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Personalization and the future of the insurance market

Mobile devices have accustomed us to doing things quickly and easily. If we can buy virtually any product or open a bank account at any time without leaving home, why not handle insurance in the same way?

The banking sector has proved that traditional companies can succeed in adapting to the digital reality. Data science and machine learning solutions process vast amounts of client data, allowing businesses to offer personalized services and boost sales while also slashing the cost of winning new customers.

What does the customer want?

Modern customers want to feel that an offer has been designed to match them personally. An increasing number of people expect offers to be closely customized to fit their lifestyles. Companies must guarantee access to solutions that meet users’ needs.

Personalization on this scale is a tall order, but insurers have never before had such extensive knowledge of their customers. Companies can now answer questions like:

  • Where and when do they buy?
  • How much are they ready to spend?
  • What contact method do they prefer?

These and other answers are within easy reach, thanks to data processing. This allows us to gradually replace the standard service portfolio with tailor-made offers. And as customer needs in the area of insurance vary immensely, this is a tremendous advantage.

Let’s look at an area where personalization is already impacting the insurance market:

Data-driven, behavior-based pricing

Technologies like the Internet of Things (IoT) present insurers with an opportunity to create much more personalized price lists. Instead of classifying clients using less accurate models, which assign them to particular groups, customers can get offers and incentives based on their own behaviors.

A standard behavior-based pricing technique is having customers use wearable devices that monitor vital signs and record healthy activities. By following a salutary lifestyle, customers gain access to cheaper covers. At the same time, this equipment provides support for health-related diagnostics, allowing the faster detection of disease symptoms and helping cut the cost of treatment.

Similarly, sensors may check if patients adhere to their doctor's recommendations or follow rehab instructions. They often generate extra savings on the insurer’s side, as customers recover faster and generally stay in better shape. Customers who lead a healthy life and avoid risky situations can also benefit financially, e.g. when offered a discount.

For home and property insurers, smart home technologies can offer similar advantages, such as simplifying claim settlement procedures by automating the more obvious cases.

Using personalization for price optimization can boost insurers’ profitability ratios for individual clients as well as help them more accurately forecast future company income. Insurers can more precisely determine customer price sensitivity and its long-term impact on customer loyalty.

Personalization = Competitive advantage

The efficient implementation of personalized solutions is hard to copy. Thus, it becomes a source of competitive advantage, particularly in a market that is still quite conservative.

Today's customers are bombarded with excessive advertising, which makes cutting through the buzz a real challenge. Targeted campaigns, focused on particular users and their needs, are becoming more relevant. Data science makes it possible to classify clients according to certain qualities, such as:

  • Financial potential,
  • Age, 
  • Purchase history,
  • Location.

Personalized offers can be customized according to what’s most import to the customer. This increases the odds of matching the right offer to the right person at exactly the right time.

Meeting the self-service challenge

Personalization starts with establishing what journey generates the most value for the customer. For many people, being able to conduct the entire purchasing process on their own is important. Due to its nature, though, buying insurance products implies acquiring a lot of information.

How machine learning benefits the insurance industry

For the insurer

  • Fewer intermediaries
  • Partial automation and shorter processes
  • Lower unit cost for winning new clients
  • Availability of new customer groups
  • Price optimization based on more and better data
  • Speeding diagnosis and cutting customer treatment costs
  • Detection of potential threats
  • Unique competitive edge
  • More accurate customer categories
  • More effective marketing campaigns

For the customer

  • Offers tailored to individual needs and lifestyles
  • Better prices after meeting specific conditions (healthy lifestyle, regular medical checkups, etc.)
  • Wider range of insurance products
  • Shorter service delivery times
  • Self-service options

For many clients, a wide and varied offer range is an obstacle to making a purchasing decision. Convenient self-service portals require good information management skills, even if service providers are bound by law to publish thorough information.

To overcome this challenge, it is vital to highlight elements that are crucial to the selecting and purchasing process. Also, emphasize the aspects of greatest value or interest to the customer. The financial sector has shown us that designing a friendly digitalized customer journey for very complex products is doable.

Another significant element is moving seamlessly between channels. Thus, designing a multichannel experience that accounts for various touchpoints, from social media to virtual assistants, is of considerable relevance for insurers.

Using the power of data

This approach requires collecting and processing immense amounts of data. Machine Learning solutions are already speeding up the application process. They’re also proving very useful in the early identification of potential risks.

This technology’s role will only grow in the upcoming years – even more so because there will be more data sources to manage. Currently, nothing prevents companies from obtaining data from cookie files, user location, social media, and some IoT devices.

One of the first solutions of this type in Poland is ERGO Hestia’s use of the Yanosik application, which informs drivers of current road and traffic conditions. On the insurers’ side, this app collects precious information about potential clients' driving styles. By applying behavioral analysis and advanced data interpretation techniques, insurers understand their clients’ real-life actions behind the wheel and better tailor offers to each one’s needs.

The same mechanism is responsible for setting premiums at a level that’s advantageous to the insurer and acceptable to the client. The appropriate use of data replaces reactive models with more advanced, proactive systems.

The marriage of insurance and tech

Being well aware that technology can enhance their operations, many insurers monitor innovations likely to be useful to their organizations. This frequently leads to cooperation with companies experienced in launching new solutions that combine technological aspects with innovative business models, e.g. microinsurance.

An insurance player that has been using this approach is the Trōv platform. It offers microinsurance services to users, who can take out coverage against the damage, loss, or theft of their valuable objects. Clients only need a smartphone and an app to insure their laptops, guitars, or bicycles.

Personalization and beyond: a data-driven future for insurers

The rise of technology has completely altered customer expectations, triggering major changes in many industries. This also applies to the insurance market, where the current situation resembles the financial industry a couple of years ago. For the last few years, financial companies have become leaders in adopting new solutions, winning new client segments, and opening up to new forms of customer relations.

How polish insurer WARTA is using artificial intelligence

The Polish insurer WARTA has been running two pilot schemes based on AI: a virtual adjuster and a call center consultant. The virtual adjuster uses image recognition to estimate the cost of repairs; so far, it’s been used on over 500 vehicles. The digital consultant has conducted 2,000 conversations with vehicle accident victims, collecting basic data (the number of the insurance cover and the circumstances of the crash) and passing it to relevant employees for further handling.

Using already-existing immense data pools enables insurers to create more engaging and personalized offers. Combined with process automation, this leads to shorter service delivery times and optimized costs. And although this technological arms race may be viewed by many companies as a complication, in reality the implemented changes may result in many benefits.

Organizations that rely on Cloud computing for their services can greatly reduce the costs of building and maintaining the necessary infrastructure. Automation and supporting customers’ trend toward self-service provide an opportunity to reduce the number of intermediaries a company must use. These and other cases prove that implementing technology is a chance to design new business models and streamline company processes.