Prescriptive Analytics

This is the final post in my series on Return on Information. Previously I addressed Descriptive and Predictive Analytics, this time we move into Prescriptive. This type of Analytics is for me especially interesting while it really gives return on the investments in information harvest. This type of analytics helps business managers translate their forecasts and business information into actionable, practicable plans.

Prescriptive Analytics – two approaches

I already spilled in beans in the start of this blog; Prescriptive analytics helps business managers translate their forecasts and business information into actionable, actionable plans. Prescriptive Analytics can be realized after two different main patterns (simulation and optimization). Each of the patterns has its applied usage. The optimization pattern has a wide tactical aim, such as impacts of different forecasts. And a Simulation pattern, which is best when applied to various design scenarios. It can help identify system behaviors under different configurations, and ensures all key performance metrics are met.

Markdown is a great example of this type of analytics.

The intent of markdown is to bridge the prescriptive analytics the actual decision making. It might optimize for different goals, one being that you need your merchandise sold within 8 weeks, still retaining as much revenue as possible. Among many factors is the requirement of optimizing such a scenario a great control over price elasticity, a corporate wide holistic inventory overview, focus on sales frequency, and not least the ability implement prices at the outlets quickly and without any delay

Other examples

In short is prescriptive analytics great applied to pricing, inventory management, operational resource allocation, production planning, supply chain optimization and utility management, sales lead assignment, marketing mix optimization and financial planning.

Imagine within transportation and distribution planning, the ability to analyze based on numerous factors, and still make shift decisions, is absolutely key to operate within that marked.

But also for services, imagine airline ticketing. Their pricing systems use prescriptive analytics to sort through complex combinations and the factors, demand levels and purchase timing to bill potential passengers with the highest price possible, not damaging client retention, or hurt their profit.

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