A poor claims experience is the single most significant reason why a customer would switch to another insurer. So, why have insurers taken so long to improve their claims processes?

By: Gordana Radmilovic

Imagine a situation where a customer, let’s call her Mary, was just involved in a car accident and is very emotional. She reaches out to her insurance company via phone, but is placed on hold for over 15 minutes, nervously waiting to speak with a customer service representative about her claim. Once Mary finally hears a human voice, she provides all her information and is informed that an adjuster will be reaching out to her shortly. When that adjuster contacts her to discuss the claim, they request all of the same information Mary already shared with the customer service representative at the contact center. Additionally, the adjuster has no clue how to handle the complex claim Mary is dealing with and is insensitive to her situation.  Can you imagine how frustrated Mary must be?

If you were in Mary’s shoes, would you stay with this insurer who appears to lack simple customer service capabilities and the knowledge to handle your claim?

A poor claims experience is the single most significant reason why a customer would switch to another insurer. So, why have insurers taken so long to improve their claims processes?

There are likely a variety of reasons, but one of them is certainly related to the lack of claims data clarity which stems from disparate claims systems with poor data quality. As if that initial poor claims experience wasn’t enough, can you imagine sending a customer satisfaction survey for that dismal experience a year later because  poor data triggered the system to push the survey out to  customers 12 months after the experience?

It is a known fact that insurers have large amounts of data, but with poor data quality, they are unable to effectively take advantage of this abundant resource in order to improve the customer experience. Insurers need to be able to better understand the data which is at arm’s reach; and, as claims are the main interaction point customers have with insurers, better understanding claims data is the key to better understanding your customers.

Three core improvements can allow insurers to more effectively utilize claims data:

Gather the right data.
Although insurers have significant amounts of data, many times there is a lack of organization to the data being collected. Insurers need to invest in establishing a clear data strategy which aligns to the future vision and critical business processes of an organization. By aligning specific business drivers to critical strategic initiatives which impact core business processes, the organization can better understand the data elements required to support those business processes and associated changes. When gathering data, to ensure it is the right data, it should be traceable to the strategy set forth by the organization.

Additionally, to ensure the appropriate data is being gathered, metrics should be put into place to ensure the data is actionable. For instance, implementing metrics to track how customers prefer to interact with you – whether it is via a contact center, online channels, or through an agency – and which specific products they purchase from each channel can help an insurer better understand opportunities for product cross-sell. By understanding how to better interact with customers based on their channel preferences and their customer segment, insurers can make the claims experience worthwhile – i.e. offer a discount for bundling the current policies with a product the customer needs.  As an insurer better understands their customer, they can make their contact center strategy more impactful.

Improve data quality
With abundant amounts of disparate claims data – some electronic-based, some paper-based – insurers tend to lack quality data. This is caused by data duplication across the insurer’s various systems and errors associated with manual entry. In fact, many of the documents associated with claims, including letters from attorneys and investigative detail, are still being shared via paper.

In order to manage information effectively and improve data quality, master data management and data quality initiatives are essential to an organization. To reduce the amount of data duplication, master data management strategies should be implemented. Master data management involves identifying the critical data elements leveraged throughout the entire organization and hosting them in one single source of the truth in order to be able to leverage these data elements effectively across the entire organization. To improve data quality and ultimately reduce the impact of manual entry errors, organizations should seek to implement automated data quality rules which force system generated exception reports. These exceptions reports will allow manual intervention to take place before the flawed data is dispersed throughout the organization. Once a data element appears as an exception (i.e. it fails the data quality check), the data owner for that specific piece of data is required to approve the exception before the data is finalized and propagated to the production environment where everyone can access it. By implementing master data management strategies, integrating paper-based documentation into textual data and automating data quality rules, an insurer can reduce this significant burden which prevents them from extracting meaningful analysis.

Differentiate by predicting and analyzing with data
Once insurers have the right, high quality claims data, they can provide differentiated services and products by analyzing and making predictions with the data. Once claims data is analyzed, insurers can predict the complexity of a claim long-term. As an insurer more accurately understands the complexity of a claim, the appropriate adjuster can be assigned to the claim to ensure the claim and associated customer receive the appropriate attention.

Another valuable benefit derived from thorough claims analysis is the ability to identify the target customer and the acceptable amount of risk for an insurer to undertake. By analyzing specific characteristics of a customer’s claims (e.g. typical severity and frequency of claims for a customer), an insurer can determine whether this customer poses the right level of risk or too much risk to undertake. Through claims analysis, an insurer is able to understand the segment of customers who fall within the targeted range considered "acceptable risk." As a result, the insurer is able to invest the appropriate efforts in retaining the right customers rather than expending effort retaining those customers who pose an unacceptable level of risk.

In summary, insurers can strengthen business performance by focusing on retaining the right customers through continued improvement on their claims data.

To learn more about advanced analytics and West Monroe Partners, contact Gordana Radmilovic at gradmilovic@westmonroepartners.com.