How to use your brokerage’s data to gain and retain clients

Finding ways to harness your brokerage’s data can improve customers’ expectations and overall satisfaction rates, two brokerage heads shared at the Insurance Canada Technology Forum in Toronto.  

For example, brokerages tracking and taking action on their internal metrics will find their customers more satisfied with their service, speakers at ICTF2024 said.

Zac Sutherland, president of Sutherland Insurance, said his brokerage began tracking how long it took for clients to receive calls from their insurers after filing a claim. “A lot of them were taking not hours, not days, but sometimes weeks to reach out to clients in a claim scenario,” he said.  

His brokerage tracked insurers’ turn-around times on claims calls, using that to inform future clients of how long they could expect to wait before hearing from insurers in the event of a claim. 

“So, when the client calls and says, ‘I’ve had a claim, and it’s with market X,’ we’re telling them to expect…‘This might [take] seven days,’” Sutherland said. “We were amazed to find that most [clients] just said, ‘Thank you,’ and didn’t call us back for seven days.” 

Previously, he said his brokerage was, “getting a lot of complaints, naturally, as to what was going on” when clients didn’t hear back from insurers right away.  

Before tracking client calls, nearly 80% of the calls the brokerage fielded about claims were due to a lack of communication, Sutherland said. But tracking clients’ calls with insurers helped the brokerage to establish better expectations. In fact, it helped his brokerage improve its internal net promoter score (NPS), which is a common metric used to score customer experience programs.  

“We increased our NPS from below 70 to 98 within a year,” Sutherland said.  

There are many ways to use data, Steve Livingstone, president and CEO at aha insurance, said. His company uses it is to understand buying behaviours—particularly, the likelihood of buying.  

“We created something called a propensity-to-buy score,” he said. “We decided to take the attributes of all the quotes we’ve ever done, and compare them to the attributes of all the sales we’ve ever done. Lo and behold, there was a correlation.” 

The propensity-to-buy score ranks customers based on the likelihood they will purchase insurance after they’ve received their initial quote.  

“We created an algorithm we [consult] every time we quote a customer, and that creates an index score [from zero (low) to 100 (high)],” Livingstone said.  “[Our brokers] tag every lead…with an index number.”  

Then, they rank all leads by that index number to determine how best to convert quotes into sales. “We’re constantly updating that algorithm, so that we have that evergreen view of propensity-to-buy,” he said. 

For brokerages seeking to take action on their data, simple is better, Sutherland recommended. 

“My journey down this path was really focused on the visualization of the data over time,” he said. For example, if “50% growth is the target this year, we broke down what that would mean in terms of overall dollar growth, customer growth, etc.”  

For Livingstone, taking action on the numbers comes down to “making sure you’ve got a tool set that allows you to see those insights in a pretty real way,” he said.  

“We actually have a data scientist on staff now. We made that investment as a business because we wanted to drive that ability to [gain] insight and learn, and then institutionalize.” 

 

Feature image by iStock.com/martin-dm