AI for Executives: AI for Customer Relationship Management

本课程是 AI for Executives 专项课程的一部分

位教师：Prof. Ernesto Damiani

访问权限由 New York State Department of Labor 提供

5个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

5 小时完成

灵活的计划

自行安排学习进度

5个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

5 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Build unified customer profiles; apply predictive analytics for churn, next-best-action, and customer lifetime value.
Design personalized engagement using recommendations, intelligent virtual assistants, and LLM-powered support.
Implement responsible AI in CRM—privacy, security, fraud prevention—and create an executive roadmap aligned to business goals and change management.

您将获得的技能

要了解的详细信息

可分享的证书

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作业

5 项作业

授课语言：英语（English）

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Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 AI for Executives 专项课程专项课程的一部分

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该课程共有5个模块

AI for Customer Relationship Management (CRM) shows executives how to use artificial intelligence to deepen customer insight, personalize engagement, and scale service—without coding. You’ll build richer customer profiles by integrating behavioral, transactional, and external data; explore graph-based community modeling to understand relationships; and apply predictive analytics for churn, next-best-action, and customer lifetime value. You’ll practice sentiment and voice analytics to capture the “voice of the customer,” then design personalized experiences with recommendations, targeted campaigns, and human-in-the-loop programs. The course also covers intelligent virtual assistants and LLM-powered support, sales enablement (lead scoring, cross-sell), dynamic pricing, and fraud detection with security and compliance in mind. Finally, you’ll develop an AI-driven CRM strategy—roadmapping technology choices, aligning initiatives to business goals, managing change, and addressing ethics and governance. By the end, you’ll be able to evaluate use cases, choose appropriate techniques, and lead responsible AI adoption across marketing, sales, and service.

This module grounds executives in CRM fundamentals and how AI elevates engagement, personalization, and measurement across the customer lifecycle. You’ll connect CRM components with outcome metrics, surface near‑term AI opportunities, and examine privacy/security trade‑offs. A light introduction to graph‑based community modeling helps you visualize customer networks for segmentation and outreach.

涵盖的内容

6个视频5篇阅读材料1个作业1个讨论话题

6个视频总计20分钟

Introduction to the Specialization 2分钟
Definitions and Components of CRM 4分钟
The AI Promise 3分钟
Enhancing Customer Engagement Through AI 5分钟
Using LLMs for CRM 4分钟
LLM Applications for CRM 2分钟

5篇阅读材料总计50分钟

Definitions and Components of CRM Briefing Document 10分钟
The AI Promise Key Concepts 10分钟
Enhancing Customer Engagement Through AI Briefing Document 10分钟
Using LLMs for CRM Key Topics 10分钟
LLM Applications for CRM Key Topics 10分钟

1个作业总计30分钟

Module 1 Quiz 30分钟

1个讨论话题总计10分钟

Design a Simple Ranking Rule (EdgeRank‑style) 10分钟

Focus on the data foundations of AI for CRM: what to capture, how to integrate, and how to safeguard quality and governance. You’ll interpret outputs from predictive models (e.g., churn) and run a practical sentiment/voice‑of‑customer workflow—including multilingual considerations—to turn unstructured feedback into action.

涵盖的内容

1个视频1篇阅读材料1个作业

Translate AI capabilities into engagement levers: personalization, assistants, and sales enablement. You’ll experiment with content/recommendation strategies, design and assess chatbot/voice‑assistant experiences, and apply LLM prompt design and specialization—all tied to revenue outcomes via lead scoring and cross/upsell.

涵盖的内容

2个视频2篇阅读材料1个作业1个讨论话题

Learn where automation moves the needle: dynamic pricing, revenue optimization, and fraud/security in customer interactions. You’ll estimate price responsiveness, design fraud‑prevention workflows with human‑in‑the‑loop review, and specify process KPIs to quantify efficiency and risk reduction.

涵盖的内容

1个视频1篇阅读材料1个作业

Bring it all together into an executive‑ready plan. You’ll align AI initiatives with business goals using a Goal–Question–Metric (GQM) approach, produce a technology roadmap, and address change management, ethics, and risk so your AI‑CRM strategy is viable, governable, and future‑proof.