Five Reasons Data Analytics are Important for all P&C Brokers

Data analytics is the process of analyzing raw data in order to gain meaningful insights into business conditions, with the view to make better decisions. Large corporations will often take this a step further by automating the decision-making into a tech-driven process by developing algorithms where pricing or offerings can automatically be adjusted in real-time to maximize sales or profits.

In the case of insurance brokerages, there are different degrees of analytics. At one end of the spectrum, there’s a broker that makes decisions based on the review of financial statements, new and lost business reports, and talking to staff and customers. One may take a more sophisticated approach by using off the shelf data analytics solutions such as “Applied Analytics” offered by Applied Systems to plan sales campaigns, deploy resources and gauge market relationships. At the other end of the spectrum, there are AI solutions such as “Amelia” developed by IPsoft that offers analytical insights and digital customer service representatives (CSR) 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. 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.

However, there is another category of brokerage. These are mid-sized brokerages with revenues in excess of $5 million who have plans to grow bigger. These mid-sized and larger brokerages 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 to 35 thousand active policies, the brokerage is too big to effectively manage without some analysis of the underlying data.


At its core, benchmarking and data analytics have a common goal and that is to provide the information needed to make better business decisions. Benchmarking helps you know what is normal and what is possible, and analytics provides the insights to make decisions.  It has been our experience that there will generally be a catalyst that compels a brokerage to devote the time and resources to create management processes using data analysis. This typically will be one of the following:

  • Desire to maximize value for a future full or partial divestiture;
  • Need to increase profits to fund shareholder succession;
  • Integration requirements after the acquisition of a competing brokerage, and
  • Transition to a professionally managed employee-owned organization.


Assuming your organization has made the commitment to upgrading your data system with the view to provide better management information, let’s look at what needs to be managed and why. We generally focus on the following buckets:

  1. Sales – Ultimately a brokerage needs to know what channels are generating sales and what are the related costs. This includes customers generated through producers, customer referrals, on-line advertising, traditional advertising, affinity relationships, existing customer development, lead purchases and physical location.
  2. Team effectiveness – Wages and benefits account for 75% of the average brokerage’s total expenditures. 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.
  3. Customer Behavior – Landing a new account and retaining them for each renewal should 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.
  4. Insurance Markets – Understanding how your team markets policies, knowing your status as to volume requirements, contingent profit, overrides and appropriate distribution of business is an important component of overall management strategy.
  5. Expense Control – 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. 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. This is an easy metric to capture and invaluable for the calculation of Life Time Value but is often not captured in the data.  Depending on one’s objective, other data points are sales channel sources, founding producer, current producer or account representative, CSR, consistent customer identifiers, accurate recording of re-marketing vs. new business and additional coverages. Generally, this information is available in customer files, but not in a format that is easily extractable. Having said that, 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.

We will address some specific strategies for the analysis of brokerage data in subsequent blogs. If you have any questions or comments on this material or other aspects of brokerage management, please contact either Alex Wong at or Gagan Ahluwalia at