Behaviour analytics in insurance is steadily gaining ground, with the steady evolution of consumer demands and an increasing focus on more flexibility and personalisation of offerings.
User behaviour analytics is crucial for helping insurance companies meet varying and evolving requirements better while gaining invaluable insights in the process. Predominant user behaviour analytics software tools enable data analytics in a more specific form for the prediction and understanding of the habits of consumers.
Predictive analytics of consumer behaviour enables diverse use cases for insurers, ranging from customised services to taking measures to combat fraud.
Insurers usually use predictive analytics customer behaviour for gaining newer insights into consumer habits and offering more personalised services including things like recommendations, cross-selling new offerings, and lower premiums for safer drivers or healthy customers, or even healthy living tips for reducing claims in the future.
These are only a few examples of the usage of behaviour analytics in insurance.
Not for nothing has the user behaviour analytics market witnessed growth by leaps and bounds. This technology can be spread throughout the entire value chain by insurance companies and it is fast becoming a priority.
Along with smoother implementation and the right software tools, the importance of proper behavioural analytics security is also a focus point for insurance companies.
This is important since there is a huge volume of confidential data that is being gathered and analysed across segments. Hence, ensuring proper security is necessary at multiple levels.
Customers are now looking for more customised experiences with their insurers. 1/5th of insurance buyers reportedly state how their insurers do not provide any personalisation although 80% of them want the same.
This has been outlined in a DataArt report that takes information from Youbiquity Finance. At the same time, 77% of people surveyed in the report stated that they were eager to exchange behavioural information for getting customised services.
The biggest reason for leveraging behavioural analytics in insurance is that customers are now looking for more flexibility, control, transparency, and customisation according to industry experts.
They want a scenario where their insurance costs are reflective of their specific behaviours and wish to tailor their insurance plans to their lifestyles.
For instance, if a consumer is medically in prime condition, then he/she will want this aspect to be reflected in premiums for policies.
Automotive insurance has been a great hunting ground for testing behavioural analytics for many insurance companies. Telematics devices in vehicles have helped generate data which is enabling price reductions and other benefits.
Life insurance is another category where customers are looking at evolving coverage amounts and controllable tenures.
Behavioural analytics is already helping people re-evaluate their requirements on a regular basis. Insurance companies will be able to tap these analytics to identify higher-risk consumers while meeting market requirements.
Global trends indicate how 5% of patients account for almost half of spending on healthcare. Hence, predictive analytics will play a crucial role in helping insurance companies identify risk factors for patients before these cases turn problematic.
These analytics can also enable firms to evaluate the regular activities of policyholders and responses in order to judge the various risks faced by them.
This will help in the removal of activities that might otherwise lead to premium increases for policies. Insurance companies can also move towards a more advisory role that is tailored toward the interests of the consumer. These analytics may also help prevent the occurrence of claims in many cases.
Behavioural analytics has been successful with regard to reducing losses, understanding customer interactions and networks within the ecosystem, and propensity modeling.
It has also helped cross-sell various offerings along with up-selling whenever the time is ripe. It has also enabled insurance companies to swiftly offer assistance to customers at the time of claims and in other scenarios as well.
Hence, these benefits make a compelling case for the usage of user behaviour analytics by insurance firms.
Digital behaviour analytics is a specific form of data analytics that measures the user habits of consumers. It tracks consumer activity and interactions, along with their behavioural patterns in order to identify their needs, risks, and offer them more personalised solutions.
Insurers benefit from using digital behaviour analytics, since they can identify high-risk customers and instances while combating fraud and lowering claims and losses. They can also personalise their products and recommendations for consumers, giving them tailored solutions for various needs. At the same time, insurers can use these analytics to cross-sell/up-sell along with adopting an advisory role for customers.
Various types of data can be analysed through digital behaviour analytics. This includes customer interactions and activities throughout social media platforms and on the internet, along with their activity across various sites and applications. In-store, web-browsing, survey, advertising, and customer service data can also be analysed, to name a few sources.
