Predictive analytics is a specialized branch of data analytics that combines the power of data mining, data modeling, artificial intelligence, and machine learning to make probabilistic predictions of future events. DATAVERSITY brought this interesting information to our attention in their article, “Fundamentals of Predictive Analytics.”

Predictive analytics relies heavily on the theoretical foundations of statistics to enable modeling of future behavior based on historical data. It is commonly used to detect fraud, predict customer behavior, plug revenue leakages, optimize marketing programs, and reduce risks. If a business can determine which customers are likely to leave, then it can offer timely discounts or other tempting incentives to retain such customers.

For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs, uses predictive analytics, the core of which relies on capturing relationships between explanatory variables and the predicted variables from past occurrences and exploiting them to predict an unknown outcome.

Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores for each individual organizational element. This is what distinguishes it from forecasting.

Melody K. Smith

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