Challenge
A U.S.-based Fortune 500 property and casualty insurer needed additional datasets to assess risk in the underwriting process. With a better understanding of consumers’ in-store behavior, our client would be able to confidently deliver unbiased, fair, and accurate quotes. With nuances in product offerings and target markets, our client’s insured businesses can be collectively evaluated by one common denominator– data.
“One quote that’s common in the insurance world is there's no such thing as bad risk, only bad pricing”
– Vice President of Underwriting, Property and Casualty Insurer
But the insurer required clarification around more than, “How many people visit a store location?” Digging deeper, our client sought answers to questions regarding consumer behavior, including:
- On average, how long are people staying at this store location?
- Which days and hours are identified as peak times? Also, what times of year?
- What do changes in foot traffic look like YoY, MoM and QoQ?
“Unacast was the most honest and transparent in outlining exactly what they did and did not have the capacity to execute.”
– Vice President of Underwriting, Property and Casualty Insurer
Using the traditional method of transaction counts or revenue to measure in-store visits proved to be unreliable. These measures fail to account for customers that shop in a group with a single payer, rendering policies grounded in this method invalid. With Unacast's Migration Patterns product, our client was confident that there existed a better solution.
“We felt like we were in good company after meeting with experts from their supply, solutions, and client success teams– their tiered approach to support was instrumental in setting them apart from their competitive set.”
– Chief Underwriting Officer, Property and Casualty Insurer
Solutions
Underwriting business insurance policies is becoming increasingly complex as both businesses and legal systems evolve. During the information gathering process, an underwriting team will look for details about the physical location, including the building’s age, occupancy, and use of machinery, in addition to statistics about the business, such as revenue and financial health.
“At the end of the day, what matters most is accuracy. Our data science team confirmed Unacast’s data as in alignment with our internal standards for validity on their first pass, earning our trust in Unacast.”
– Vice President of Underwriting, Property and Casualty Insurer
Ensuring profitability means ensuring that quote prices adequately cover potential losses, as positive growth year over year is dependent on a foundation of properly assessed risk. That said, the insurance industry has traditionally been late to the game in terms of adopting and integrating alternative datasets that validate risk evaluations.
“While location data is one only piece of the puzzle, we believe that it paints a more holistic picture helping us generate more objective, accurate quotes.”
– Chief Underwriting Officer, Property and Casualty Insurer
Results
Attributing success to profitability, our client didn’t expect the dataset to grow business. Yet, this major property and casualty insurer was able to use a set of custom metrics to tie a through line between identified risk and the number of claims submitted. What’s more, they were able to confirm which times, days, and seasons were most likely to result in increased claims.
“We came with a wish list of custom variables to help us effectively manipulate the data, and the Unacast solutions team was able to deliver on about 80% of those asks.”
– Director of Data Science, Property and Casualty Insurer
Like most insurance companies, our client uses a loss ratio and combined ratio to measure the profitability of an insurance company. The loss ratio measures the total incurred losses in relation to the total collected insurance premiums, while the combined ratio measures the incurred losses and expenses in relation to the total collected premiums. While our client won’t have a full understanding of how our data helped improve loss ratios until after a full policy year, we did receive confirmation of initial positive improvements during their testing process.
“You have no idea how many people are coming to the businesses that you insure. And you're probably looking at the wrong information– financial transactions.”
– Vice President of Underwriting, Property and Casualty Insurer
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By leveraging Unacast’s location data, our client is confident in their ability to efficiently and effectively optimize their pricing strategy for profitability. What’s more, the value in the partnership extends past a single offering to include unparalleled support from Unacast’s solutions, client success, and product teams.
