Predictive analytics: Difference between revisions

Revision as of 12:55, 13 February 2025

Revision as of 13:04, 13 February 2025

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The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement.

Artificial intelligence in predictive analytics transforms raw data into actionable intelligence. For instance, by analyzing past customer behavior, a predictive model can forecast future buying patterns. Similarly, in healthcare, AI-powered models can predict patient outcomes, assisting healthcare providers in crafting preemptive treatment plans.<ref>{{Cite web |last=Stihec |first=Jan |date=2024-12-09 |title=How to Use AI for Predictive Analytics and Smarter Decision Making |url=https://shelf.io/blog/ai-for-predictive-analytics/ |access-date=2025-02-13 |website=Shelf |language=en-US}}</ref>

== Definition ==