The Power of Personalized Customer Experiences Through AI Innovations

Overview

Discover how to build recommender engines and implement AI-driven personalization strategies that enhance customer experiences and drive business growth through practical demonstrations.

Syllabus

• Introduction to AI in Personalization
Overview of AI technologies in customer personalization
Importance of personalized customer experiences for business growth
• Understanding Customer Data
Types of customer data: behavioral, demographic, transactional
Data collection and preprocessing techniques
• Fundamentals of Recommender Systems
Types of recommender systems: collaborative filtering, content-based, hybrid
Key metrics for evaluating recommendation systems
• Building Collaborative Filtering Recommender Systems
User-based and item-based collaborative filtering methods
Implementation with practical examples
• Content-Based Recommender Systems
Feature extraction and similarity measures
Building a content-based recommender with case studies
• Hybrid Recommender Systems
Combining collaborative and content-based approaches
Implementing hybrid systems for improved accuracy
• AI-Driven Personalization Strategies
Personalization techniques: targeting, segmentation, customization
Case studies of successful AI-driven personalization
• Machine Learning Models for Personalization
Supervised and unsupervised learning applications
Training and deploying models for real-time personalization
• Ethical Considerations in AI Personalization
Data privacy and security concerns
Transparency and explainability in AI-driven recommendations
• Emerging Trends in AI Personalization
Customer journey mapping and omnichannel personalization
Future innovations and the role of AI in evolving customer experiences
• Practical Demonstrations and Project Work
Hands-on workshops for building recommender systems
Group projects to implement AI personalization strategies in a business context
• Course Review and Final Assessment
Summary of key concepts
Final project presentations and feedback sessions

Subjects

Data Science