As competition from other financial service providers and pressures from customers and regulatory agencies continues to mount, insurance companies are forced to explore ways to improve operational efficiency and cut costs without sacrificing customer service.
The volume and complexity of information collected by, or available to, most organizations has made its effective use difficult; even as managers and regulators demand faster answers to deeper questions. Business and information technology professionals have turned their focus to data mining and predictive analytics, a process that uses a variety of analysis and modeling techniques to discover patterns and relationships in existing data using the insight to make accurate predictions.
While most business users cannot participate in the process of data mining, trend analysis or design of prediction models, many business users are already using the products of predictive analytics, for example:
Customer Service Representatives: Predictive analysis in applications for marketing campaigns, sales and customer services are becoming increasingly common. The insights from predictive analytics are used to build scripts that are used by customer service representatives to deliver campaign messages, proactively identify and mitigate issues that have the potential to cause client dissatisfaction with services, or to improve customers’ sentiments about the company.