The insurance industry is facing myriad challenges – ranging from severe weather to aging populations and customers with ever-increasing customer service expectations.
Insurers should deploy predictive analytics for marketing, product development, channel management, new market identification, customer acquisition and retention, customer service, litigation management, claims management risk management and cost control, the report notes.
The insurance industry exists in a world filled with more and more rich data, according to Barry Rabkin, principal analyst at Ovum. Insurers must take the steps necessary to leverage all this information.
“More and more data is available from existing sources (e.g., third-party providers offering information about weather events and forecasts, attributes of geographic locations, and consumer credit behavior), newer sources (e.g., social media), and those that are largely still conceptual (e.g., from machine-to-machine communications, also known as the “Internet of Things” – specifically from vehicle telematics),” he says.
To exploit this growing volume of data, insurance companies should create new departments staffed with data scientists, he suggests.
“An insurer should expect a data scientist to approach a predictive analytics initiative by first collecting data – although not necessarily all the data required to complete the initiative – and then investigating the data on an iterative basis until a coherent hypothesis emerge,” Rabkin says.
Additionally, Ovum believes that data scientists should be responsible for models that support the spectrum of corporate objectives.
But what is the first step insurance companies should take to prepare for and improve the ability to use analytics within their enterprises?
The website PropertyCasualty360.com has tackled this question by asking multiple technology analysts to identify the best first steps for insurers to take to use analytics.
Here are some of their answers:
Setting a strategy should be the first step because if this is not done well it can derail later efforts, notes John Lucker, principal at Deloitte Consulting.
“Too often insurers fail to develop their analytic strategy choosing instead to focus on tactical tasks; projects that they want to do or feel they need to do,” he says. “At times, models and solutions are built and then companies ask ‘now what?’ Analytics needs to be part of a holistic strategy of moving toward a more metrics-oriented, fact-based business process culture.”
Karen Pauli, research director, insurance, CEB Tower Group, notes that it’s critical for insurers to reorganize around blended teams to most effectively use analytics to gain actionable insights.
“Representatives from field operations (e.g., claims, underwriters, loss control, sales, etc.), business analysts, statisticians, analytics experts, modeling experts, and business strategy should all be part of blended analytics teams,” she says. “Certainly, the exact structure will depend on the product mix of a carrier. However, the point is that full value of analytics comes from multi-discipline collaboration and decisioning.”
Insurers must also develop analytics strategies to show what impact the data science team is having on the insurer’s financial performance, “both directly through analytics that measure performance against the carrier’s goals as well as the analytics that capture an indirect association to business performance like brand awareness or perception,” adds Ellen Carney, principal analyst, insurance e-business and channel strategy at Forrester Research.
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