Course Outline 

Module 1: Introduction to Predictive Analytics in Marketing

• Basics of predictive analytics & machine learning

• Applications in marketing: churn, CLV, campaign optimization

• Overview of tools: Python, R, Power BI, Excel
  Case Study: How predictive analytics transformed customer retention in retail

Module 2: Data Collection & Preparation for Marketing Analytics

• Gathering data from CRM, GA4, Ads platforms, and social media

• Data cleaning and preprocessing (Excel & Python Pandas)

• Feature engineering for marketing data
  Exercise: Prepare a dataset for churn prediction

Module 3: Customer Segmentation & Clustering

• K-means and hierarchical clustering

• Identifying high-value segments

• Targeting campaigns based on segments
  Case Study: Segmenting customers for a loyalty program

Module 4: Regression Models in Marketing

• Linear and logistic regression explained

• Predicting lead conversion probability

• Evaluating model accuracy (R², AUC, confusion matrix)
  Practical: Build a regression model in Excel & Python

Module 5: Classification Models for Marketing Predictions

• Decision trees, random forests, and gradient boosting

• Predicting customer churn and response likelihood

• Hands-on with Python Scikit-learn & R
  Case Study: Predicting churn in a subscription business

Module 6: Time-Series Forecasting for Marketing

• Basics of time-series (ARIMA, Prophet, Exponential Smoothing)

• Forecasting sales, campaign results, and seasonal trends

• Using Power BI & Python Prophet for forecasting
  Exercise: Build a sales forecast for a marketing campaign

Module 7: Customer Lifetime Value (CLV) Prediction

• Understanding CLV and its business importance

• Calculating CLV in Excel & Power BI

• Predictive CLV models in Python/R
  Case Study: Predicting CLV for a B2C e-commerce brand

Module 8: Lead Scoring & Predictive Campaign Targeting

• Building predictive lead scoring models

• Integrating predictive analytics with CRM systems

• AI-driven personalization in campaigns
  Exercise: Design a predictive lead scoring model

Module 9: AI & Advanced Predictive Marketing Tools

• Using AI tools for predictive insights (Google AI, Azure ML, Salesforce Einstein)

• Real-time predictive analytics in digital campaigns

• Ethical considerations & bias in predictive models
  Case Study: AI-driven ad targeting with predictive analytics

Module 10: Capstone Project – Predictive Marketing Strategy

• Building an end-to-end predictive marketing solution

• Combining segmentation, churn, CLV, and forecasting models

• Presenting insights with Power BI dashboards
  Capstone Project: Design and present a predictive analytics framework for a real-world marketing scenario