The Importance of Contact Data

Extensive Data Inaccuracies

Despite the strong investment in data quality that was previously mentioned, insurers still find that their databases are riddled with errors. Of those surveyed, 60 percent stated that at least six percent or more of their database contains inaccurate or missing contact data. The top data errors reported were incomplete or missing data, followed by outdated information, spelling mistakes, and incorrect data.

These types of data errors can greatly affect claim departments. If data is incomplete, inaccurate or outdated, communications may not reach the claimant. Unfortunately, these types of errors often go unnoticed until mail is returned a few weeks after it was issued. This can slow down the investigation and anger policyholders.

In addition, claim departments face many compliance regulations. While these vary from state to state, many deal with timely, written communication. Without accurate contact information, insurance organizations are at risk for non-compliance.

Current Cleansing Strategies

Respondents stated that they currently manage quality contact data through staff training, software tools, and staff measurement. Insurance organizations are also measuring the accuracy of their contact data. Forty-seven percent of insurers are using manual processes and 51 percent are using analysis from marketing campaign response rates. It is interesting to point out that, in general, less than half of the respondents use a front-end verification tool, which explains the high level of missing information.

According to those surveyed, the information technology department is most commonly responsible for the cleansing of contact data. While it is important for IT to be involved in rolling out data quality solutions, end user departments need to help in the effort. They are the ones most affected by poor data management.

Best Practices

With so much bad data contained in insurance databases, developing a cleansing strategy can be overwhelming. But with contact data playing such an important role in so many processes, it is important to get started. Here are a few steps that claim departments can take to properly clean their contact data:

1. Understand the database. In order to implement a data quality strategy, insurance organizations and claim departments need to first understand common errors within their database. Review contact data to determine common errors, as each insurer has its own set of challenges. While the survey revealed that incomplete or missing information is common, claim departments may see records that are consistently missing area codes or secondary address information.

2. Clean existing data. Once common data errors are identified, those errors need to be corrected within existing records. Third party resources or manual internal resources can be leveraged depending on the size of the database. It is important to clean the data so that it does not continue to waste resources.

3. Verify data during all capture processes. Once existing contact data is clean, put processes in place to ensure that all new contact data is accurate before it enters a claim process. By knowing their data, insurance organizations can determine the best place to implement point-of-capture verification tools. The survey found that customer service is a common area for data entry errors. If departments are restricted by budget, as found in the survey, they can implement tools by department based on who will achieve the strongest return on investment.

4. Enhance and update data. Enhance and update data on a frequent basis. Fifty-eight percent of those surveyed stated that outdated information was a common data error. By refreshing data, insurers can make sure that policyholders still live at their listed addresses. This means that they can continue to send relevant forms and information to claimants, while complying with government regulations.

Moving Forward

Even though insurance organizations have been scaling back, the investment in data quality continues. This strong push in data quality initiatives shows that insurance organizations view their central database as a valuable asset that helps to drive the business forward.

Customer retention continues to be a theme for insurers and 68 percent of those surveyed evaluate the lifecycle or lifetime value of each customer. Of those respondents tracking customer value, 46 percent see it steadily increasing, while 25 percent see it rapidly increasing. Policyholders rely on insurance organizations when they file a claim, and that process can often make or break the business relationship based on how quickly and easily the claim is addressed. Therefore, it is important to ensure the accuracy of the contact information used in these processes.

Data quality initiatives can be broad master data management strategies, or, depending on the budget, can be prioritized based on the projects that will produce the greatest return on investment. Ultimately, ensuring accurate contact data will improve claim processes and provide dividends for years to come.