Predictive analytics describes a range of analytical and statistical techniques used for developing models that may be used to predict future events or behaviors. There are different forms of predictive models, which vary based on the event or behavior that is being predicted. Nearly all predictive models produce a score; a higher score indicates that a given event or behavior is very likely to occur.
Predictive analytics, along with data mining techniques and predictive models, relies on multivariate analyzing techniques, including time-series or advanced regression models. These techniques allow organizations to decide on relationships and trends and predict future behaviors or events.
Some common examples of predictive models are those used by credit bureaus for developing credit scores. The credit bureaus make use of information, including income, outstanding loan balances, credit history and so on, to develop a credit score for predicting whether the person is likely to be able to repay his or her present and future debts.
Predictive analytics offers the following benefits:
- Provides a quantitative foundation to rapidly recognize, confidently practice and rationally assess opportunities
- Helps to identify the type of individuals to target, how to get in touch with them, when to contact them and what messages should be used for communicating with them
Organizations that make use of predictive analytics in their day-to-day activities achieve the following benefits:
- Enhance business processes to improve decision-making and achieve the ability to automate, optimize and direct decisions as per demand to reach business goals
- Manage current challenges and boost the probability of future success
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