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Predictive analytics is the study of historical data to make future predictions.
By interpreting data, businesses can make decisions that will improve their performance, says Smirnov.
So, predictive analytics is nothing new, says Meier.
The key strength of predictive analytics lies in its ability to turn massive amounts of data into actionable insights.
This helps businesses anticipate opportunities and risks before they arise.
The process of teaching a model to recognize these patterns from historical data is called training.
Once trained, the model can be used to make predictions.
PA use cases
Arunkumar Thirunagalingam, Manager of Enterprise Data Management at Santander Consumer USA, agrees.
It also helps stores like Walmart optimize supply chains by forecasting demand and managing inventory.
This helps ensure the stores have the right products available at the right time.
Kaveh Vahdat, Founder and President at RiseOpp shares a couple of interesting use cases for predictive analytics.
This allows companies to preemptively adjust their strategies, optimizing content and ad placements for maximum engagement and conversion.
Vahdat says predictive analytics is equally crucial in the gaming industry.
His company, RiseAngle leverages generative AI to create personalized gaming experiences.
Predictive analytics often uses ML algorithms to enhance its forecasting capabilities, adds Vahdat.
Essentially, predictive analytics is a subset of AI that leverages ML to refine its predictions.
