Picture this scenario - a CEO is trying to decide whether to invest millions of dollars in a new product line. Before committing to the new product, she asked her executive team to draft a proposal for the Board of Directors on how much market share the company could gain, marketing dollars to achieve penetration, customer acquisition cost, and potential liability from the new product. Two weeks later, the executive team presents their reports to the CEO with a caveat - while they believe that the new product will be profitable and only present minimal risk, they are basing their conclusion on data that is unreliable.
Making critical business decisions is difficult enough. It’s even more challenging when the organization does not trust its data.
The CEO now has an especially tough decision - move forward with unreliable data, or miss an important opportunity to grow the company? While this seems like a hypothetical scenario, it's very much real. In a 2017 survey of 1,300 CEOs by global consulting group KPMG, only “35 percent said they have a high level of trust in the way their organization uses data and analytics.” Over 60% of CEOs polled reported having some reservations or active mistrust in their data and analytics. Making critical business decisions is difficult enough. It’s even more challenging when the organization does not trust its data.
Data integrity is a thorny problem for the insurance industry. While the KPMG survey was not insurance specific, if it had been, it would have uncovered that within insurance, as much as 80% of executives say their data has a low level of reliability, based on research conducted by the authors. In an industry that revolves around risk management, data integrity issues compound losses, slow product innovation, and compromise the ability of insurance companies to grow their market share. This issue results from three principal contributors, which, when resolved, allows companies an important competitive edge in the crowded insurance marketplace.
Insured Changes
Insureds are busy - running businesses, helping their family, making career moves. It’s fair to say that when major changes occur, insureds do not always notify their agent in a timely manner. When insureds fail to do this, they miss out on lower rates and adequate coverage and can have uncovered claims. The data the insurance agent will have on that policy will also be out-of-date.
The longer this cycle occurs, the less reliance executives will place on making data-driven decisions, further weakening the enterprise.
Without having the correct information, it is difficult for insurance professionals to understand their own exposure, new opportunities, and the likelihood of churn. Having up-to-date insured information is a compounding problem - the more out-of-date information there is, the longer it takes to correct it. The longer this cycle occurs, the less reliance executives will place on making data-driven decisions, further weakening the enterprise.
This problem becomes a perpetual plague without a system to continuously verify policyholder information. To understand the severity of this problem, and rectify it, begin by checking the average age of your data. Data that is less than six months old is less likely to be out-of-date than data that is two years old. As an organization, set a goal around the recency of your data, with the oldest data only a year old. This will help provide a 90% confidence that your customer data is correct.
After establishing the data recency goal and communicating it to the organization, begin verifying the oldest data first. While there are many ways to go about this, the easiest and least time-intensive way may be running an automated email campaign. Most AMSs or CRMs can help you do this - in the email, ask the insured to verify the information you have on file for them. If it’s incorrect, have them update it themselves, which will reduce the time required by the agent. Flag any updated information so the agent can verify that the policy is still valid. As a best practice, run this automated campaign every quarter that the insured does not have contact with their agent.
Human error
It’s no secret that humans are fallible - it’s why we have spell check. But unlike an email, data is monolithic. Lack of data integrity is often a direct result of poor data entry. Ensuring that information is being entered correctly, either manually by staff or on customer-facing systems, is crucial. Let’s break data entry problems into two subsections - staff training and proper AMS or CRM configuration.
While companies expect staff to utilize proper data entry practices, such as verifying the information before they submit it, how many organizations have formal training to ensure this? Training staff for data entry is simple and cost-effective - put together a short job-aid listing the steps they should take every time they add or update customer information. Also, consider training as a recurring need. Investing in it annually will build organizational excellence paying dividends in data quality and customer service.
Configure your system with the assumption that users will enter ‘bad’ data.
Properly configuring the system that houses the customer information is a crucial step for ensuring data integrity. Regardless of whether the system is for internal use or has customer-facing components such as a portal, the system should alert users to potentially erroneous data. Configure your system with the assumption that users will enter ‘bad’ data. This does not mean that the information entered will be willfully negligent (even though it may,) but life provides an unlimited amount of distractions that can lead to poor data entry.
The most cost-efficient way to configure the system to ensure data accuracy is to break the information being entered into bite-sized pieces. Rather than having users enter the address information on one line, break the field into an individual house number that is a number field, a street name that is a text field, and so on. This will allow the system to alert the user that a house number has an 'O' rather than a 0. To further ensure data integrity, invest in a plugin that matches entered information to actual address data providing verification and trust. For information that is more subjective, such as a company’s total number of employees, build a check that asks the user to confirm what they have entered is correct before allowing them to submit.
The best way to ensure proper data entry is to ensure that incorrect data cannot be, or has as little chance as possible of being entered. Doing this will drive the accuracy of your company’s data above the 90% confidence mark, which will more than repay the cost of user training and system configuration.
Data storage
It should come as no surprise that data stored outside of a central AMS or CRM is less likely to be accurate. Remember, the bronze rule of data - spreadsheets are never your friend. The only thing worse than having information on spreadsheets is storing raw emails and policy documents outside of a central system. Either of these is a guaranteed way to have little to no confidence in your data. If storing data outside of a central system is a surefire way to reduce a company’s ability to remain competitive, paralyze decision-making, and reduce profit, then why do so many insurance professionals do it? It’s called spreadsheet paralysis and refers to the insurer’s common argument that if it’s always been done this way, why change it?
To remain competitive, companies must make data-driven decisions. Besides complicating decision-making, spreadsheets are a compliance risk, reduce an organization's intellectual property (as someone who leaves can take insured information with them), and increase staffing costs. Implementing new software can be an expensive, time-consuming proposition, but for those reasons alone cannot be avoided. After all, the future success of the company depends on the management's ability to make accurate and informed decisions. Before purchasing a new software system, businesses should take the time to evaluate what their actual needs are, and select a central system accordingly.
Companies that trust their data to make informed decisions regularly outperform those that do not. For that reason alone, insurance companies, from retail brokerages to large carriers, should invest in their data integrity. It’s an investment that will repay itself a hundredfold as the divide between companies that use their data and those that do not continues to widen at an exponential pace. As with any new business initiative, start with the easy opportunities first. As the organization's capabilities mature, invest in the more intensive and costly upgrades. By keeping information up-to-date, ingraining proper data entry processes into the company’s culture, and investing in centralizing the data, companies will increase data accuracy to the 90% threshold and beyond, empowering decision-making and boosting revenue.
