What Is the Difference Between Descriptive, Predictive, and Prescriptive Analytics?

Descriptive analytics explains what happened, predictive analytics estimates what may happen, and prescriptive analytics recommends what actions could be taken. Businesses can use these three approaches together to understand past performance, anticipate future outcomes, and make more informed decisions.

Descriptive vs. Predictive vs. Prescriptive Analytics

1. Descriptive Analytics — What Happened?

Descriptive analytics analyzes historical data to understand past performance.

Example:
A retailer analyzes last year’s sales by product, region, and month.

Common outputs include:

• Reports
• Dashboards
• Sales summaries
• Performance metrics

2. Predictive Analytics — What Could Happen?

Predictive analytics uses historical data, statistical methods, and, where appropriate, machine learning to estimate potential future outcomes.

Example:
A retailer predicts which products may experience higher demand next month.

It can support:

• Demand forecasting
• Customer churn prediction
• Risk assessment
• Maintenance prediction
• Sales forecasting

3. Prescriptive Analytics — What Should We Do?

Prescriptive analytics goes a step further by evaluating potential actions and their expected outcomes.

Example:
A retailer uses demand forecasts to determine how much inventory to order and when.

Simple Comparison

Type	Main Question	Example
Descriptive	What happened?	Last month’s sales
Predictive	What might happen?	Next month’s demand
Prescriptive	What should we do?	Recommended inventory level

Final Thoughts

These approaches are complementary. A business may first understand what happened, estimate what could happen, and then determine what action may be appropriate.