What are Predictive Analytics?
Predictive analytics refers to the analysis of big data to make predictions and determine the likelihood of future outcomes, trends or events. In business, it can be used to model various scenarios for how customers react to new product offerings or promotions and how the supply chain might be affected by extreme weather patterns or demand spikes. Predictive analytics may involve various statistical techniques, such as modeling, machine learning, and data mining.
The power of predictive analytics stems from a wide range of methods and technologies—big data, data mining, statistical modeling, machine learning, assorted mathematical processes—that can be used in conjunction with parameters to sort through massive volumes of data, both current and historical, to pinpoint patterns and predict occurrences and situations that will likely occur at a specified time. This is especially useful for helping companies find and take advantage of patterns within data, whether highlighting risks and opportunities, behavior relationships, or supply chain management.
Reliability and accuracy sets modern predictive analytics apart from past tools used to forecast sales, inventory, scheduling, occupancy, revenue, and a number of other critical business areas. Organizations in virtually any market can maximize a marketing campaign using predictive analytics to encourage customer purchases and feedback, and retain the most valuable customers through carefully targeted offers and promotions.
Use Cases, by Industry
Numerous industries are employing predictive analytics to find ways to operate more efficiently—saving money—and tap into new ways to increase revenue. Retailers can fine tune the customer experience, both online and in store. Airlines, hotels, and restaurants can build pricing around customer travel and dining habits. And predictive analytics brings a previously unknown accuracy to managing inventory and coordinating logistics. Predictive analytics is a popular crimefighting tool because it can find and stop fraud, cyberattacks, and other crimes by tracking behaviors. When unusual activity is detected an organization can take action before bad actors strike.
Healthcare providers use predictive analytics to determine which patients are most at risk, what those risks are, and the best course of care. Companies providing healthcare coverage are able, thanks to these types of analytics, to more readily identify fraudulent claims as well as track patient adherence to prescribed care.
The Difference Between Predictive and Prescriptive
Prescriptive analytics functions at a slightly higher level than predictive analytics, but is still an extension of predictive analytics. While predictive analytics is used to predict what will happen in the future, prescriptive analytics is used to recommend or prescribe specific actions when certain information states are reached, or conditions are met. It uses algorithms, mathematical techniques and/or business rules to choose among several different actions that are aligned to an objective (such as improving business performance) and that recognize various requirements or constraints.