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article imageQ&A: Why data & analytics will transform insurance in 2020 Special

By Tim Sandle     Dec 17, 2019 in Business
The insurance landscape is changing rapidly, and agencies need more insight to adapt with the industry. Their data is an untapped resource, but most teams don’t know how to translate it into actionable insights. Chad Hawkinson, at Vertafore explains.
Data and analytics tools can help agencies stay ahead of change and will be vital in 2020 to provide clients with the experience they expect. They give agencies a 7% higher client retention than those that don’t use them, showing they can deliver a better client experience by being more informed, according to Chad Hawkinson, Chief Product and Data Officer at Vertafore. Digital Journal spoke with Hawkinson to learn more.
Digital Journal: How is the world of insurance changing?
Chad Hawkinson: One major trend is the instrumentation of individuals and objects, and the resulting volumes of data made available. Insurance is all about quantifying risk and assigning an economic value to mitigating that risk, and data is the currency that makes that process run. Traditionally, carriers leveraged their experience with similar individuals or businesses to determine rates. Until recently, the data available was episodic in nature, collected only at a specific event like a policy renewal or claim. The rise of connected devices, and more broadly the Internet of Things, is having a profound impact on the data available to underwriters to make decisions about risk.
Most people regularly use a device that tracks their whereabouts (their smartphone), and many of us are wearing devices that are collecting biometric information at a high frequency (a device like an Apple Watch is capable of collecting millions of data points per day.) Data like this is used by innovative health tech companies to detect, or in some cases predict, the onset of disease long before an individual is clinically symptomatic.
Similarly, we are starting to see innovative insurance companies use biometric data to offer competitive and differentiated products for life insurance, with their underwriting contingent on you proving your health through device or activity data. This trend will only increase as we – and the devices we use – are connected to the internet. Data privacy and informed consent of the use of data are becoming increasingly important, and the government is increasingly putting laws in place to protect consumers from adverse use of their data. It’s easy to imagine the public outcry if a company leveraged a consumer’s health data to make an underwriting decision without that consumer’s consent.
DJ: What experiences do clients expect?
Hawkinson: There is a generational shift occurring in the world of insurance. Many of the conversations that we have with our customers indicate they are having difficulty attracting and retaining talent from the millennial generation. This is due, at least in part, to the expectations this generation has around the technologies they have access to at their jobs. However, aspects of our industry still run nearly entirely on paper, and agencies and brokerages will need to modernize their technology both for the sake of their employees and their customers.
Customers expect to interact with their agents when they want and how they want, and to have a more consumer-grade experience. For example, I’ve become accustomed to doing much of my shopping on Amazon, where other products are recommended to me based on my buying history, what data I’ve consented to allow Amazon to collect and analyze and what Amazon has learned about others like me over time. That level of service is fine for low-dollar, everyday purchases that I make where I don’t need advice or counsel.
However, I also at times need a very high-touch purchasing experience, like when replacing my skis, buying a car, etc. In those cases, I want to talk with someone with deep experience with the product that can help guide me through the purchasing process. Customers of insurance agencies increasingly want both of these experiences. If they want to view or download their policy information, there should be an app or website to do so, but in many cases there is not.
Similarly, if they are just out of school and need their first renters insurance policy, many people will be comfortable completing that transaction online, without any interaction with another human being. However, if that same person has just had their first child and needs to understand how their risks have changed and what types of insurance they might now need, many people want to sit across the table from someone for a discussion.
DJ: How important is data for the sector?
Hawkinson:The increase in available data is impacting how products are packaged and sold, and much of that is being driven by innovative carriers. Another factor that warrants discussion is the how agencies can leverage data and insights to better run their business and service their clients. Every transaction generates new data: data about the customer, agent, producer and customer service rep, and data about the product sold or claim made. There is a huge amount of ‘exhaust’ that comes off of people’s interactions with systems in processing data, and there is an opportunity to leverage this data to optimize the industry.
Going back to the Amazon model, agents need to have a deep understanding of the potential risks that their clients and prospects face. Many agents develop this understanding over time through working with a particular client set that has a common set of risks. Data is increasingly allowing us to surface insights to an agent that has not previously worked in a particular line of business, including which carriers have an appetite to underwrite that business in their geography.
DJ: How should teams translate data into meaningful insights?
Hawkinson:Adopting analytics capabilities is hard, particularly in an industry like insurance that trades on personal relationships and intimate knowledge of customers as individuals. Business is still often done over a meal or round of golf, and the use of data and analytics in this environment can often be seen as too cold and calculating.
The key is having a clear understanding of your objectives and to align your adoption of tools and capabilities to those objectives. Is margin expansion my primary goal? If so, am I focused on growing my revenue on a flat or decreasing cost basis, or cutting my cost basis while maintaining revenue, or both? Am I trying to grow into a new line of business within my existing geography, or am I looking to expand with my existing LOBs into a new geography? Only once you have really defined your goals and strategy should you start to think about what insights you need in order to achieve those goals, and only then should you start thinking about the data you need to get there.
A common trap in adopting analytics capabilities is over measurement. I once had a client that tracked 150 metrics about her business unit, monthly. She and her team found themselves paralyzed by the data, unable to find signal from the noise. We worked with them to whittle that down to thirteen KPIs using the following framework: 1) What is the universe of what I can measure? 2) Of the things I can measure, can I take action to influence them? 3) If I can influence it, does it have an actual impact on the things that I care about?
My recommendation is to use the above framework to identify the handful of measurements that really matter, and look at them between once a month and once a quarter, depending on the rhythm of your business. It’s important that these are constructed in a way that does not require a large operational lift beyond the initial implementation. I’ve seen customers go down this path successfully only to fall short on automating the implementation and reporting, subsequently finding themselves in a situation where they need to build a team to manage the reporting process at great operational expense. This is an area where pre-packaged tools and vendors can really help.
DJ: Can data and analytics tools help?
Hawkinson:There are 30,000+ insurance agencies in the US, and we estimate nearly 3,000 insurance carriers. Our experience is that the top 1 - 2% of these organizations are making large investments in building data science teams and developing their own analytics capabilities. Their primary interest remains in obtaining additional sources of data. Outside of the top 1 - 2%, the mid-sized or small, often family-run organizations, don’t have access to enterprise scale tools or capabilities. In these cases, we see a much stronger desire for turnkey solutions that do not require an investment in a team or in-house infrastructure and tooling. This is where organizations like Vertafore can really help.
We offer several tools that address the needs of carriers and agencies, and our flagship product is called RiskMatch. RiskMatch and tools like it help organizations extract and standardize data across their Agency Management Systems into a singular view. Once the hard work of standardization is complete, agencies are then able to leverage the out of the box capabilities to win more new business, upsell new products to their existing portfolio of clients and optimize the profit made on transactions. This is enabled through a series of benchmarking capabilities made possible by the very broad data set that Vertafore has collected, normalized and anonymized through working with our clients.
Separately, analytics tools can be used to identify areas to focus on for internal process improvement. As the tools used by insurance agencies are increasingly becoming digitized, it is becoming possible to track things like cycle times for completing certain workflows and the profitability of certain types of business.
We are just starting to see the emergence of capabilities that are predictive in nature, and we predict this will be where the next wave of major improvements for the industry comes from.
DJ: Are there any key case studies?
Hawkinson:We’ve found that customers that have embraced a digital transformation and have adopted analytics solutions are outpacing their peers in two areas: client retention and product density. For example, customers of Vertafore’s that have embraced RiskMatch have, on average, 4 products in place with each of their customers against an industry average of 2.4. Additionally, agencies that use RiskMatch enjoy an annual client retention rate of 89%, whereas non-RiskMatch users have an average retention rate of 82%.
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