What is predictive analytics?
Predictive analytics is the use of historical data, statistical algorithms, predictive modeling, and big data machine learning techniques to help organizations predict future outcomes more accurately, plan for unknown events, and discover opportunities in future activities.
As a branch of data science for business, the growth of predictive and augmented analytics coincides with that of big data systems, where larger, broader pools of data enable increased data mining activities to provide predictive insights. Advancements in big data machine learning have also helped expand predictive analytics capabilities.
Predictive analytics includes a variety of statistical techniques that analyze current and historical facts to make predictions about the future. With the help of sophisticated tools, as well as big data machine learning and AI models, organizations can use historic and current data to reliably forecast trends and behaviors seconds, days, or years into the future with a great deal of precision.
Experienced data scientists use predictive models to identify correlations between different elements in selected datasets. Once data collection is complete, a statistical model is formulated, trained, and modified to generate accurate predictions.
Predictive analytics examines all actions on a company’s network in real time to pinpoint abnormalities that indicate fraud and other vulnerabilities.
Companies use predictive analytics models to forecast inventory, manage resources, and operate more efficiently.
By dividing a customer base into specific groups, marketers can use predictive analytics to make forward-looking decisions to tailor content to unique audiences.
Conversion and purchase prediction
Companies can take actions, like retargeting online ads to visitors, with data that predicts a greater likelihood of conversion and purchase intent.
Credit scores, insurance claims, and debt collections all use predictive analytics to assess and determine the likelihood of future defaults.
Organizations use data to predict when routine equipment maintenance will be required and can then schedule it before a problem or malfunction arises.
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