The insurance industry may be a “data haven,” but turning the data into actionable insights is another story. If anything, many insurance decision makers are stymied by a form of “analysis paralysis” that keeps them from making the most of the data streaming in.
That’s the view of PegaSystems’ Eric Deitert, who has been exploring the potential of predictive analytics within the industry. In a blog post, he observes that for insurers, “the issue is not a lack of data. The issue is the data is constantly changing, and data paralysis halts analytic initiatives due to the massive data sets.”
The current emphasis on predictive models is one-sided, Deitert cautions. To track changes in data, he urges data managers and decision makers to bring “adaptive analytics” into the equation. In essence, the combination of predictive and adaptive analytics function “like a state-of-the-art GPS system” — helping to pinpoint where things will be based on changing data, as opposed to trying to predict tomorrow based on yesterday’s information. By way of analogy, a car’s GPS will adapt its routing suggestions based on real-time information about traffic jams and construction projects.
Deitert maintains that a good place to start with the combined GPS approach is with customer service. This lets the insurer combine real-time observations with predictive analytics to formulate a “best response” to customers.
For example, such a system could combine predictions about additional products the policyholder is likely to buy “with real-time information to adapt a typical service call into a highly relevant cross-sell or up-sell offer — based on the customer’s buying propensity or information gathered as the conversation unfolds.”
The combined approach also is helpful in mitigating risks associated with expected losses, Deitert says.
Joe McKendrick is an author, consultant, blogger and frequent INN contributor specializing in information technology.
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