2023 trends for data analytics in insurance
Using data to gain a competitive advantage seems to be a hot business topic these days, but hasn’t analyzing data always been the norm for the insurance industry?
While it’s true the industry has relied on various data points to evaluate risks, set prices and sell, data siloing has been the norm. For example, underwriters, actuaries and new business development teams might be working from different data sources and using different parameters to measure and make business decisions. But computing speed, digital handshake capabilities, and the volume and variety of data available are transforming how information can be collected and parsed into actionable intelligence.
So, in today’s world, what is data analytics for insurance, and what’s the value to insurance agents? This article tackles those questions and looks at emerging trends to see what lies ahead.
Why is data analytics for insurance important for the insurance industry?
By harnessing the vast amounts of data available today and employing the right business intelligence solutions to make conclusions about that information, data analytics is useful to the insurance industry in every facet of its business: to model what-if scenarios, diagnose why something happened, predict what might happen and determine what should be done next.
As such, data analytics is important to the insurance industry because the insight it offers can be used to scale value to everyone under the insurance umbrella: the insurer, the agency and the customer.
There are many practical applications for data analytics in insurance. These include mapping risks, setting pricing, targeting prospects, tracking sales and service, analyzing claims, detecting fraud and studying consumer behavior. A simple illustration of how insurance companies are using data analytics in property insurance is the monitoring of weather patterns, neighborhood claims history and construction costs to model risks and set pricing. Access to enriched data associated with a geographic location can also help an agent meet the needs of customers in real time, such as recommending flood insurance to a new homeowner.
Where should insurance agents start with data analytics?
Employing data analytics opens the door for agencies to begin benefitting from greater transparency and insights. If using data analytics seems too complex or out of reach, consider the following low-barrier ways agents can get started by leveraging their Agency Management System (AMS) and Customer Relationship Management (CRM) capabilities:
- To enrich prospect and client records for quoting, cross-selling and upselling, explore the agency’s system of record’s pairing capabilities to third-party data sources. For example, pulling in geographic information, such as weather and geo-specific risk factors, can be paired with prospect and customer records to deliver customized personal and commercial lines property coverage solutions based on those risks. This can also help identify coverage gaps and spotlight new opportunities within an agency’s book of business.
- To build marketing campaigns that target new prospects and convert leads in the pipeline to sales, set up reporting from the system of record to mine lead generation data and reveal a customer’s journey from first contact to sale. Use that data to inform future marketing efforts.
- To gauge customer satisfaction, automatically track online reviews and aggregate that data over time to illuminate and fix what’s not working and continue to support what is.
- To gain producer insight, set up automated reporting to track sales activities and conversions to illuminate producer productivity. Use that insight to automate rules for assigning leads based on the data, such as the geographic location of the lead, required expertise and potential deal value.
- To support customer service activities, set up automated triggers based on the data in the system of record to identify and alert staff to reach out to clients to check up on claims, identify cross-sell and upsell opportunities, or strengthen at-risk relationships.
2023 trends for data analytics in the insurance industry
Advancements in data availability and technology are chief drivers of the scale and speed at which businesses can use data analytics to their advantage today. Specific to insurance, the following trends are likely to shape the industry in the short term:
Fraud prediction—Using business rules and red flags to detect insurance fraud isn’t working, costing businesses and consumers an estimated $40 billion a year.1 Initiatives that drive the use of predictive modeling, link analysis (the evaluation of how data is connected) and artificial intelligence (AI) to prevent fraud before it happens is top of mind for insurance companies.
CRM insurance technology—With the ability to manage leads, segment customers, automate routine tasks, measure conversion rates and analyze information, it’s no surprise that the adoption of CRM software is expected to continue to be one of the fastest growing sectors in digital technology over the next three years.2
Behavior-based risk assessment and pricing—In 2023, it’s estimated that there will be 15.4 billion Internet of Things (IoT) devices, such as home voice controllers, smart TVs, connected thermostats, home security systems, domestic robots, connected appliances, smart door locks and connected car devices.3 With the ability to combine behavioral data generated by IoT with external factors, such as data from driving behaviors and neighborhood safety statistics, insurer adoption of predictive analytics to assess risk and set premium rates based on behavioral data will continue to advance.
Product optimization—Historically, it’s been difficult for insurers to customize policies at the individual level. But access to vast amounts of IoT data and advanced automation capabilities is changing that. Through machine learning, insurance companies will be focused on using predictive analytics based on customer behaviors, buying preferences and pricing sensitivity to offer the most attractive and relevant insurance products.
Personalized user experiences—With access to exponentially increasing sources of third-party data from IoT devices and the wider adoption of machine learning, chatbots and AI, insurers will continue to advance ways to turn the customer experience from a one-size-fits-all approach to a seamless automated journey where products are personalized, pricing takes into consideration behavioral data and claims can be settled quickly.
The future of data analytics
Data analytics is a constantly evolving science. What isn’t changing is the value data analytics will contribute to insurance companies in their efforts to target customers, improve customer experiences, streamline operations and enhance efficiencies. To learn more about why adapting your systems and practices can help you succeed in today’s changing environment, check out the Guide to Agency Technology and learn more about how Nationwide partners with you to stay competitive in today’s dynamic market.
The information included in this article was obtained from sources believed to be reliable, including subject matter experts, to help users address their own risk management and insurance needs. It does not and is not intended to provide legal advice. Nationwide, its affiliates and employees do not guarantee improved results based upon the information contained herein and assume no liability in connection with the information or the provided suggestions. The recommendations provided are general in nature; unique circumstances may not warrant or require implementation of some or all of the suggestions. Nationwide, Nationwide is on your side, and the Nationwide N and Eagle are service marks of Nationwide Mutual Insurance Company. ©2023 Nationwide.