In business, 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 assess their prospective risks and rewards. The basis of predictive analytics is to use the relationships between various types of data to estimate the potential or the risk of a given set of conditions.
Predictive analytics attempts to explain, analyze, and predict behavior by mathematical or scientific means. A company may capture and analyze its customer data, and, using pattern recognition, game theory, odds algorithm, or statistics, attempt to predict future customer behavior based on what that behavior has been in the past. Data mining techniques have advanced the field by enabling the data to be sorted and categorized in various ways. The greater the level of granularity to which the data can be categorized, the more useful and accurate it will be in predicting future outcomes.
Customer relationship management (CRM) relies on predictive analytics to understand the purchasing behavior of customers. By using customer data captured at the time of sale, and applying the various statistical techniques, companies can better understand how to market and sell new products to existing customers. They can also understand how best to motivate people who are not yet customers to try their products or frequent their stores. The retail and direct marketing business segments have long used CRM techniques, and are often at the forefront of new applications.
Predictive analytics is commonly used in industries such as financial services and insurance. In financial services, companies will use credit scoring to predict the likelihood that a consumer will default on a loan. The assessment is based on information about the customer’s credit history and the loan application, compared with the same data from similar customers in the past. The insurance industry will attempt to determine the likelihood of a loss, based on the profile of the applicant and the past performance of customers with similar profiles.
Other industries that use predictive analytics to increase their profitability include health care and pharmaceuticals, retail, telecommunications, and travel. Even the Internal Revenue Service employs predictive analytics to try to predict and identify income tax fraud. Accounting firms use this method to attempt to identify fraud in the financial statements of the companies they audit.
In addition to predicting consumer behavior, predictive analytics can be used to assess aggregate demand at the store, region, or national level. It can be used to predict the performance of an entire industry under certain economic conditions. The government may use it to predict factors that impact the entire economy, such as unemployment or housing starts.
