- Descriptive Analytics
What it is: Descriptive analytics tells you what has happened. It takes raw data and summarizes it so you can see patterns, trends and performance outcomes.
How it’s used:
• Generating regular reports like monthly sales, revenue trends or customer engagement statistics.
• Creating dashboards that show key performance indicators (KPIs) at a glance.
• Tracking historical results to establish a factual baseline for future planning.
- Diagnostic Analytics
What it is: Diagnostic analytics digs into the data to explain “why something happened.” It goes beyond just reporting results and explores relationships, patterns and underlying causes.
How it’s used:
• Identifying reasons for drops in performance or spikes in demand.
• Examining correlations between variables that could explain outcomes.
• Segmenting data to uncover hidden drivers.
- Predictive Analytics
What it is: Predictive analytics answers “what might happen next?” It uses historical data combined with statistical models and machine learning to forecast future outcomes.
How it’s used:
• Forecasting sales or demand for products.
• Anticipating customer churn or credit risk.
• Identifying likely trends in supply chain performance.
- Prescriptive Analytics
What it is: Prescriptive analytics goes one step further by recommending actions that improve outcomes. It combines predictions with optimization techniques to suggest the best possible decisions.
How it’s used:
• Recommending optimal delivery routes in logistics to reduce costs.
• Suggesting marketing strategies based on future customer behavior.
• Guiding resource planning in healthcare or manufacturing.
These four analytics types build on one another. Descriptive gives you the story of what happened. Diagnostic explains why it happened. Predictive forecasts what could happen next. Prescriptive tells you what to do about it. Successful analytics programs often integrate all four to support strategic decisions, operational improvements and future planning.