About the Book
In the digital age, data has become the cornerstone of effective marketing strategies. Data Engineering for Data-Driven Marketing explores the vital intersection of data engineering and marketing, providing a comprehensive guide to harnessing the power of data for successful and impactful campaigns.
Offering a thorough exploration of the symbiotic relationship between data engineering and modern marketing strategies, Data Engineering for Data-Driven Marketing uses a strategic lens to delve into methodologies of collecting, transforming, and storing diverse data sources. With real-world case studies actionable insights, the chapters within navigate the reader through the intricate process of building robust data pipelines, optimizing data quality, and implementing real-time processing techniques, all tailored to elevate marketing campaigns through precision targeting, customer personalization, and predictive modelling.
Table of Contents:
Chapter 1. Exploring AI in Data Driven Marketing: Understanding the Intersection of Data Engineering and Marketing; Sonal Trivedi, Veena Grover, and Balamurugan Balusamy
Chapter 2. Architecting for Success: Designing Robust Data Infrastructures to Power Data-Driven Marketing Campaigns; Siva Karthikeyan Krishnan, Kumaravel Ponnusamy, and Kanav Sharma
Chapter 3. AI in Data-Driven Marketing: Decoding Consumer Choices and Behaviors; Valliappan Raju, KK Ramachandran, Wang Chenxi, Mangairkarasi V, and Zdenka Konecna
Chapter 4. Ingesting Insights: Data Ingestion Strategies and Techniques for Marketing Data; Veena Grover and Purnima Pal
Chapter 5. Mastering Data Transformation: Preparing Marketing Data for Actionable Insights; Channi Sachdeva, Veena Grover, Amandeep Kaur, and Veer P Gangwar
Chapter 6. Seamless Data Flow: Constructing End-to-End Data Pipelines for Real-Time Marketing Analytics; Anitha K, Anitha A, Preetha S, and Annie Sam
Chapter 7. Crafting Customer Profiles: Data Engineering for Comprehensive Customer Understanding; Anuradha Chakraborty, Soumendra Roy, and Amit Sarkar
Chapter 8. Empowering Personalized Marketing: Leveraging Data Engineering for Customer Segmentation; Veena Grover, Mahima Dogra, Divya Sahu, and Manju Nandal
Chapter 9. Quality Assurance in Marketing Data: Ensuring Accuracy and Reliability; Arvind Nath Sinha, Vibha Srivastava, and Kashvi Sinha
Chapter 10. Integrating Marketing Data Ecosystems: Merging Diverse Data Sources for Holistic Insights; Rubina Gill, Pankaj Raj Kumar, Mastu Patel, and Harmesh Kumar
Chapter 11. Data Security in the Age of Marketing: Safeguarding Customer Information and Compliance; M. K. Nallakaruppan, Ansh Pethani, and Danilo Pelusi
Chapter 12. Harnessing AI and ML for Marketing: Integrating Advanced Analytics into Data-Driven Strategies; M. K. Nallakaruppan, Francesco Benedetto, and Mridul Jain
Chapter 13. Ethics and Privacy in Data-Driven Marketing: Navigating Legal and Ethical Considerations in the Age of Big Data; Manisha Singh and Fatima Qasim Hasan
Chapter 14. Unlocking the Potential of Data Lakes: Organizing and Storing Marketing Data for Analysis; Magesh R, Ilakkiyaa U, Shanthini R, and Charanya R
Chapter 15. Optimizing Outcomes: Professional Perspectives on Data-Driven Marketing Mastery; Aditya Prasad, Tejasv Rastogi, Trisha Paul, Utkarsh Tyagi, and Francesco Benedetto
About the Author :
Balamurugan Balusamy is Associate Dean at Shiv Nadar University, Delhi-NCR following being Professor, School of Computing Sciences and Engineering as well as Director International Relations at Galgotias University, Greater Noida, India.
Veena Grover is a distinguished Economics professional currently Professor of Economics at Noida Institute of Engineering & Technology, Greater Noida, India.
M. K Nallakaruppan is Assistant Professor at the School of Information Technology and Engineering, Vellore Institute of Technology, India. He is an Associate Member of the IEEE and Member of Soft Computing Research Society.
Vijay Anand Rajasekaran is Associate Professor in VIT University, India following his time as Senior Technical Lead at HCL-CISCO, India. His contributions focus on Engineering Education, Block chain and Data Sciences.
Mariofanna Milanova is Professor in the Department of Computer Science for University of Arizona at Little Rock, USA, and has been a faculty member since 2001. She is an IEEE Senior Member, Fulbright U.S. Scholar, and NVIDIA Deep Learning Institute University Ambassador.
