Prediction is the killer app for AI in business. Know what's coming, and you can prepare, prevent, or capitalize.
Prediction Use Cases
Demand Forecasting: What will customers want, when, and where?
Churn Prediction: Which customers are about to leave?
Risk Scoring: Which transactions, applications, or events are risky?
Maintenance Prediction: When will equipment fail?
Sales Forecasting: What revenue can we expect?
Building Predictions
1. Define the problem: What exactly are you predicting?
2. Gather data: Historical outcomes and relevant features.
3. Build models: Test multiple approaches.
4. Validate: Ensure predictions are accurate and unbiased.
5. Deploy: Put predictions into decision workflows.
6. Monitor: Track performance over time.
Data Requirements
Good predictions need:
- Sufficient historical data
- Relevant feature data
- Clean, consistent data
- Representative samples
Acting on Predictions
Prediction alone isn't valuable. You need:
- Decision rules for different predictions
- Systems to deliver predictions at point of decision
- Feedback loops to improve over time
Common Pitfalls
- Predicting things you can't act on
- Ignoring prediction confidence
- Not validating on held-out data
- Deploying without monitoring