What Insurers Need to Know About Behavioural Analytics

Megatrends like artificial intelligence (AI) applied to big data have opened up many opportunities for various industries. 

From making real-time recommendations for streaming services to facilitating more accurate weather forecasts, and optimising processes in manufacturing, the many different applications of these megatrends are creating disruptions across industries in various ways.

One of the essential applications of AI and big data is behavioural analytics. This article will explore how behavioural analytics has specifically impacted the insurance industry and what insurers need to know about it.

What is behavioural analytics in insurance?

Behavioural analytics involves analysing customer behaviour to understand and predict customer behaviour. This can be done by analysing their purchase history, browsing history, location data, etc. 

Insurance companies use it in several sectors, including product management, marketing, and digital advertising. The insights gained from behavioural analytics can help insurers improve customer experience and generate business growth to understand and predict customer behaviour.

How is it different from customer surveys and focus groups?

Behavioural analytics is a technique that collects data from the user's online behaviour and makes predictions about their future behaviour. Customer surveys and focus groups are techniques used to gather deeper insights from a selected group of people. 

Both provide insights that complement each other, and the data collected from customer surveys and focus groups can be used to improve the algorithms used in behavioural analytics. However, one downside of customer surveys and focus groups is that it's primarily based on the opinion or memory of those surveyed. 

For example, a question in a survey might be, "How many times have you used Brand X in the past 6 months?" The person answering this question will make a rough guess or provide the answer within a range, such as 5-10 times.

 On the other hand, behavioural analytics is much more accurate because it tracks hard data of the actual behaviour recorded. 

Another advantage that behavioural analytics has over customer surveys and focus groups is that it draws insights from volumes of raw data of people's online behaviour, from gaming to retail sites and even applications. 

While surveys and focus groups typically only focus on one area with a limited number of people, behavioural analytics has the potential to gather data from hundreds and thousands in a fraction of the time. The result is more accurate data that can be used to predict trends and make business decisions.

Creating a paradigm shift in the insurance industry

Given the advantages of behavioural analytics over traditional ways of understanding the customer, such as customer service and focus groups, the technology has created a paradigm shift for the insurance industry. 

There are many ways to use the deeper insights that behavioural analytics offers the insurance industry. One way is by using an analysis of customers' purchase history to offer discounts or new products that they might be interested in. Another way is by using predictive models for insurers which predict the probability of an event happening based on past events or similar events happening in the future.

Since it helps understand customer behaviour, these insights can be used to more accurately determine the risk a customer is likely to pose to the insurance company. It can also improve customer experience, which will ultimately lead to increased profits.

The following sections will explore how behavioural analytics has disrupted various insurance lines, from healthcare to travel insurance.

Impact on healthcare insurance

The predictive analysis resulting from behavioural analytics has become extremely important in providing a more accurate estimation of risk assessments. 

Our business analyst, Joel Sundström, said, "With 5% of all patients accounting for nearly 50% of all healthcare spending, it's more important than ever to utilise available predictive analytics solutions to identify risk factors in patients before they become problematic."

Risk is the likelihood that any given event will occur. Determining how risky a person is for health insurance is key to determining the price of the insurance and what type of coverage to provide. Each person's risk level influences their premiums, so identifying which health risks a person has is vital to offer them appropriate coverage plans.

Beyond risk assessment, data from behavioural analytics opens up the path for insurers to proactively offer new packages with lifestyle incentives to those predisposed to heart diseases, stroke, diabetes, and other chronic illnesses. 

For example, a customer might be prone to heart disease because cardiovascular issues run in the family. Behavioural analytics has also identified them to have high-risk lifestyle choices. 

Instead of leaving the situation alone as a ticking time bomb, insurers can leverage partnerships with health wearables to target this customer with an offer of lower premiums if the customer achieves certain health milestones, such as doing cardio workouts 3 times a week.

Impact on pet insurance

Pet insurance helps pet owners to cover the costs of veterinary care. According to the North American Pet Health Insurance Association, pet insurance has grown steadily by over 20% every year between 2019 and 2020—despite the pandemic, while Global Market Insights predicts pet insurance to be worth more than $10 billion by the year 2025.

This makes it prime time for insurers to enter the fast-growing pet insurance niche. However, many insurers may hold back from doing so because they're unsure about the risks involved with pet insurance—especially since pet insurance is relatively new.

Although pet insurance was first sold over 100 years ago in 1890, these policies were mainly for horses and livestock involved in business and the livelihood of farmers. The first pet insurance policy for domesticated pets—a pet dog actually—was actually sold in 1924, and by 1947, Britain started issuing pet insurance policies. 

Thus, compared to the oldest insurance policy documented in 1716 for property insurance, pet insurance is a relative newcomer that is over 300 years behind. Without a lot of historical data in this niche industry, behavioural analytics plays a more important role in helping insurers identify trends and risk factors for pet insurance.

Impact on dental insurance

With the rising dental care costs, many consumers are turning to private dental insurance, also known as a dental plan. 

In 2019, the global market for dental insurance was valued at $152.26 billion, and it's projected to reach $237.11 billion by 2027, according to Allied Market Research. This makes dental insurance a very profitable niche for insurers to target.

Much like health insurance, dental insurance relies on accurate analysis of risk assessments, whereby the risks can be predicted with confident lifestyle choices, such as diet and oral hygiene.

Insurers looking to offer dental plans can use behavioural analytics to more accurately predict risks that can impact the insurance plan and premiums offered. For example, smokers or tobacco users face significantly higher risks of stained teeth, gum disease, and oral cancer. 

Like health insurance, insurers can use behavioural analytics data to be proactive with high-risk customers. For instance, an insurer can offer incentives such as discounts, vouchers, or lower premiums to customers who go through and complete a program to quit smoking. 

Impact on travel insurance

We're all familiar with how the pandemic affected travel and, in tandem, travel insurance. However, factors like the pandemic are external forces that can't be predicted. So what can insurers predict with behavioural analytics when it comes to travel insurance?

One application is matching purchasing trends with life milestones. For example, someone who has just graduated from high school or college may be more likely to travel for their gap year. Or someone who is planning for a wedding will also be preparing for their honeymoon. 

In this case, behavioural analytics isn't used for risk assessment, such as how it can be applied in health or dental insurance. Instead, it's used to predict the probability of travel, allowing insurers to target their policies.

Behavioural analytics in travel insurance will often go hand-in-hand with health insurance. For example, health coverage for travel to certain countries could include coverage for diseases that have been identified as a higher risk for that region. Insurers could also include additional benefits or lower premiums for those vaccinated against these identified risks, where applicable, before travelling. 

The shift from reactive to proactive

Our business analyst, Kristian Greenway, stated that analysing a customer's normal behaviour can provide insurers with a better estimate of risks in order to help assess and adapt the products and policies offered.

In essence, this is a paradigm shift from a reactive to a proactive approach in insurance to offer better customer service, improve the value chain, and gain a competitive advantage.

Greenway further expands by saying, "With the help of behavioural analytics, predicting and being proactive for [every] customer is possible to a much larger degree than before. The insurance companies can work proactively towards the customer, mitigating risks and advising the customer [regarding] each explicit kind of insurance instead of the more general advice that are currently market-standard." 

Gain a competitive edge with behavioural analytics

Insurers can leverage behavioural analytics for risk assessment in various types of insurance lines, as well as use it to apply predictive analysis for purchasing trends, such as in travel insurance.

With this data, insurers can shift from a reactive to a proactive approach to offer more competitive packages, create more diverse coverage, increase their market share, avoid unnecessary costs, and reduce liability.

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