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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

AI-powered sentiment analysis and customer 
sentiment tracking

In the landscape of AI-enhanced customer service, sentiment analysis and customer sentiment tracking stand out as pivotal tools. These AI-driven capabilities extend the function of virtual agents and chatbots beyond mere transactional interactions, allowing them to gauge and respond to emotional undertones in customer communications. This section explores the technical aspects, applications, and impacts of sentiment analysis in modern customer service.

Technical aspects of sentiment analysis

Sentiment analysis in AI systems primarily relies on NLP and ML. NLP interprets and understands human language, enabling the AI to process not only what is being said but also how it’s being said. By analyzing word choice, sentence structure, and even the rhythm of the customer’s language, the AI can identify whether the sentiment is positive, negative, or neutral.

ML for enhanced sentiment detection

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