Predictive Customer Behavior Modeling AI Full Syllabus

Module 1: Introduction to Customer Behavior Prediction

• What is customer behavior modeling?
• Importance in e-commerce, SaaS, banking, retail
• Key behaviors to predict:
  • Purchase likelihood
  • Churn (customer leaving)
  • Click-through rate
  • Product return
  • Customer lifetime value (CLTV)
• Real-world case studies: Amazon, Netflix, Zomato

Module 2: Data Collection & Preprocessing

• Behavioral data sources:
  • Website/app events (clicks, scrolls, time spent)
  • CRM data (calls, emails, support logs)
  • Transaction history (purchases, refunds, cart)
  • Demographics, location, device info
• Cleaning and preparing data:
  • Handling missing data
  • Encoding categorical variables
  • Normalization & standardization

Module 3: Exploratory Data Analysis (EDA)

• User segmentation and persona identification
• Behavioral funnel analysis
• Recency, Frequency, Monetary (RFM) analysis
• Heatmaps, time series of user activity
• Cohort analysis

Module 4: Feature Engineering for Behavior Prediction

• Creating features from time series & logs
  • Last purchase time
  • Number of items in cart
  • Number of support tickets
• Rolling averages, decay functions
• Feature selection and dimensionality reduction (PCA, Autoencoders)

Module 5: Machine Learning Models

• Classification models for behavior prediction:
  • Logistic Regression
  • Random Forest, XGBoost
  • Neural Networks (basic MLP)
  • KNN, Naive Bayes
• Regression models for value prediction:
  • Linear/Polynomial Regression
  • LSTM for time-based value predictions
• Model evaluation:
  • Accuracy, Precision, Recall
  • ROC-AUC, Confusion matrix
  • MAE, RMSE for regression

Module 6: Predicting Churn, Purchases & Retention

• Building a churn prediction model
• Purchase intent prediction
• User retention score modeling
• Targeting “at-risk” users with predictions
• Real-world implementation examples

Module 7: Customer Lifetime Value (CLV) Prediction

• What is CLV and why it matters
• RFM + probabilistic models
• Survival analysis & cohort-based CLV
• Using Deep Learning (LSTM) for dynamic CLV prediction

Module 8: Personalization Based on Predictions

• How prediction outputs are used:
  • Dynamic recommendations
  • Targeted emails/SMS
  • Personalized offers & upselling
  • Support prioritization
• A/B testing behavior-based campaigns

Module 9: Tools, Frameworks & Platforms

• Python: Pandas, scikit-learn, XGBoost, Keras
• Google Analytics + BigQuery
• Customer.io, Mixpanel, Segment
• CRM + AI platforms: HubSpot, Salesforce AI, Zoho CRM
• ML pipelines with Vertex AI, AWS Sagemaker

Module 10: Ethics, Privacy & Responsible AI

• Data privacy (GDPR, CCPA) in behavioral prediction
• Avoiding biased models (race, gender, income bias)
• Explainability (SHAP, LIME) for trust
• Transparent usage policies for customers