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

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AI For Marketing and Product Innovation\nContents\nPreface\nAcknowledgments\nIntroduction\nChapter 1: Major Challenges Facing Marketers Today\n Living in the Age of the Algorithm\nChapter 2: Introductory Concepts for Artificial Intelligence and Machine Learning for Marketing\n Concept 1: Rule-based Systems\n Concept 2: Inference Engines\n Concept 3: Heuristics\n Concept 4: Hierarchical Learning\n Concept 5: Expert Systems\n Concept 6: Big Data\n Concept 7: Data Cleansing\n Concept 8: Filling Gaps in Data\n Concept 9: A Fast Snapshot of Machine Learning\n Areas of Opportunity for Machine Learning\n Application 1: Localization and Local Brands\n Application 2: Value and Rationalization of Social Media Cost\n Application 3: Rationalization of Advertising Cost\n Application 4: Merging of Innovation and Marketing and R&D\n Application 5: Co-creation\nChapter 3: Predicting Using Big Data – Intuition Behind Neural Networks and Deep Learning\n Intuition Behind Neural Networks and Deep Learning Algorithms\n Let It Go: How Google Showed Us That Knowing How to Do It Is Easier Than Knowing How You Know It\nChapter 4: Segmenting Customers and Markets – Intuition Behind Clustering, Classification, and Language Analysis\n Intuition Behind Clustering and Classification Algorithms\n Intuition Behind Forecasting and Prediction Algorithms\n Intuition Behind Natural Language Processing Algorithms and Word2Vec\n Intuition Behind Data and Normalization Methods\nChapter 5: Identifying What Matters Most – Intuition Behind Principal Components, Factors, and Optimization\n Principal Component Analysis and Its Applications\n Intuition Behind Rule-based and Fuzzy Inference Engines\n Intuition Behind Genetic Algorithms and Optimization\n Intuition Behind Programming Tools\nChapter 6: Core Algorithms of Artificial Intelligence and Machine Learning Relevant for Marketing\n Supervised Learning\n Unsupervised Learning\n Association\n Clustering\n Dimensionality Reduction\n Reinforcement Learning\nChapter 7: Marketing and Innovation Data Sources and Cleanup of Data\n Data Sources\n Workarounds to Get the Job Done\n Cleaning Up Missing or Dummy Data\n Completing Consumer Purchase Data\n Filling In Geospatial Data\n Normalizing Temporal Scales Across Data\n Eliminating Seasonality from Data\n Normalizing Data Across Different Ranges\n Detecting Anomalies and Outliers\n Integrating Qualitative and Quantitative Data\n Weather and Environmental Data\nChapter 8: Applications for Product Innovation\n Inputs and Data for Product Innovation\n Analytical Tools for Product Innovation\n Step 1: Identify Metaphors – The Language of the Non-conscious Mind\n Step 2: Separate Dominant, Emergent, Fading, and Past Codes from Metaphors\n Step 3: Identify Product Contexts in the Non-conscious Mind\n Step 4: Algorithmically Parse Non-conscious Contexts to Extract Concepts\n Step 5: Generate Millions of Product Concept Ideas Based on Combinations\n Step 6: Validate and Prioritize Product Concepts Based on Conscious Consumer Data\n Step 7: Create Algorithmic Feature and Bundling Options\n Step 8: Category Extensions and Adjacency Expansion\n Step 9: Premiumize and Luxury Extension Identification\nChapter 9: Applications for Pricing Dynamics\n Key Inputs and Data for Machine-based Pricing Analysis\n A Control Theoretic Approach to Dynamic Pricing\n Rule-based Heuristics Engine for Price Modifications\nChapter 10: Applications for Promotions and Offers\n Timing of a Promotion\n Templates of Promotion and Real Time Optimization\n Convert Free to Paying, Upgrade, Upsell\n Language and Neurological Codes\n Promotions Driven by Loyalty Card Data\n Personality Extraction from Loyalty Data – Expanded Use\n Charity and the Inverse Hierarchy of Needs from Loyalty Data\n Planogram and Store Brand, and Store-Within-a-Store Launch from Loyalty Data\n Switching Algorithms\nChapter 11: Applications for Customer Segmentation\n Inputs and Data for Segmentation\n Analytical Tools for Segmentation\n Step 1 : PCA and Clustering Techniques\n Step 2 : Metaphor-based Segmentation\n Step 3 : Algorithmic Facet-based Segmentation\n Step 4 : Segment Fusion Based on Plurality of Approaches\n Step 5 : Segment-specific Offerings\nChapter 12: Applications for Brand Development, Tracking, and Naming\n Brand Personality\n Brand Personality Type 1: New Experiences and Openness\n Brand Personality Type 2: Orderly Progression and Conscientiousness\n Brand Personality Type 3: Positivity, Talkability, and Extraversion\n Brand Personality Type 4: Collaboration, Harmony, and Agreeableness\n Brand Personality Type 5: Emotional Volatility and Neuroticism\n Machine-based Brand Tracking and Correlation to Performance\n Machine-based Brand Leadership Assessment\n Machine-based Brand Celebrity Spokesperson Selection\n Machine-based Mergers and Acquisitions Portfolio Creation\n Machine-based Product Name Creation\nChapter 13: Applications for Creative Storytelling and Advertising\n Compression of Time – The Real Budget Savings\n Template for Constructing a 30-Second Ad\n Template for Constructing a 15-Second Ad\n Template for Constructing an 8-Second Ad\n Template for Constructing a 5-Second Ad\n Template and Components for Constructing a Print Ad\n Template and Components for Constructing an Internet Banner Ad\n Template and Components for Retail POS\n Weighing the Worth of Programmatic Buying\n Programmatic Advertising Purchase Logic\n Template and Components for Meme Construction\n Neuroscience Rule-based Expert Systems for Copy Testing\n Capitalizing on Fading Fads and Micro Trends That Appear and Then Disappear\n Capitalizing on Past Trends and Blasts from the Past\n RFP Response and B2B Blending News and Trends with Stories\n Sales and Relationship Management\n Programmatic Creative Storytelling\n Template for Programmatic Storytelling\nChapter 14: The Future of AI-enabled Marketing, and Planning for It\n What Does This Mean for Strategy?\n What to Do In-house and What to Outsource\n What Kind of Partnerships and the Shifting Landscapes\n What Are Implications for Hiring and Talent Retention, and HR?\n What Does Human Supervision Mean in the Age of the Algorithm and Machine Learning?\n How to Question the Algorithm and Know When to Pull the Plug\n Next Generation of Marketers – Who Are They, and How to Spot Them\n How Budgets and Planning Will Change\nChapter 15: Next-Generation Creative and Research Agency Models\n What Does an ML- and AI-enabled Market Research or Marketing Services Agency Look Like?\n What an ML- and AI-enabled Research Agency or Marketing Services Company Can Do That Traditional Agencies Cannot Do\n The New Nature of Partnership\n Is There a Role for a CES or Cannes-like Event for AI and ML Algorithms and Artificial Intelligence Programs?\n Challenges and Solutions\n Big Data\n AI- and ML-powered Strategic Development\n Creative Execution\n Beam Me Up\n Will Retail Be a Remnant?\n Getting Real\n It Begins – and Ends – with an “A” Word\nAbout the Authors\n A.K. Pradeep\n Andrew Appel\n Stan Sthanunathan\nIndex\nEULA