There has been a lot of talk about the use of predictive analytics in high-risk industries like insurance, energy and banking. But predictive analytics touches a lot more industries than those three. Those in retail, for example, may use SPSS predictive analytics to determine future changes in selling patterns and quickly translate that into a series of coordinated decisions that go right up the supply chain. SPSS predictive analytics has also been leveraged in healthcare to help hospitals better treat diseases. Using SPSS, hospitals can analyze insights from clinical data, combine it with patient information and create personalized treatment plans for its patients. Predictive analytics fits perfectly into those industry models to predict likely outcomes, make smarter decisions and get better results; nearly every industry can benefit from the use of predictive analytics – here’s why.
More data than ever before is now available to help fuel decision making. Organizations gather data about customer preferences and purchasing habits, inventory levels, product and equipment problems, submitted claims, and much more. Read on to discover 5 ways SPSS predictive analytics can be leveraged across any business line.
Businesses want to operate: Effectively. Efficiently. Profitably.
New risks and mounting threats can get in the way of operational objectives. SPSS offers a proactive, systematic approach to closing gaps and classifying risk to operate at peak performance. By using predictive analytics solutions, businesses identify vulnerabilities and draw the line between acceptable and unacceptable risk.
Using the information that business already has, SPSS analyzes the possibility and probability of events and thwarts threats and risk. The software uses historical and real-time data to identify key areas of risk, accommodate regulations, and refine and monitor policy. SPSS also works to detect subtle patterns and associations in data and build powerful predictive models to identify anomalies. The software can also monitor multiple data sources, detect suspicious behavior and prevent unacceptable activity by taking action.
2. Save money
Using predictive analytics can add up to big savings.
For example, by using SPSS predictive analytics IBM customers in the banking and insurance industries:
IBM customer Grupo Bancolombia, one of Columbia’s biggest banks, used IBM SPSS software to detect suspicious transactions. Grupo Bancolombia generated productivity savings of 80 percent through its more targeted analyses of transactions.
Think about it, when businesses shorten up the time it takes to investigate fraud or risk, predict problems before they even occur, or just simply tailor business activities to customer trends—customers are going to directly benefit. Take for example, an IBM customer: an independent regional health insurer. This company reduced fraud investigations from weeks to days, increasing the amount of customers they could help in shorter periods of time.
But again, predictive analytics is not just about fraud. With SPSS Predictive Analytics, marketing can segment and tailor offerings to match customer needs; call center agents can up-sell and cross-sell products customers are looking for; HR professionals can target and recruit more-qualified candidates. All increasing how the company effectively works with its customers.
IBM customer Elie Tahari, a global fashion brand, uses IBM SPSS software to predict customer orders four months in advance with over 97 percent annual accuracy. This process enabled the retailer to optimize production and guarantee full availability of its products for customers. The brand was also able to view real-time sales, inventory and logistics information partnering SPSS with a data warehouse—reducing the reporting cycle from two days to a few minutes.
Accurately and cost-effectively predicting the operating characteristics that can lead to a greater frequency of failure or cause more downtime is one of the biggest benefits of using SPSS software. Downtime can have a direct and significant impact on the return on investment over the life of a system. Using SPSS software to predict costly problems before they occur optimizes production line uptime and decreases disruptive, costly, unscheduled downtime.
The median return on investment (ROI) for predictive analytics projects comes in at a whopping 250 percent. A report from IDC points out that “[a] growing body of market research shows that predictive analytics can impact the profitability and competitiveness of an organization.”
Predictive analytics can help you turn vast amounts of structured and unstructured data into actionable insights, allowing you to predict what events are likely to happen next and act accordingly.
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