Traditionally, the insurance industry relied on historical data to assess the likelihood of potential risks occurring in the future. Now, as digitization and the usage of digital technologies increase across all business processes, data is being collected at every touchpoint of the customer journey.
Today’s insurers have access to an enormous volume of data, including real-time data, that can be used to supplement historical data. When paired, the two forms of information can give them a deeper understanding of the customer experience and potential risks. Applying analytics to this data is helping insurers get the insights they need to personalize products and services, improve operations, make faster and more strategic business decisions, and drive more value across the insurance value chain.
Let’s explore the top five use cases of insurance data analytics:
1. Design products based on customer behavior
As organizations look to grow their business in the digital age, putting customers first is no longer a differentiator, but an industry must-have. With the volume of customer data readily available – either through public domains or collected from points of interactions with the customer – companies are leveraging this data to understand the customers’ needs and preferences.
Data science is utilized to synthesize a large amount of data and generate actionable insights. For example, Natural Language Processing (also known as text analytics) can build data engines that go through multiple social media platforms and review websites. After collecting information from various channels and touchpoints, these tools can develop their own digital content to study and comprehend what an insurer’s customers are talking about in real time.
This insight can then be applied to create innovative products and services, develop an easy-to-use interface for customer-facing processes, or provide customized communications – all from a customer-first perspective.
2. Develop an enhanced customer experience
Outside of providing personalized products and services, insurers are also increasingly expected to be able to deliver high-quality customer service.
Just some of the leading factors in determining when a service experience was “good” include a fast response time, personalized recommendations, and cross-channel consistency.
As consumers continue to conduct the majority of their interactions with brands via digital channels, they’ve come to expect 24/7 customer support. With an efficient analysis of customer buying and behavioral data, insurance companies can respond to customer queries instantaneously using chatbots. Apart from answering customer queries, chatbots can provide contextually relevant insights, educate customers on process flows, help compare policies, and suggest suitable insurance coverages. Conversational chatbots are powered by data analytics to provide personalized experiences, new product quotes, and can even be programmed to address a customer’s insurance claims.
For example, a typical auto claims experience requires a customer to call their insurer’s call center for assistance. This process can often result in a long wait time as the caller is bounced through several service reps before eventually getting the support needed. Instead, using advanced data analytics, insurance providers can improve this process by immediately obtaining the customer’s profile, accessing the GPS locational data, and providing a personalized — and frictionless — claims experience.
Timely and efficient services will help customers feel valued, thereby increasing long-term brand loyalty.
3. Generate actionable insights for targeted marketing
With the rise of digital media, various new marketing tactics such as emails, texts, social media, and in-app engagements are being used to attract and engage both existing and potential customers. This targeted strategy not only helps insurers stay competitive, but can also offer a financial incentive.
Some benefits that can be unlocked by targeted marketing efforts include:
- Longer-lasting customer relationships: Customers are far less likely to switch brands if you continuously provide them with relevant and valuable information.
- A lower cost to acquire new customers: When ads are tailored specifically to the consumer, the length of time it takes to convert a potential customer is reduced.
- Higher engagement levels: By offering innately valuable messaging to the target audience, insurance providers will be able to connect more effectively with both existing and potential customers.
Advanced analytics can help insurers streamline their marketing efforts and ensure that the messaging is more proactive, personalized, and targeted. One way to achieve this is by consolidating customers’ search data. By organizing this massive volume of information, it becomes easier to analyze buying behaviors, send customized messages, and promote suitable products to the target market in the appropriate channels. Insurance marketers can also leverage customer lifetime value (CLV) data to segment their existing customer base and better understand the real-time performance of campaigns.
By applying data science, insurers will be able to use a more targeted approach, increasing the effectiveness of their marketing campaigns while also creating a more positive perception of their brand.
4. Improve your risk management strategy
Another powerful application of insurance data analytics is the ability to perform an optimized risk assessment of each application before a policy is ever issued.
Now more than ever, insurance risk managers have improved access to internal and external data as well as analytics that allow them to conduct comprehensive risk assessments. For example, 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.
Previously, underwriters were relying on specific, predefined guidelines, basic statistical models such as 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-driven predictions about a customer’s risk profile with higher accuracy and apply these insights to premium pricing accordingly.
5. Minimize the risk of insurance fraud
Any organization that exchanges money with customers, service providers, or vendors has to consider its risk of exposure to fraud and abuse. However, due to the massive size of the insurance market, there is a significantly higher rate of fraud compared to other industries. In fact, estimates from the FBI put the total cost of insurance fraud at more than $40 billion per year. This impact isn’t only felt by insurers, however; the average U.S. family can expect to pay between $400 and $700 per year in the form of increased premiums.
To better prevent fraud while also safeguarding the interests of legitimate customers, insurers can leverage data science and predictive modeling tools to analyze real-time customer data with a higher degree of accuracy.
By using predictive analytics and studying historical data and customer behaviors, data professionals can identify the likelihood of a false insurance claim and detect patterns of suspicious behaviors, automatically triggering the appropriate response based on predefined conditions. Compared to traditional human-led fraud detection, AI and machine learning algorithms can uncover correlations and patterns of fraudulent behavior more quickly and with less of a chance for error.
Addressing fraud through the use of data analytics and minimizing fraudulent claims can save an insurer a significant amount in pay-outs, enabling the company to extend the benefit of competitive insurance premiums to all customers.
The future of insurance
Data analytics is empowering modern insurance professionals, giving them the business intelligence needed to understand their customers and build better products and services in order to meet customer needs. As market demands continue to evolve, business leaders will need to make strategic data investments and have a nimble IT infrastructure to respond quickly. Doing so will allow insurers to harness each and every byte of data to gain deeper insights and drive profitable business results.
Ready to leverage data analytics for your insurance company 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.