The insurance business traditionally relies on historical data to assess a risk and predict the likelihood of that risk occurring in the future. With digitization and usage of digital technologies across insurance business processes, data is being collected at every touchpoint of the customer buying journey. Insurers now have an enormous volume of data, even real-time data, that they can supplement historical data with to get a more comprehensive understanding of the risk or customer that they want to do business with. Data analytics, then, is the treasure trove helping insurers derive deep insights from their data to apply to their processes, products, and services to drive business value across the insurance value chain.
Forward-looking insurers are harnessing the power of data analytics, leveraging AI and machine learning, to make smart business decisions faster, build innovative products & services, and achieve better business outcomes. Let’s take a closer look at 5 innovative ways of applying data analytics in insurance:
Data Analytics in Insurance: Use Cases
Design Customized Products
Customer-centricity is playing a key role in today’s competitive insurance business landscape. Customers today want every product and service to be personalized and tailored to their needs. Data that is being collected on every customer—publicly available and/or the data that is collected from points of interactions with the carrier—can be useful to understand the customers’ needs and preferences.
For instance, Natural Language Processing (also known as text analytics) can build data engines that go through multiple social media platforms and review websites, and develop their own digital content to study and comprehend what an insurer’s customers are talking about. Meaningful insights derived from such data can help insurers enhance the customer insurance experience – right from providing innovative, customized insurance products/services to an easy-to-use user interface to buy those products to customized customer communications.
Improve Customer Service
The ability to deliver high-quality customer service will have a compounded effect in achieving customer loyalty and retention. Today’s customers demand 24/7 customer support and immediate gratification. With an efficient analysis of customer buying and behavioral data, insurance companies can respond to customer queries instantaneously with appropriate automated replies via chatbots. Apart from answering customer queries, chatbots can provide contextually relevant insights, educate customers on process flows, help compare policies, and suggest suitable insurance policies. The conversational chatbots are powered with data and analytics to provide personalized experiences, new product quotes, and are even programmed to address a customer’s insurance claims.
For instance, a typical auto claims experience involves the customer calling his insurer’s call center for help, waiting for a long time and being routed to several customer service reps before getting the help that he needs. Using advanced data analytics, insurers can improve this process by immediately obtaining the customer’s profile, accessing the GPS locational data and providing a personalized and frictionless experience. Such timely and efficient services make the customer feel valued and will increase the customer’s loyalty to the brand.
Implement Targeted Marketing Strategies
With the rise of digital media, various new marketing tactics such as emails, texts, social media, and apps are being leveraged to attract and engage potential customers. This targeted marketing strategy not only helps insurers stay competitive but also offers a financial incentive.
Customers usually only engage with messages or ads that are relevant to their needs and requirements. In fact, as per one research study conducted in 2019, 90% of consumers say that they don’t engage with messages that are not personally relevant to them, while around 50% are willing to switch to brands that better personalize marketing communications. Furthermore, personalized marketing improves conversion rates by 22%.
AI-powered data analytics can help insurers’ marketing efforts to be more proactive, personalized, and targeted. With data analytics, insurers can use a targeted marketing approach to increase the effectiveness of their marketing campaigns. One way to do this is by consolidating customers’ search data and analyzing their buying behaviors to send customized messages and promote suitable products to the target market in the appropriate channels to improve customer conversion rates. Insurance marketers can also leverage customer lifetime value (CLV) data to optimize their targeted marketing activities and analyze the performance of such efforts.
Assess Potential Risk
Data analytics enables insurers to further identify and assess the risk of each applicant before a policy is issued to them. Now more than ever, insurance risk managers have improved accessibility to internal and external data and analytics that allow them to conduct comprehensive risk assessments. A customer’s risk profile can now be measured for a defined timeframe by analyzing historical data collected from credit agencies, third-party vendors, and also by observing social media. Underwriters were previously relying on specific, predefined guidelines, basic statistical models like profiling and scoring models, and their intuitions for evaluating the risk of insurance policies. With the availability of predictive analytical models, underwriters can now make data-based predictions about a customer’s risk profile with higher accuracy and apply these insights to premium pricing accordingly.
Prevent Fraudulent Claims
The FBI reports that the total cost of insurance fraud is more than $40 billion per year, excluding medical insurance. The incidents of fraudulent claims have become sophisticated. Usually, the fraudsters would try to game the system with various tools and techniques. To prevent frauds and safeguard the legitimate interests of customers, it is pertinent to have an expert analysis of customer data and improve prevailing sub-optimal conditions. By leveraging predictive analytics and studying historical data and customer behaviors, data professionals can identify the likelihood of a false claim and detect patterns of suspicious activities. AI and machine learning algorithms can detect correlations and patterns of fraudulent behavior that are likely to beat human intelligence.
Fraudulent claims influence premium costs for other claimants. The FBI found that insurance fraud costs the average US family between $400 and $700 per year. Addressing fraud through the use of data analytics and minimizing fraudulent claims will save the company a significant amount in pay-outs, thereby enabling the company to extend the benefit of competitive insurance premiums to all customers.
Get Ahead with Insurance Data Analytics
Data analytics is giving insurers a way to better understand their customers and build better products and services in order to meet their needs. With the changing market demands, insurers need to make strategic data investments and have a nimble IT infrastructure to respond quickly. Doing so will allow insurers to harness each byte of data to gain deep insights and drive profitable business results.
Ready to leverage data analytics for your insurance business but need guidance on how to get started? Check out our data services page to learn how we can help you tap into the power of data analytics.