Prescriptive analytics builds upon the three other types of data analytics which describe the present and make predictions about the future. It then uses heuristics, machine learning and rule-based systems to make specific recommendations based on data and probability-weighted projections.
Let’s look a bit deeper at the different processes and stages of human input for each. You can see below that only prescriptive analysis takes you all the way to a specific recommended next step you should take (“Action”). Certain situations require human intuition and judgment and in these cases, it can provide decision support rather than decision automation.
Here are some common examples of prescriptive analytics and types of prescriptive insights provided by advanced AI analytics tools.
Reduce risk by automatically analyzing credit risk or loan default likelihood.
Provide better patient care based on patient admission and readmission forecasting.
Deliver more consistent service by predicting peak demand cycles.
Automatically set pricing and marketing messages to increase customer repurchase propensity.
Determine the most efficient and effective territory alignment.
Optimize investments in transportation infrastructure based on population density.
Segment customer base and promote optimal packages and pricing.
Ensure ability to fulfill orders by accurately forecasting demand.
The high-level prescriptive analytics workflow is similar to the traditional machine learning or AI workflow except that instead of leading to predictive analytics and what-if scenarios, it leads to recommended actions.