Prescriptive analytics uses outcomes and scenarios to answer the question “What should we do to make things happen?” It takes predictive analytics a step further to define actions that will affect outcomes, rather than just predicting the outcomes from actions that have occurred.
Prescriptive analytics shows the implication of various decisions to anticipate what, when, and why an event will happen. In a way, prescriptive analytics combines elements of predictive analytics and descriptive analytics to arrive at actual solutions.
Prescriptive Analytics Use Cases:
- Assist medical practitioners in quickly deciding patient treatment procedures
- Optimize products and prices
- Identify micro-markets to manage the supply chain
- Decide where to target a marketing campaign
- Reduce operational cost
- Decide which offer a customer is likely to respond to
Beware of assumptions made by the application that contradict common sense or indicate gaps in the methodology setting up the prescriptive analytics scenarios.
Other Definitions of Prescriptive Analytics Include:
- “Identifying the best possible action to take given the constraints and the objective.” ( Brown University)
- “Assessing the likely outcome of different actions to inform the best course of action to achieve and optimal business result.” (Kimberly Nevala)
- “Any combination of analytics, math, experiments, simulation and/or artificial intelligence used to improve the effectiveness of decisions made by humans or by decision logic embedded in applications.” (Forrester)
- A system leveraging “data and machine learning to provide specific, actionable leadership suggestions in real time.” (Forbes)
- Determining how to make something happen. (ZDNet)
Businesses Use Prescriptive Analytics to:
- Get the best solution to an operational problem.
- Increase profit margins.
- Get ready-made business solutions in an emergency.
- Inform and evolve decision logic.
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