Insurance organizations, like many other businesses, are constantly looking for new ways to reduce bottom-line expenses. However, given the current scrutiny around rate increases, it is more important than ever for insurers to cut internal costs in order to help maintain current growth rates. But how does a company reduce costs without affecting customer service?
By ensuring the accuracy of contact data quality, insurers can improve efficiency, which will in turn reduce bottom-line expenses. In fact, a recent Experian QAS survey revealed that the main reason insurers maintain quality contact records is to reduce costs. Reducing costs through accurate contact data allows insurers to continue providing a high level of customer service while maintaining the lean operating costs required by investors.
However, cleaning up contact data can seem daunting. Every database contains errors, but types of errors vary by business. In order to improve efficiency and ensure accurate data, insurers need to understand what data quality errors exist in their business and how they impact operational processes. Then insurance organizations need to find ways to improve data and finally, track the benefits of accurate contact data to quantify the return on investment.
Top Data Quality Errors
The first step in ensuring accurate contact data is to figure out what problems already exist in a given company's database. Database analysts can look through contact information to spot common errors.
While data quality errors vary by organization, there are some trends that exist across the industry that could provide analysts with a good starting point. In an August 2010 survey of 100 insurance organizations conducted by Experian QAS, the top data quality errors reported were missing or incomplete data, outdated information, spelling mistakes, and incorrect data.
These errors can affect a myriad of business processes. Again, while these issues can vary, there are common processes affected by poor contact data quality:
Once analysts figure out which errors are occurring and how they are impacting businesses process, they need to look at how these errors are entering the database. According to the Experian QAS research, customer service and sales were cited as the departments that cause the most data errors. Analysts can identify how data flows throughout the organization and see where common errors are appearing. This data will be invaluable later on when selecting a solution.
Ways to Improve Contact Data
Once insurers can identify how data quality errors are entering their database, processes can be put into place to correct information. The most common practices for insurance organizations to clean contact data include staff training, software tools and staff measurement.
While all of these processes can help improve contact data, they are typically too broad and not thorough enough to clean the most prevalent errors. The processes are typically manual and allow a multitude of errors to enter a database.
Best practices for collecting accurate contact data include putting verification tools in place at all points of capture so that contact data is standardized and complete before it enters the database. These tools can be interactive so that they can prompt for questions or missing information.
The database knowledge discussed above can help insurers implement best practice solutions where they are needed most. If a large amount of poor data is coming through an agent portal, then staff can first roll out verification tools to the portal, and then maybe roll out software to other departments such as Customer Service later on.
The research ultimately allows an insurance organization to tailor solutions to its own individual needs, rather than depending on an enterprise-wide solution that is too expensive or a manual process that does not adequately clean data.
Observing the Benefits
As with many businesses trying to reduce operational expenses, insurers need to prove a return on investment for each project. After an insurance organization rolls out a contact data quality solution, the benefits of that system need to be tracked.
The first benefit commonly seen is improved efficiency. Insurers should review how much time staff members spend correcting contact information and re-sending returned mail pieces. Chances are, insurers will find an improvement and staff members will be able to communicate accurately with customers the first time.
This improved communication will lead to better customer service. Sending communications on time builds trust between the policyholder and insurer. This assists the insurer in retaining that policyholder over time.
By comparing the data that existed prior to implementing a solution to the data that is present post-implementation, insurers can prove a return on investment and show that the solution they chose benefited the business in measurable ways.
Improving the accuracy of contact data allows insurers to improve operational efficiency and reduce costs. But more importantly, accurate contact data can advance overall communication with customers and allow insurers to see a greater return on investment from each customer.