Predictive analytics uses historical data to analyze past patterns and predict future patterns, helping businesses discover potential opportunities and evaluate risks. It is used in industries such as finance, insurance, healthcare, retail, and government to predict consumer behavior and assess aggregate demand.
In the business world, predictive analytics is the process of using historical data to analyze past patterns and predict future patterns. This process is used in business to discover potential opportunities and to evaluate their potential risks and benefits. The foundation of predictive analytics is to use relationships between various types of data to estimate the potential or risk of a given set of conditions.
Predictive analytics attempts to explain, analyze, and predict behavior by mathematical or scientific means. A company can capture and analyze data from its customers and, using pattern recognition, game theory, the algorithm of probabilities or statistics, can attempt to predict future customer behavior based on what that behavior is been in the past. Data mining techniques have advanced the field by allowing data to be sorted and classified in various ways. The more granular the data can be classified, the more useful and accurate it will be in predicting future outcomes.
Customer relationship management (CRM) relies on predictive analytics to understand the buying behavior of customers. By using customer data captured at the point of sale and applying various statistical techniques, companies can better understand how to market and sell new products to existing customers. They can also figure out how to best motivate people who aren’t customers yet to try their products or frequent their stores. The retail and direct marketing business segments have long used CRM techniques and are often on the cutting edge of new applications.
Predictive analytics is commonly used in industries like financial services and insurance. In financial services, companies will use credit scores to predict the likelihood of a consumer defaulting on a loan. The assessment is based on information on the customer’s credit history and loan application, compared with the same data from similar customers in the past. The insurance industry will attempt to determine the probability of a loss, based on the profile of the claimant and the past performance of customers with similar profiles.
Other industries using predictive analytics to increase their profitability include healthcare and pharmaceuticals, retail, telecommunications, and travel. The Internal Revenue Service also uses predictive analytics to try to predict and identify tax fraud. Accounting firms use this method to attempt to identify fraud in the financial statements of the companies they control.
In addition to predicting consumer behavior, predictive analytics can be used to assess aggregate demand at the store, regional or national level. It can be used to predict the performance of an entire industry under given economic conditions. The government can use it to predict factors affecting the entire economy, such as unemployment or housing starts.
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