Last week we talked in broad brushstrokes how our Data Science & Analytics team helps companies use their own data to focus in on the best ways to increase their profitability. Often the result is a custom submission grader. Continuing that discussion, here’s another look at how the team uses data to improve hit ratios and help National Programs companies grow their business.
“We start with your major KPIs: profit margin, core revenue, organic growth,” explained Jason Perone, data scientist for the team.
“Then we get into the drivers of those KPIs. What’s your yield ratio, quote ratio, hit ratio? You need all this information to determine your sweet spots – where you garner the most accounts, are the most competitive, or where a new product change can move the needle,” he said.
“The custom submission grader is a real eye-opener. Our goal is to lift one or more of those KPIs – your organic growth, profit margin and core revenue – as seen through the lenses of your marketing, underwriting, pricing and/or claims.”
Many companies simply don’t store the data needed to help them grow, he explained. Or if they store it, they’re not sure how to use it. The team helps them gather data and then, by careful study, they build a custom submission grader unique to that company’s KPIs. The grader shows the company’s sales and underwriting teams where to focus so that they can achieve the most bindable accounts.
Snapshot case study: How the Data Science team boosts hits for Lawyers Protector Plan
Specializing in professional liability coverage for attorneys and law firms, Lawyer’s Protection Plan (LPP) receives upwards of 600 submissions a month, totaling roughly 6,000-7000 submissions annually. To handle the flow of submission, LPP utilized a largely manual grading process which was inefficient and could slow service response time. At the same time, LPP was struggling to capture a sufficient amount of new business regardless of the high volume of incoming submissions.
Laura Simon, Lawyer’s Protection Plan executive vice president explained, “We had two issues, and they both stemmed from the fact that we have a large submission flow.” She continued, “New business was down, but we had the incoming submissions that should produce new business. And the efficiency was weak.”
Constructing a Solution
LPP partnered with National Programs’ Data Science team to tackle the growth and efficiency issues while streamlining the grading process. Director of Data Science Daren Eiri astutely described the challenge, “We wanted to help them increase new business.” Eiri and his colleagues designed and implemented an automated submission grader and placed it at the beginning of the submission flow. The custom submission grader uses historical underwriting data to inform present submissions in real-time and apply a grade value to each submission. A report is then forwarded onto underwriters, detailing which submissions are incomplete, declined or in-appetite. Eiri explains, “It’s all about assisting the underwriters to improve their workflow. The grader is there to provide an indicator of the likelihood that a submission will bind.”
With the submission grader in place, Christina Melia, assistant program leader, has witnessed a marked growth in new business. “We are better able to prioritize submissions and give attention to the ones that are likely to bind,” she said.