Customer Data & Behavior Analytics: AI for Personalization and Retention

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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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