Predictive Analytics

Definition

Predictive analytics uses historical data, statistical algorithms, and machine learning techniques to identify patterns and predict future outcomes and trends. Instead of just reporting what happened (descriptive analytics) or explaining why it happened (diagnostic analytics), predictive analytics forecasts what is likely to happen. In marketing, predictive analytics applications include: lead scoring (predicting which leads will convert), churn prediction (identifying customers likely to leave), customer lifetime value forecasting, next-best-action recommendations (what offer to make), demand forecasting, and pricing optimization. The process involves: collecting relevant historical data, identifying patterns and correlations using ML algorithms, building predictive models, validating accuracy, and continuously refining models as new data arrives. Predictive models improve with more data and time. Accuracy varies—70-80% prediction accuracy is common for lead scoring, enabling significantly better decisions than guessing or intuition (typically 50-60% accurate).