The insurance industry is being disrupted. Tech giants such as Tesla, Amazon and Alibaba are looking to get into the insurance industry and come armed with vast reserves of consumer data to help them shape products and market effectively. Auto insurers are moving from a traditional insurance model to a usage-based one to meet consumer expectations and assess risk in real-time.1 In order to remain competitive, insurers need to understand how to use big data to better serve customers and mitigate risk while increasing the bottom line.
Today’s consumers want more personalized communications as well as an immediate response to their inquiries. Hubspot’s recent research, found that 90% of customers rate an “immediate” response as important or very important when they have a customer service question.2 Insurers can provide personalized, instant customer service using AI-powered chatbots or Natural Language Processing (NLP) to keep customer satisfaction high.
Case study: AA Ireland uses chatbots to increase conversions and decrease agent handling times
Auto insurer, AA Ireland wanted to increase operational efficiencies and quote conversions. To address these needs, they implemented an AI-powered chatbot which could tap into consumer data to provide more personalized insurance policy recommendations 24/7. Customers can message the bot on both the website and on the mobile app, requesting help to find the right coverage at the right price. If a customer needs further assistance, or has a more complicated query, the chat is transferred to a human agent.
The results: AA Ireland saw a 40% decrease in agent handling time and an 11% increase in quote conversions.2
The underwriting process – properly calculating the risks and coverage of policy holders – is time-consuming. However, access to real-time data can make the underwriting process more automated and intuitive for actuaries. Machine learning uses an enormous amount of data to achieve a more comprehensive analysis of a potential policy holder’s risk. AI and ML algorithms help insurers verify policy holder data (from basic contact information to assets) and perform more accurate risk assessment based on an analysis of the prospective policyholder’s past behavior as well as the behavior of those who have similar demographic characteristics. For example, teenagers tend to have higher auto insurance premiums because they are inexperienced drivers and are therefore more likely to get into accidents.
Insurers can build or work with solutions providers to develop their own AI/ML algorithms to better evaluate premiums for potential policy holders and shorten processing times. Shorter processing times and more accurate premiums will help insurers satisfy and retain high-value policy holders.
Example: Wearables in employer-sponsored medical or life insurance
There’s much debate about the ethics of using wearables, such as activity trackers and glucose monitors, to collect real-time health information on policy holders. However, in the American workplace, wearables are becoming more popular. Health insurance companies such as United Health Group, Humana, Cigna, and Highmark have established workplace programs that encourage the use of wearable devices.3 For example, United Healthcare’s employee wellness program has allowed integration of data from Samsung and Garmin wearables4 since 2017. UnitedHealthcare’s program, UnitedHealthcare Motion, gives participating employees up to $1,000 a year if they hit certain fitness goals, such as 10,000 steps a day with 3,000 of those steps within 30 minutes or 500 step intervals throughout the day.5 Insurer Humana recently announced that wellness program participants can connect hundreds of smart devices to their Go365 wellness program, ranging from high-tech smartwatches to simpler blood glucose monitors.6
These employer wellness programs also have benefits for insurers. The data provided allows insurers to better assess premiums and risk for that specific work force. Wellness programs also promote preventative care, which means people are more likely to catch potential health problems before they become serious and more costly.
A Forrester report found that seventy-nine percent of organizations that exceed revenue goals have a documented personalization strategy.7 Personalization is just as important in the insurance industry as it is in any other. Insurance companies can use first and third-party data to create more compelling messages, improving conversions and engagement.
Case study: Data Axle helps a top 5 U.S. insurer personalize local ads to increase acquisition
It can be frustrating for consumers to see ads for products that they don’t qualify for or that aren’t available where they live. Data Axle helped a large national health insurer use data to create geo-targeted acquisition campaigns with ads personalized based on the location of their audience. By combining location data with demographic audience attributes such as age and occupation, the insurer was able to target consumers with products that served their specific needs and were available in their area.
The results: the geo-targeted acquisition program has driven a 4% to 8.2% lift in acquisition rate for their target audiences, based on the specific segment.
For more details of this compelling case study, keep reading.
Smart data analysis and use of new technologies like AI, ML, and NLP can create a lot of opportunities for the insurance industry, including improvements in customer experience and accurate risk assessment. Insurers need to continuously modernize their programs and processes to keep pace with changing consumer expectations and industry disruptors.
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As Content Marketing Manager, Natasia is responsible for helping strategize, produce and execute Data Axle's content. With a passion for writing and an enthusiasm for data management and technology, Natasia creates content that is designed to deliver nuggets of wisdom to help brands and individuals elevate their data governance policies. A native New Yorker, when Natasia is not at work she can be found enjoying New York’s food scene, at one of NYC’s many museums, or at one of the city’s many parks with her two teacup yorkies.