Predictive analytics uses historical data to analyze past patterns and predict future patterns, uncovering potential opportunities and assessing risks and rewards. It is used in various industries, including financial services, insurance, healthcare, and retail, to predict consumer behavior and assess aggregate demand. CRM relies on predictive analytics to understand customer buying behavior.
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 uncover potential opportunities and assess their potential risks and rewards. The foundation of predictive analytics is using the 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 its customers’ data and, using pattern recognition, game theory, probability algorithm, or statistics, attempt to predict future customer behavior based on what that behavior has been like in the past. Data mining techniques have advanced in the field, allowing data to be sorted and categorized in a variety of ways. The higher the level of granularity at which data can be categorized, the more useful and accurate it will be in predicting future outcomes.
CRM (Customer Relationship Management) relies on predictive analytics to understand customer buying behavior. By using customer data captured at the point 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 scores 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 loan application, compared to the same data from similar customers in the past. The insurance industry will attempt to determine the likelihood of loss based on the applicant’s profile and the past performance of clients with similar profiles.
Other industries that use predictive analytics to increase their profitability include healthcare and pharmaceuticals, retail, telecommunications, and travel. Even the Internal Revenue Service employs predictive analytics to try to predict and identify tax fraud. Accounting firms use this method to try 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, regional, or national level. It can be used to predict the performance of an entire industry under certain economic conditions. The government can use it to predict factors that affect the entire economy, such as unemployment or housing starts.
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