Being able to collect better data regarding sales and customer behaviour are obvious reasons to upgrade the data system at your brokerage, but there are other benefits, Vancouver-based P&C insurance consulting firm Smythe Advisory said in a blog published Tuesday.
Assuming your brokerage has made the commitment to upgrade your data system with the view to provide better management information, here are five main buckets on which to focus:
Ultimately, a brokerage needs to know what channels are generating sales, as well as the associated costs. Such information would include customers generated through producers, referrals, online advertising, traditional advertising, affinity relationships, existing customer development, lead purchases, and physical location.
Wages and benefits account for 75% of the average brokerage’s total expenditures, said the blog Five Reasons Data Analytics are Important for all P&C Brokers. Understanding how team members interact with both the clients and their file is fundamental to current profitability and planning for growth and manpower needs in the future.
Landing a new account and retaining the client for each renewal is only be a starting point. Increasing the ‘life time value’ of the client relationship flows out of providing better service and awareness of the customer’s needs. In turn, this leads to appropriate levels of coverage, multi-policy sales, and providing appropriate endorsements.
Understanding how your team markets policies, as well as knowing your status regarding volume requirements, contingent profit, overrides, and appropriate distribution of business, is a crucial component of overall management strategy.
Expense budgeting, monitoring and review is an obvious, but often-overlooked component of data analytics.
“The heavy lifting in the process is not extracting the data and formatting it into an appropriate dashboard,” said the blog from Smythe Advisory partner Alex Wong and senior manager Gagan Ahluwalia. “Instead, it is ensuring you are capturing the right data when adding new customers and data points for existing customers.”
A simple example is recording the date a new client is onboarded, Wong and Ahluwalia wrote. This is an easy metric to capture and invaluable for the calculation of life time value. But it’s often not captured in the data.
Other data points might include sales channel sources, founding producer, current producer or account representative, customer service representative, consistent customer identifiers, accurate recording of re-marketing versus new business, and additional coverages.
Generally, this information is available in customer files, but not in a format that is easily extractable. That said, all the major brokerage management systems have the capabilities to reflect this information. The issue is first understanding the value of the data and then committing the resources to recast the information in a way so it can be easily accessed, the blog said.
In the case of insurance brokerages, there are different degrees of analytics.
At one end of the spectrum, brokerages that make decisions based on the review of financial statements, new and lost business reports, and talking to staff and customers. A more sophisticated approach may be to use off-the-shelf data analytics solutions to plan sales campaigns, deploy resources, and gauge market relationships.
At the other end of the spectrum, artificial intelligence solutions offer analytical insights and “digital CSRs” that work on their own or in conjunction with human agents.
“It has been our experience that small to mid-sized brokerages with up to, say, $4 million in revenue do a great job in managing their business,” the blog said. “They know the strengths and weaknesses of their team, have a good handle on who their customers are, and why they buy. These brokers can manage their brokerage based on their experience, gut feel and by reviewing the monthly financial statements and new and lost business summary reports.”
But mid-sized brokerages with revenues in excess of $5 million, with plans to grow bigger, typically have a sales team, relatively good data, and are professionally managed.
“As a brokerage gets larger, it becomes more difficult to manage based on instinct. Once a brokerage nears commission revenue of $10 million, then formal systems become a necessity. With approximately $65 million in premium and 30,000 to 35,000 active policies, the brokerage is too big to effectively manage without some analysis of the underlying data.”