AI-Driven Marketing Research and Data Analytics

The surge in technological advancements, coupled with the exponential growth of data, has left marketers grappling with the need for a paradigm shift. The once-established methods of consumer engagement are now overshadowed by the complexities of the digital age, demanding a profound understanding of artificial intelligence (AI) and data analytics. The gap between academic knowledge and practical applications in the field of marketing has widened, leaving industry professionals, educators, and students seeking a comprehensive resource to navigate the intricacies of this transformative era.

AI-Driven Marketing Research and Data Analytics is a groundbreaking book that serves as a beacon for marketers, educators, and industry leaders alike. With a keen focus on the symbiotic relationship between AI, data analytics, and marketing research, this book bridges the gap between theory and practice. It not only explores the historical evolution of marketing but also provides an innovative examination of how AI and data analytics are reshaping the landscape. Through real-time case studies, ethical considerations, and in-depth insights, the book offers a holistic solution to the challenges faced by marketing professionals in the digital age.

This book is not just an academic exercise; it is a straightforward guide that transforms theoretical concepts into actionable strategies. By delving into topics ranging from AI-driven market campaigns to ethical considerations in data usage, it equips readers with the tools needed to navigate the complexities of contemporary marketing. The book ensures that its readers are not only informed about the latest trends but also empowered to apply these insights in their daily routines. For universities, colleges, and industry professionals seeking a foundational text that seamlessly integrates academia with real-world applications, AI-Driven Marketing Research and Data Analytics stands as an indispensable resource, heralding a new era of marketing excellence in the digital age.

The many academic areas covered in this publication include, but are not limited to:

• A/B Testing and Optimization
• AI and Augmented Reality (AR) in Marketing
• Artificial Intelligence Algorithms
• Artificial Intelligence Driven Marketing Campaigns
• Artificial Intelligence in Marketing Research
• Attribution Modeling for Fraud Detection
• Bias and Fairness in Marketing Data and Algorithms
• Big Data Marketing Implications
• Blockchain and Web 3.0 Marketing
• Chatbots and Artificial Intelligence Powered Customer Support
• Churn Prediction Modeling for Customer Retention
• Consumer Behavior Analysis Artificial Intelligence
• Convolutional and Recurrent Neural Networks
• Customer Lifetime Value Prediction with Survival Analysis
• Data Analytics in Marketing Research
• Data Anonymization Techniques
• Data Collection and Management
• Data Privacy and Ethical Considerations
• Deep Learning and ARIMA Models
• Ethical Considerations and Challenges
• Evolution of Digital Marketing
• Forecasting Demand Using Time Series Models
• Future of Marketing in the Metaverse and Web 3.0 Environments
• Handling Censorship and Dynamic Predictors
• Key Performance Indicators (KPIs) in AI Marketing
• Machine Learning for Predictive Analytics
• Machine-learning Algorithms for Bid Optimization
• Marketing Attribution Models
• Measuring Returns on Investments (ROI) and Performance
• Metaverse Brand Presence and Engagement
• Neural Networks and Deep-learning Models for Marketing Analytics
• Personalization and Customer Experience
• Predictive Analytics for Targeted Campaigns
• Real-time Bid Optimization for Programmatic Advertising
• Real-time Fraud Monitoring
• Regulatory Frameworks and Compliance
• Sentiment Analysis and Social Media Listening
• Voice Search and Visual Search