By Kevin Sheetz
Big data and analytics are fundamentally transforming business decision-making. Insurance agents and financial planners can now harness massive stores of structured data to better target potential clients, verify income and even improve customer retention. Big data equips them with knowledge that makes the entire process more efficient and effective. But that’s true only if the information is accurate.
Many financial institutions collect their data from surveys or third parties, and that information often includes misinformation or selection bias with the samples. Boiling down the most relevant and accurate data is complicated even for data scientists, and few companies have benchmarks to help parse and interpret the constant stream of new data they collect.
Now more than ever, the veracity of data is paramount. Tapping into accurate and relevant data that provide context will help agents and financial planners connect customers more easily and accurately with the life insurance and retirement planning they need while building lasting professional relationships.
Insurance and financial planning in the land before data
If you ever dialed your way from A to Z through the telephone directory, or went into a meeting blind to a customer’s income, number of children or real needs, you’re likely a veteran of the insurance and financial world of just a handful of years ago.
Access to data was limited, which meant that building relationships yielded the most value in understanding a customer’s needs. If you were fortunate to have access, a quick look at a potential client’s home and auto insurance policies would give you some insight prior to meeting them. But until you spoke with them, you couldn’t gain a true sense of the type of life insurance, annuity or retirement advice they might need. Going in blind costs you time that could be saved with a better sense of whom you’re going to talk to.
Lack of data made life more difficult for underwriters as well. The painful process of collecting paper W-2 forms to confirm income was slow and tedious. The weeks it could take to follow the paper trail and verify income could be spent on more fruitful pursuits.
Financial planners, before having an in-depth conversation with prospects, had no way of knowing how or where those prospects were investing their money and whether they were leveraging their assets as strategically as possible.
The dawn of data
Now big data can make the whole process easier for insurance and financial professionals to find qualified prospects as well as understand their financial position. Instead of combing through the phone book and calling hundreds of prospects a day, you can pinpoint specific streets where there’s a concentration of wealth, where there are families with a large number of dependents, or where the residents have assets that could be better invested.
Life insurance agents who are meeting with a prospect may not only have home and auto insurance policies to reference, but can get a detailed picture of that prospect’s household income and number of family members. With this additional information, they can begin to estimate a prospect’s insurance and financial needs, and come to the meeting prepared with specific product recommendations.
Rather than waiting for and relying solely on the paper trail to verify income, you can instead compare the reported income with the distribution of all income in that ZIP code to determine if it’s reasonable.
Data on interest income can point to opportunities for financial or retirement planning. For example, if you see the average interest income generated in a particular area is $300 at an average 1 percent interest rate for savings accounts or certificates of deposit, you can estimate that households in that area have approximately $30,000 on average in interest-bearing accounts. This shows a potential opportunity to step in with a more strategic approach to their finances.
Data consumer, beware
But while data represents a powerful new tool for finding, vetting and retaining customers, there still are limitations to much of the information that insurance and financial professionals have at their fingertips. When financial institutions collect their data from surveys or third parties, the information often includes holes and biases.
In these cases, the data doesn’t produce more strategic decisions. Instead it produces false confidence in data that only partially or incorrectly fills gaps in knowledge. Garbage in, garbage out as they say in the data world.
Big data is said to be defined by four Vs: volume, velocity, variety and veracity. Of these, veracity is the most important. To determine whether the income a customer is telling you is correct, you need to know your data is accurate. In order to find qualified prospects at the street level without spending hours combing through the phone book, you need to ensure data is relevant. And in order to walk into a meeting and know exactly which products a client needs, you have to draw on data that provides context.
Veracity requires a technological foundation that pulls complete data from reliable sources and establishes benchmarks against which brokers and advisors can weigh any insights. As insurance and financial professionals increasingly draw on data to streamline their boots-on-the-ground approach to finding and serving clients, surveys and third-party data will only create as many challenges as they solve. The veracity of big data should be the rallying cry of all insurance and financial professionals as they seek more strategic ways to match the right products to the right people, and connect and build relationships with their customers.
Kevin Sheetz is CEO and co-founder of Powerlytics. A former audit and advisory partner at KPMG, he was a financial services specialist earlier in his career. Kevin may be contacted at [email protected].