AI for marketing and product innovation powerful new tools for predicting trends, connecting with customers, and closing sales - Pradeep, A. K. 1963-; Appel, Andrew; Sthanunathan, Stan (Wiley, 2019)
Intro – 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