Like many other businesses, insurance providers wage a constant battle to keep costs down while also investing in their company's future. This balance is especially difficult when it comes to data quality technology upgrades.

Data quality has a major impact on insurers, especially within the claim department. Contact data not only enables claims departments to communicate with key stakeholders, but also impacts investigations and eventually payment.

With such clear ties to business processes, it is not surprising that improving contact data quality is a priority for many claim departments. However, these projects are not without their challenges.

As with every initiative, stakeholders must justify costs to the business. However, a data quality initiative can be difficult to quantify without proper planning. In order to ensure this level of accountability, executives need to select relevant stakeholders, determine the total cost of the current contact data quality problem, select a solution, and then prove the return on investment (ROI).

Selecting Key Stakeholders

Great advancements in the quality of the organization's contact data can be made by improving processes in the claim area alone, but it is important to remember that much of the poor contact data originates in other departments. This data can filter through from agents, underwriting, or customer service -- in the form of fat fingering, inputting data in the wrong fields, or just leaving information out all together. Therefore, while claim departments need to make improvements to their own processes, the insurer as a whole needs to put measures in place to ensure data accuracy.

Whether the project is just in the claim department or throughout the insurance organization, members of claims will be key stakeholders in any data quality initiative as their interaction with policy holders often defines the longevity of the customer relationship. Other key stakeholders that should be considered are representatives from other policyholder-facing departments who collect contact data, along with technical staff that help implement any solutions. Once stakeholders have been properly identified, the insurer must take steps to appropriately quantify the contact data quality problem in relevant departments.

Quantifying Contact Data Quality

One of the most important steps in a contact data quality project is calculating the total cost of bad contact data for a given department or entire organization. Unfortunately, this step is often overlooked. However, it helps to identify key problem areas to better determine system requirements. This will also help the insurer establish the extent of the current business problem.

According to Data Quality Expert David Loshin of Knowledge Integrity, researching the root cause of an issue may lead to potential solutions. "Evaluating how data errors reduce the effectiveness of any business activity provides the 'cost' aspect of justifying data quality improvement," Loshin says.

So what are some key business activities to consider when assessing the cost of inaccurate contact data and how it relates to claims?

Customer service is a top priority for any insurance provider. A trusting connection needs to be built between the customer and the insurance organization to ensure a long-term, profitable relationship. Granted, this can be difficult to achieve with inaccurate customer contact information. Communications can be delayed, or mail might never reach the intended recipient. Such situations can cause dissatisfaction with the provider, or even policy cancelation. This is especially true as it pertains to claims. Timely communications are a necessity in ensuring accurate investigations and payment. Errors in the claim process can be extremely detrimental to the long-term customer/insurer relationship. To determine the cost, insurers should consider the number of customer service calls taken due to missing communications.

Claim processing can be affected by many different variables. Inaccurate contact data can negatively impact processes by unnecessarily increasing the time needed to execute a claim. Given the large quantity of forms that need to be mailed throughout the claim process, one incorrect address can needlessly throw off the entire process. To gauge the impact this is having, insurers should review returned mail and claims that need to be reprocessed because of inaccurate contact data.

Rate quoting depends on the accuracy of address data to determine risk for a given policy. Obviously, someone whose home is located in a flood plain or inner city neighborhood will have a higher premium than someone who lives in a middle class suburb. If address errors occur upon entry, a potential policy may be created with an inaccurate rate assessment. While this data is not captured by claims, the department is affected. When a claim is filed, the policyholder could cause a lot of unnecessary work for the claim department if the policy does not adequately cover the damage.

Operational costs are greatly influenced by inaccurate data. Bad data ultimately wastes staff time, raises postage costs, and damages policyholder relationships. All of these items quickly add up, but they can be easily avoided by verifying contact data at the point of entry, prior to entering business processes. For cost estimation, insurers can review return mail and interview staff members to see how much time they spend correcting contact data.

Compliance violations are more likely when inaccurate information exists in the database. While laws regulating insurance organizations vary by state, most legislation requires that insurers contact customers regarding claims or policy changes in writing within specific time constraints. Inaccurate contact data can expose an insurer to unnecessary compliance risks and fines.

Selecting a Solution

The analysis above sets the insurer or department up for success. These statistics better enable insurers to select the solution that is right for them. "This analysis allows the business sponsor to prioritize improvements based on different variables, such as cost to implement, overall positive impact, time to value, or organizational trust," Loshin says.

The cost of the total contact data quality problem should be considered before choosing a solution. Without this consideration, it is nearly impossible to ensure a positive ROI or a solution that fits the insurers' needs.

"Very simply, if the cost to fix the problem is less than the cost associated with the collected impacts, it makes sense to fix that problem," Loshin explains.

There are various contact data quality solutions available, but one common best practice method is ensuring that data is verified, standardized and is accurate before it enters a central CRM. This allows staff members to verify data while the potential policyholder or the claimant is still engaged, or assists in deciphering handwritten forms. By ensuring that contact data is accurate before it enters business processes, problems in the departmental activities mentioned above can be avoided.

Best practice methods are available to anyone, regardless of the budget. One way to control the cost of a product is to perform the rollout in stages. Insurers can use initial research to pinpoint the departments that are most heavily affected by poor contact data quality. By launching a solution in key problem areas, technical departments can lower the cost of initial implementation and potentially prove a faster ROI.

Proving the ROI

It is much easier to prove a positive return if the total data quality cost is calculated prior to implementation.

Loshin suggests that insurers "develop a reasonable plan for designing and implementing a solution, along with related performance metrics that will demonstrate the value improvement." This allows insurers to more accurately compare results.

If the steps above are followed and the total cost of the current data quality problem is assessed, then proving a subsequent ROI should be relatively easy. The same steps used to evaluate the current contact data quality problem should be implemented for a second time. After its completion, the insurer should compare the results with those from the first round of testing.

This calculation will indicate if the contact data quality initiative was beneficial to the claim department and/or the insurer. By calculating a measurable improvement, insurers can better determine the total ROI, as well as attribute those cost savings directly to the contact data quality improvement project.

Tying it all Together

It is easy to prove the success of a project if stakeholders take the time to determine the true cost of poor contact data quality prior to shopping for a solution. Loshin comments that "understanding how data requirements relate to business drivers can help narrow the scope of opportunities for data quality improvement to those providing the greatest 'bang for the buck'." Deploying solutions (even when confined to the claim department) that have been well-researched and properly analyzed can ultimately have a significant impact on the bottom line.

Joel Curry is COO of Experian QAS, a provider of address verification software and services. He may be reached at joel.curry@qas.com; www.qas.com.

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