Insurance companies are finding ways to leverage big data both internally and externally. In doing so, they are becoming more informed about their customers and agents alike.
From diagnosing a patient to determining what pitch to throw to a batter, big data has changed the way people approach problems across virtually every profession — and the insurance industry will not be left untouched by these changes. In fact, according to a 2016 Towers Watson report, 42% of insurance carriers use data analytics in their pricing, underwriting, and risk selection. That number is expected to jump to 77% over the next two years.
Data-driven processes in insurance are nothing new. For decades, insurers have used customer data and risk assessment techniques to inform pricing practices. However, even with years of experience handling customer data, the insurance industry faces a unique set of challenges in tapping the massive amounts of unstructured data available today.
What Can Analytics Do For Agencies?
Today, data is available in higher volumes than ever before. The US, UK, and EU all recently launched “open data” sites to make massive quantities of government statistics available to the public, and third-party data sources reduce the need for carriers to source data internally. Given the sheer amount of data available, the possible applications for analytics in insurance are quite literally endless.
Agencies can leverage data to track and assess an individual sales professional’s effectiveness, analyze the factors that lead top-performing salespeople to leave an agency, and identify the agents who are best at retaining clients. From there, they can implement a series of operational changes to train other agents to perform in a similar manner. Companies are also using data to segment prospects from internet lead providers by intent. Additionally, tools like lead management software and CRMs let agencies know which lead sources and strategies are most effective.
Externally, data can be used for behavioral analysis of clients and prospects. Years ago, the auto insurance industry discovered that customers with good credit ratings generally exhibit safer driving habits. Now, with a new wave of data analytics tools available, there is no limit to the number of data points an underwriter can assess before drawing up a policy.
Strategizing with Analytics
The data that you collect should inform your business decisions, but those decisions should not come at the expense of your support staff. Instead, the data should be used as a means to drive institutional change in tandem with the needs identified by your team.
Ask yourself a few questions before starting a new project: What is the business value of this analytics project? How does this new tool lead to improved operations, revenue growth, or increased profitability? Work with experienced analytics experts who can integrate resources from both internal and external sources in order to unlock the business potential of your agency data. From there, they can partner with functional decision-makers to build out powerful predictive models.
Whenever a team is tasked with building a new analytics strategy, the end goal should always be seamless integration into the agency workflow. That means each tool should be functional, easy to use, and trusted by the professionals tasked to interact with it on a daily basis.
In the end, data analysis should make the job of the agent more effective and efficient. Integrating data solutions into your business operations is a continuous process — you’ll need to develop, test, tweak, and be willing to learn in order to ensure that your firm is leveraging data to its maximum potential.