Predictive analytics is transforming all kinds of industries. It can catch fraud before it happens, turn a small-fry enterprise into a titan, and even save lives. There is no limit to the number of situations in which predictive analytics can help.
Read on to explore five end-to-end examples of how predictive analytics works for five very different industries.
Data growth affects every industry today. As healthcare data explodes in volume, the popularity of machine learning and predictive analytics grows. By embedding predictive analytics in their applications, healthcare practitioners can improve patient outcomes, improve healthcare operations, and detect fraud.
In a healthcare setting, the data analyzed may include patient demographics, patient vitals, past medication history, visits to the hospital, lab test results, and claims.
A common example of predictive analytics in healthcare involves predicting which patients are at high risk for a specific condition (such as diabetes). Practitioners can then prioritize high-risk patients for screenings first. In addition to helping patients, this allows practitioners to be more efficient with their time and resources.
For manufacturers, machine downtime can cost millions of dollars a year in lost profits, repair costs, and lost production time for employees. By embedding predictive analytics in their applications, manufacturing managers can monitor the condition and performance of equipment and predict failures before they happen.
Data may include maintenance data logs maintained by the technicians, especially for older machines. For newer machines, data coming in from the different sensors of the machine—including temperature, running time, power level durations, and error messages—is very useful.
A typical example of predictive analytics in manufacturing involves determining the likelihood of breakdowns. Manufacturers can then plan ahead to shut machines down for preventive maintenance. They can also use predictive analytics to limit or prevent any impact on the production pipeline.
Any finance professional knows how much of a disruption missed payments can be. Financial groups with outstanding invoices need to know who will—and who will not—pay their bills on time. The predictive analytics solution can analyze company or individual demographics, products they purchased/used, past payment history, customer support logs, and any recent adverse events.
By predicting which individuals or businesses will likely miss their next payment, financial groups can better manage cashflow as well as take steps to mitigate the problem by sending reminders to potential late payers.
Many creative tactics can be used to commit insurance fraud, including staged incidents, withholding or falsifying information, and making fraudulent transactions. Insurance companies can use predictive analytics technology to track and monitor potential scammers, without spending time sorting through every claim.
The predictive analytics algorithm can consider the location where the claim originated, time of day, claimant history, claim amount, and even public data.
By applying the model to new claims, insurance companies can quickly detect suspicious activity. Any claim that appears abnormal is marked as an outlier. Claims that are likely to be fraudulent will be put on hold and sent back to investigators for further review. Potential alerts can also be cross-referenced with information in public registers to reduce the likelihood of false leads accompanying legitimate ones.
#5. Software as a Service (SaaS)
Customer churn has always been a difficult metric to understand for SaaS companies. Most churn applications only tell you how many customers churned last month and how much money was lost. With predictive analytics, product managers can forecast and mitigate churn with much more precision than typical analytics tools—which can lead to significant revenue.
The predictive analytics algorithm should consider customer demographics, products purchased, product usage, customer calls, time since last contact, past transaction history, industry, company size, and revenue.
Actions may include an automated email showing the customer how they can get more value from the application, or a trigger to the customer success team to proactively get in touch to understand what can be done to help the customer. It’s not only important to identify who will churn, but also who will not churn. Predicting which customers will not churn means you can find different ways to engage them with new products or strategic partnerships.