Insurance is an industry of information and the manipulation of data. Contact data in particular is present in every database. This information critical to insurance operations as it affects risk assessment, policyholder communications and compliance.
But the difficulty of maintaining data is almost as prevalent as insurers having contact information in the first place. Because of the fluidity of information, insurers have a hard time keeping contact data clean and up to date.
Most insurance organizations work to keep data clean, but there are many that view inaccurate data as a standard part of doing business. While that is true to a certain extent, data can be maintained and cleaned to enable more efficient and cost effective business practices if organizations put certain processes in place.
Download this white paper to learn common data quality errors, where those errors originate and different cleaning strategies that allow insurers to overcome the ever-present dirty data.
By focusing on address data quality, organizations control escalating direct marketing costs, enable a single view of their customers for analysis and trending, ensure effective account setup and communication, and improve overall customer experience. Unfortunately, the traditional means of mitigating poor address data is solely through scheduled, or at minimum one-off, batch cleansing efforts. While after-the-fact batch cleansing is extremely valuable for scrubbing large volumes of legacy or acquired address data, it is a downstream process with no ability to request additional information. The only way to guarantee that an address will be accurate and deliverable is to verify it during the data capture process � while the customer is still engaged online, on the phone or in person.
This Forrester Research white paper discusses the importance of verifying customer address data as it�s captured to improve business process efficiency and reduce costs while gaining more insight into your customer data. Additionally comparisons are made between traditional batch cleansing and recommended front-end verification.