 4: Collaboration, Harmony, and Agreeableness Brand Personality Type 5: Emotional Volatility and Neuroticism 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 Template for Constructing a 30-Second Ad Template for Constructing a 15-Second Ad Template for Constructing an 8-Second Ad Template for Constructing a 5-Second Ad Template and Components for Constructing a Print Ad Template and Components for Constructing an Internet Banner Ad Template and Components for Retail POS Weighing the Worth of Programmatic Buying Programmatic Advertising Purchase Logic Template and Components for Meme Construction 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 Template for Programmatic 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 A.K. Pradeep Andrew Appel Stan Sthanunathan Index EULA
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