SAS provides us with a good definition of “predictive analytics”, so that we don’t have to reinvent the wheel. According to the expert, predictive analytics “is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future.”
To no surprise, many analytics experts believe that the time for the technology has finally come. “With interactive and easy-to-use software becoming more prevalent, predictive analytics is no longer just the domain of mathematicians and statisticians. Organizations worldwide are turning to predictive analytics to increase their bottom line and competitive advantage”, SAS affirms.
Some of the most common uses SAS identified include:
- Improving operations. Many companies use predictive models to forecast inventory and manage resources: For instance, airlines use predictive analytics to set ticket prices or hotels try to predict the number of guests for any given night to maximize occupancy and increase revenue. In a nutshell, predictive analytics enables organizations to function more efficiently.
- Optimizing marketing campaigns. Predictive analytics are used to determine customer responses or purchases, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow their most profitable customers.
- Detecting fraud. Combining multiple analytics methods can improve pattern detection and prevent criminal behavior. As cybersecurity becomes a growing concern, high-performance behavioral analytics examines all actions on a network in real time to spot abnormalities that may indicate fraud, zero-day vulnerabilities and advanced persistent threats.
- Reducing risk. Credit scores are used to assess a buyer’s likelihood of default for purchases and are a well-known example of predictive analytics. A credit score is a number generated by a predictive model that incorporates all data relevant to a person’s creditworthiness. Other risk-related uses include insurance claims and collections.
Finally, “businesses across a wide range of industries are beginning to realize what large financial institutions have known for years: Predictive analytics has the power to significantly improve the bottom line. From better targeting and risk assessment to streamlining operations and optimizing business decisions, predictive analytics can help any organization gain and maintain a competitive edge”, SAS concludes.