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Microsoft Dynamics 365 AI for Business Insights

You're reading from  Microsoft Dynamics 365 AI for Business Insights

Product type Book
Published in Mar 2024
Publisher Packt
ISBN-13 9781801810944
Pages 178 pages
Edition 1st Edition
Languages
Author (1):
Dmitry Shargorodsky Dmitry Shargorodsky
Profile icon Dmitry Shargorodsky

Table of Contents (18) Chapters

Preface 1. Part 1: Foundations of Dynamics 365 AI
2. Chapter 1: Introduction and Architectural Overview of Dynamics 365 AI 3. Chapter 2: Microsoft Dynamics 365 AI Architecture and Foundations 4. Part 2: Implementing Dynamics 365 AI Across Business Functions
5. Chapter 3: Implementing Dynamics 365 AI for Sales Insights 6. Chapter 4: Driving Customer Service Excellence with Dynamics 365 AI 7. Chapter 5: Marketing Optimization with Dynamics 365 AI 8. Chapter 6: Financial Analytics with Dynamics 365 AI 9. Part 3: Advanced Applications and Future Directions
10. Chapter 7: Leveraging Generative AI in Dynamics 365 11. Chapter 8: Harnessing MS Copilot for Enhanced Business Insights 12. Chapter 9: “Virtual Agent for Customer Service” in the Context of MS Copilot and Microsoft Dynamics 13. Chapter 10: Fraud Protection with Dynamics 365 AI 14. Part 4: Looking Ahead
15. Chapter 11: Future Trends and Developments in Dynamics 365 AI 16. Index 17. Other Books You May Enjoy

Leveraging AI for customer segmentation and targeting

In today’s data-rich digital landscape, the role of AI in revolutionizing customer segmentation and targeting is undeniable. Dynamics 365, combined with advanced AI algorithms, elevates this revolution, bringing forth intricate, data-driven, and actionable insights that empower businesses to interact with their audiences in more personalized and impactful ways.

Segmentation beyond the surface

Historically, customer segmentation was largely demographic. Businesses would categorize their audience based on age, gender, geographic location, and perhaps income. But with the introduction of AI, this has evolved dramatically. Now, companies can not only use demographic data but can also delve deep into psychographic segmentation, categorizing customers based on lifestyles, behaviors, and preferences.

Consider a streaming service platform that previously segmented its audience as “young adults aged 18-25.”...

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