- Level: Beginner
- Prerequisites: None
- Assessments: Weekly Micro Assessments; Full Assessment in the Final Week
Week 1: Introduction to Retail AI
Learning Outcome: Understand how AI is transforming retail and its applications.1.1 Basics of personalization in retail.
1.2 Role of customer behavior data in AI models.
1.3 Examples of AI-driven customer experiences.
Practical Component
Explore how simple recommendation systems work.
Week 2: Data Understanding Customer Preferences
Learning Outcome: Learn to analyze customer behavior using AI.1.4 Data collection from customer interactions.
1.5 Basics of feature engineering for retail data.
1.6 Simple customer clustering methods.
Practical Component
Use clustering to group customer preferences.
Week 3: Creating Recommendation Engines
Learning Outcome: Build basic AI models to recommend products.1.7 Content-based recommendations.
1.8 Collaborative filtering.
1.9 Hybrid recommendation systems.
Practical Component
Create a simple recommendation engine.
Week 4: Evaluating and Improving AI Models
Learning Outcome: Measure the success of your recommendations.1.10 Key performance metrics.
1.11 Fine-tuning recommendations.
1.12 Handling real-world challenges.
Practical Component
Evaluate and improve an existing model.
Week 5: Advanced Personalization Strategies
Learning Outcome: Use AI to predict future customer needs.1.13 Predicting trends and future preferences.
1.14 Automating personalization with AI.
1.15 Testing personalization strategies in live environments.
Practical Component
Test advanced personalization methods.
Week 6: Final Project
Learning Outcome: Implement a real-world personalization use case.1.16 Define a use case and collect relevant data.
1.17 Build and test your AI solution.
1.18 Present your results and get peer feedback.
Practical Component
Build and demonstrate your own AI solution for retail personalization.