Many insurers are only beginning their data journey, which is to say they almost certainly possess substantial troves of data, but not yet a strategy to guide its directed use. Across every organization, it’s helpful to assess the degree to which a data strategy has been established, shared, and understood by all stakeholders. Whether at the initial implementation or advanced scaling stages, there’s always more work that can be done to better leverage internal and external data.
Data maturity enables analytics maturity
Most often, data is taken into account during periods of post-performance review and reflection. But this kind of descriptive analytics can only answer the question of what happened over a given time frame. Companies further along in their data journey consider diagnostic analytics to understand why their data is what it is, and even more advanced teams lean on predictive analytics—what will happen—and prescriptive analytics—how can something specific be made to happen—to drive future success.
How looking at past data can prepare agencies for the future
In the independent insurance industry, many agencies have trouble identifying just how much work their employees will be able to handle, especially during high work-volume periods. But historical trend analysis can help agencies know when to hire and prepare for future growth.
Being able to visualize historical trends makes it much easier to answer important questions. It also helps to keep your agency operating at peak efficiency and avoid bottlenecks, because you’re able to see what’s taken place in the past and predict the likelihood of workloads in the future.
Solutions that can help make use of data include an AMS with a dashboard to see trends and track KPIs quickly and a CRM to track sales data and automate many parts of the sales and renewal process. By taking advantage of InsurTech solutions with data visualization tools, you can use historical data to make future-looking decisions.
Insights for more effective data strategies in personal and commercial lines
Beyond workload, your approach to data and the opportunities that come as a result can differ depending on lines of business. While the principles of being data-driven are largely the same, there are key distinctions between personal and commercial lines in terms of focus areas:
- Risk Complexity
While complexity exists in personal lines, the diversity of commercial risks brings additional challenges to capturing the necessary data points for assessing a particular customer. - Channel Nuances
Small commercial customer shopping has made headway in the direct-to-consumer space, but nowhere near the extent that a massive tranche of the personal lines market has. - Price Communication
Comparative rating debuted in the personal lines marketplace more than a decade ago and is a concept that continues to permeate consumer expectations. In recent years, commercial insurance, too, has begun a shift toward comparative rating, a trend that is only likely to increase. With simpler exchanges for data and risk appetite, carriers and their agency partners can better target more favorable business. - Third-Party Data Maturity
From driver and household information to contributory databases, personal insurance is the noteworthy leader of third-party data maturity, but advancements in commercial data are continuing to change that landscape. - Demands for “Frictionless”
Regardless of market, end-insured customers and agency partners alike expect quick and intuitive ways of doing business. To continuously improve workflows and experiences across the value chain, carriers must collect, analyze, and make improvement decisions with thorough, accurate information.
To learn more about these and other data topics, watch this webinar below and explore Vertafore’s proven analytics solutions for data-driven insurers.
