Customer Data & Behavior Analytics: AI for Personalization and Retention
artificial-intelligence-ai.
Course Modules:
Module 1: Introduction to Customer Data Analytics
Importance of customer behavior analysis in FinTech, eCommerce, and SaaS
Types of customer data: demographic, transactional, interactional
Overview of the analytics lifecycle: collect → clean → model → act
Module 2: Data Preprocessing and Feature Engineering
Aggregating and transforming raw behavioral data
Deriving RFM features (Recency, Frequency, Monetary)
Handling time-based and event-driven data (e.g., clicks, purchases, logins)
Module 3: Segmentation and Clustering
K-means, DBSCAN, and hierarchical clustering
Customer personas and lifecycle mapping
Targeting segments with personalized strategies
Module 4: Predictive Behavior Modeling
Churn prediction using classification models (Logistic Regression, Random Forest)
Conversion likelihood and upsell prediction
Behavioral scoring and risk modeling
Module 5: Personalization and Real-Time Analytics
Recommendation engines (collaborative, content-based, hybrid)
Real-time user tracking and dynamic targeting
A/B testing and uplift modeling
Module 6: Capstone Project – Customer Behavior Dashboard
Choose a dataset (e.g., eCommerce users, banking customers, SaaS accounts)
Build a segmentation or churn prediction model
Submit a visual dashboard and business insight report
Tools & Technologies Used:
Python
Pandas, NumPy, Scikit-learn, XGBoost
Seaborn, Matplotlib, Plotly for visualization
Streamlit or Tableau for dashboard creation
Target Audience:
Product managers and digital marketers
Data analysts and customer success professionals
AI practitioners building customer intelligence tools
Students entering data-driven business roles
Global Learning Benefits:
Understand customer journeys using machine learning
Predict churn and personalize outreach effectively
Build dashboards and models that inform business decisions
Combine AI with marketing and CX strategies to drive retention
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