Many industries are embracing predictive analytics techniques to increase efficiency and change the way businesses interact with their customers. The vast amounts of data that organizations have access to is utilized along with insight into customer preferences and buying habits, allowing industry leaders to predict trends and provide more exact services tailored to consumers. The following trends in predictive analytics are changing the future in several industries.
Insurance companies have access to large amounts of raw data that encompass customer driven habits, patient health records, instances of burglary and theft, and fraudulent claims. Analyzing this data shows patterns and trends in risk assessment and personalized pricing. The insurance company Progressive offers a tool called Snapshot®, which is a device plugged into a vehicle that monitors data such as changes in speed, high-risk driving times, and total miles driven. This tool allows companies like Progressive to personalize pricing of car insurance based on the driving habits of the consumer.
Using predictive analytics and information from homeowners’ insurance claims, insurance carriers can also help their clients lower the risks of home invasions. Patterns based on burglary statistics, such as the time of day homes are most often broken into, allow companies to warn their customers of when to keep their doors locked and be vigilant.
Analytics is also providing insurance carriers the opportunity to detect behavior that leads to fraudulent claims. Predicting these false claims through analytics has the potential to save companies both time and money, and in turn eventually help lower premiums for customers.
Traffic and Driver Safety
Predictive analytics has shown potential for enhancing safety in the automotive industry. Reducing the number of traffic-related fatalities is a difficult task requiring a lot of time, personnel, and funding. While developing safety technology and enforcement programs has worked to increase safety on the road in the past, new methods involving sensors built into roads and vehicles that collect data are providing a foundation for analyzing the factors that lead to traffic accidents and fatalities. The Tennessee Highway Patrol (THP) has been working with IBM to analyze data about DUI arrests, past accidents, and weather reports to predict where accidents will most likely occur. By setting up patrols and speed reducing tactics in these areas, casualty rates have dropped by 6 percent and DUI arrests have increased by 43 percent.
The health care industry continues to adopt methods made available by advances in analytics technology to reduce costs and improve the quality of care. There is an abundance of health care data now available and by 2020 that amount of data is predicted to increase fifty fold. In order to take advantage of this wealth of data and employ predictive analytic techniques, data warehouses are necessary. Also needed are standardization and cleansing methods aimed at eliminating redundant, missing, and unformatted data.
The Veteran’s Health Administration (VHA) collected over thirty years of electronic patient data. After constructing a data warehouse and developing algorithms that are able to predict certain health and death risks, they used this data to increase efficiency while improving the quality of care. This led to the VHA receiving net benefits of $3 billion as a result of predictive analytics.
Another possible benefit of predictive analytics includes leveraging the available data from electronic health records (EHRs) to predict patient outcomes and compliance issues. Physicians use predictive analytics powered by bedside medical data and algorithms to monitor patients in the ICU and detect signs of infection, sepsis, and other crises, hours before humans are able to.
Efficiency is a central focus in the manufacturing industry. Predictive analytics is being employed in manufacturing through the use of machines fitted with data sensors and connected devices. This allows for data scientists to collect and monitor data from these machines and pinpoint when and where problems arise and maintenance is needed. By predicting these maintenance issues before they occur, manufacturers can save time and money.
Predictive analytics also has the potential to increase the quality of products produced, forecasting the demand and future sales of products, and scheduling machine usage for maximum utilization.
As the amount of available data continues to increase and new ways to organize and understand this data are discovered, predictive analytics will have a bigger role in increasing both the efficiency and quality of the products and services of a variety of industries.
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