What is Predictive Analytics?
Predictive Analytics — The use of historical data, statistical algorithms, and ML to identify the likelihood of future outcomes.
Predictive analytics uses historical data and statistical models to forecast future events. Common applications include demand forecasting, churn prediction, preventive maintenance scheduling, and risk scoring. It is one of the most mature and proven applications of AI in business.
Frequently Asked Questions
How accurate is predictive analytics?
Accuracy depends heavily on data quality and the predictability of the phenomenon. Weather forecasts, demand prediction, and churn models routinely achieve 80-95% accuracy. Novel events with no historical precedent are inherently unpredictable.
What data do I need?
Historical records of the outcome you want to predict, plus relevant features that influence that outcome. More history and more features generally improve predictions, but quality matters more than quantity.
How is predictive analytics different from generative AI?
Predictive analytics forecasts outcomes (will this customer churn?). Generative AI creates new content (write an email to this customer). They solve fundamentally different problems.