Insurance data strategy revolving around personalized customer experience

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Property and casualty insurace companies with self-actualized, mature analytic strategies “are creating new data types, new data features, and new analytic methods to create new value [that] is becoming a competitive advantage."

That's according to Marty Ellingsworth, senior analyst at Celent, who surveyed 31 North American property and casualty CIOs in April and May 2020. The resultant "Analytic strategy execution – Insurer practices and priorities - North America property and casualty edition" report helps undersstand how insurers are executing on their data and analytics strategies; and how they’re setting priorities, organizing scarce resources in data science, and improving their capabilities for better data, analytics, and decision support.

“Personalization and customer centricity are seen as key ways to improve both retention and the top line with targeted new business,” Ellingsworth says.

When asked about the types of analytics techniques their firm uses, about two thirds (68 percent) said the use third-party data and scores. Other common techniques include analysis of data from enterprise systems (45 percent), internally build predictive analytics (39 percent), and purchased and built analytics (39 percent).

The key challenges to the successful use of data include dealing with other competing priorities, having a culture that is not data driven, poor data governance, and having legacy data silos that are holding the firm back.

The data types that firms perceive as adding the most value to the business include pre-fill data from third-party vendors, images from cameras and smartphones, PDFs of all sorts of data, and customer segmentation.

Areas where companies are prioritizing value from analytics are underwriting (cited by 94 percent), pricing (81 percent), claims (81 percent), fraud (69 percent), and customer experience (66 percent). Larger companies are more focused on customer insight and experience now, while smaller firms are working harder on pricing, the report notes.

Creating an analytics strategy is a recognized short-coming for many companies, and it will be important for accelerating future investments, the report says.

“The two key contributions of an analytics strategy are the roadmap of where you want to go, where you are now, and what will you do next; and using a ‘data and analytics GPS’ to update what changes in data, analytics, solutions are occurring which would let you leapfrog current barriers or roadblocks or accelerate things faster,” Ellingsworth says.

“This is extremely important today, when so many breakthroughs in accessing, processing, and analytics of old and new data seem to be ‘going live’ almost every quarter now,” Ellingsworth says.

Firms should not fear change—particularly during the pandemic—and should not hesitate to move ahead with analytics initiatives, Ellingsworth adds. “Don’t wait; it’s really not about carrier manual processes being good enough anymore,” he says. “Distanced, touchless, and from home are the current normal for all stakeholders, and data of all sorts are being fused into immediate payment and self-service experiences.”

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Analytics Data science Data strategy Geospatial data Customer experience