AI Marketing Prediction Lab
Understand and visualize how neural 🧠 networks (an "A.I." tool) can be designed to predict consumer behavior
What is this tool?
Neural networks learn patterns from data by adjusting internal weights through training. This playground lets you experiment with different network architectures and see how they learn to classify marketing scenarios like customer segments, churn prediction, and A/B test outcomes.
📊 Step 1: Choose Your Business Problem
Select a real marketing scenario. Each has different patterns the network must learn.
Customer Churn
Will customers stay or leave?
Market Segments
Identify customer groups
A/B Test
Predict which version converts
Product Affinity
Who will buy together?
Current Scenario:
Predict customer churn based on pricing and service quality. Blue = likely to stay, Red = likely to churn.
🧠 Step 2: Design Your Model
Configure what information the network uses and how it processes it.
Input Features
Network Architecture
Advanced Settings
🎯 Step 3: Model Training
Network Architecture
Training Progress
✅ Step 4: Model Validation
Decision Boundary
Background = predictions | Dots = actual data
Color Intensity = Confidence
Deep Color = Certain | Grey = Uncertain
Performance on Unseen Data
Test Accuracy
Percentage of test data points correctly classified
🤔 Understanding Validation
- Test data were NOT used during training
- Good fit: Similar performance on train/test
- Overfitting: Great train, poor test
Train your model to see validation results...