AI for Executives: AI for Customer Relationship Management

Übersicht

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.

Lehrplan

• Module 1: Introduction to AI in Customer Relationship Management (CRM)
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.
• Module 2: AI-powered Customer Data Management
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.
• Module 3: AI-Driven Customer Engagement Strategies
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.
• Module 4: Automation and Efficiency in CRM Processes
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.
• Module 5: Integrating AI into CRM Strategy and Future Trends
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.

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Unterrichtet von

Prof. Ernesto Damiani

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Fachgebiete

Business