Insurance: Dwelling Data Pun Intended!

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Using Quality Data to Enhance User Experience

Insurers who sell homeowners insurance direct-to-consumer through a website must find that delicate balance between collecting enough information to quote and bind a policy with the possibility of consumer drop-off. Prepopulating data fields using known information about the homeowner and their home is one of the methods that some sites use to ease the data entry burden on the consumer and enhance dwell time1.

Because the reconstruction cost of the home is critical to providing the right coverage amount and determining the premium, most self-service sites include at least one or more pages asking about the building characteristics of the home. The data collected is used to determine reconstruction cost and supply some of the data elements which may also be used as rating criteria. Prepopulating many of these data elements improves the user experience by allowing the homeowner to review and make changes rather than respond to every single question.

By maximizing both the number of data elements that are prefilled and the quality of the data, efficiency and ease of use can be delivered in the direct-to-consumer market in a way that reduces the likelihood of abandonment in the middle of a quote. But finding the right source for critical data can be challenging. The quality and number of data elements available from public record data can vary dramatically from jurisdiction to jurisdiction as well as by provider. Insurers need “insurance ready data” that can be created by subjecting raw data aggregated from a variety of sources to a rigorous process that results in higher data quality and greater depth of data for each unique address.

Best practices for introducing data quality into raw public record data dictate that the data for every address be evaluated using both logical and construction criteria. Offending records or data elements can then be discarded. This process eliminates the impact of errors from a range of issues in the raw data-from simple data entry errors to incorrect reporting-in order to help ensure that the data provided will never include illogical (e.g. a year built 10 years in the future) or incompatible (e.g. a basement garage specified for a home with a slab foundation) data elements.

To ensure completeness, every address record must include at least the minimum data elements that are required to generate a reconstruction cost calculation while preserving as many building characteristics as are available, all of which can be used to prefill reconstruction cost calculation fields and/or provide additional rating criteria to minimize user required input.

The integration of high-quality data into the direct-to-consumer workflow provides a more user friendly experience for the homeowner by reducing the amount of data entry required. It may also supply answers that the homeowner, particularly when getting quotes for a new home purchase, doesn’t always possess. It’s a win-win proposition providing a better user experience for the homeowner with reduced risk of drop-offs for the insurer and reliable data to expedite the quoting and binding process.

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