By Kenneth Koh, Head of Industry Consulting, SAS Asia Pacific
Insurance customers and prospects alike are becoming increasingly digitally savvy and demanding in their expectations. Coupled with the surge of customer touchpoints available in today’s integrated marketing environment, this has led in recent years to drastic changes in the realm of insurance selling and buying.
On top of this, the global pandemic has upended all industries, including insurance, making it more vital than ever that players in the insurance space constantly innovate in order to adapt to shifting customer demands and ongoing social and environmental disruptions. The Asia-Pacific region, in many ways, holds the key to the insurance industry’s future, given its population and economic clout. Consumers in the region have high expectations for seamless and personalised digital experiences – inspiring innovation among the industry leaders.
Thus, personalisation is simply not enough anymore when it comes to marketing insurance products to potential and existing buyers — insurance providers will now have to implement hyper-personalisation in their customer offerings to stay ahead of the curve.
IDC predicted last year that 15 percent of customer experience applications will deliver hyper personalisation, through reinforcement learning algorithms continuously trained on a wide range of data and innovations. Furthermore, according to EY, hyper personalisation is among the factors that will reshape Southeast Asia’s financial services ecosystem in 2021.
With the emergence of big data, artificial intelligence and machine learning enabling the process of hyper-personalisation, the potential benefits are vast. One example is the exponential increase of customer satisfaction, as their needs are anticipated with services and solutions tailored to their specific lifecycle. This all begins with predictive modelling. Predictive modelling is defined as the collection and analysis of data from various internal and external sources, which serves to better understand and predict the behaviour of customers in a short amount of time. It can help insurance providers understand whether potential.
customers are interested in buying their products and services, reduce issues and underwriting expenses, keep a strong hold on customer relationships and claims, while increasing sales and profitability at the same time.
The pandemic has further shown insurance providers the importance of predictive modelling, allowing them to anticipate unprecedented changes and define rate changes and new products more efficiently. Without it, insurers miss out on inconspicuous warning signs, which will further result in the loss of cost and time when remedying. Predictive modelling serves as a huge and vital driver behind the capabilities of hyper personalisation.
With the help of predictive modelling, insurance providers can implement hyper personalisation. This helps them to identify potential target markets, understand the customer demographics and characteristics, and tailor their efforts towards the right direction. Through aggregating complex data from multiple customer touchpoints, insurers gain a holistic, 360-degree view of individual customers, enabling a target engagement at the “moment of truth” — where a specific message is delivered to them at the right place, and at the right time.
Data collected from hyper personalisation is largely first-hand information, making results more direct and the insights more valuable. For example, hyper personalisation can help insurers identify if a customer is at risk of cancellation or if they require more specialised attention, allowing the insurer more time to remedy issues and meet the customer precisely at their moment of truth. Hyper personalisation can also help in identifying and reducing the risk of fraud before it happens, implementing corrective measures at breakneck real-time speed. This all contributes to a loyal and passionate customer base.
Hyper personalisation is not, however, beneficial only to insurance sellers. Customers also reap its perks, as they are offered products and services customised to their personal circumstances. In insurance, that means an improved price and risk selection and the prioritisation of pressing claims, which all serve to save customers’ time and money. Behavioural insights and individual user history are all extracted and collated through advanced data analytics to anticipate needs and tailor services accordingly.
All of this has been strengthened and proven possible by advanced data analytics software and solutions. A few things factor into its progressive reliability — the vast surge of customer touchpoints, from social media to smart devices, as well as organic interactions between insurance specialists and customers, allows the total aggregation of all information collected into a single, comprehensive space of valuable and individualised customer insights.
Utilising the vast amount of information and data from their clients, insurers can personalise the engagement at each visit to their digital properties. The analytics and AI engine looks at each individual client when they log in and decides what messages would be of value to that individual, based on what the insurer knows about them, and then prioritises only the most important messages for the client to see during that session.
Furthermore, the ability of advanced data analytics to comb expeditiously through Internet-of-Things (IoT) enabled data allows the understanding as well as anticipation of the needs and desires of customers. This is further complemented by its ability to analyse customer behavioural data in real time. For example, an insurance provider can offer health insurance to a regular buyer of medication based on outcomes of a predictive model, which indicates the person or their loved one may be suffering from a particular condition.
The objective is to become more proactive in engaging with customers, to improve their awareness of what is available to them and, in a sense, nudge them to act in their own best interest — generating positive outcomes for themselves and their families.
There is tremendous benefit to be reaped from hyper personalisation, while at the same time still so much to learn from it. Through continuous adaptation and deep analysis of the troves of data readily available, insurance providers will be able to innovate ahead of the curve, providing their customers and buyers with the best possible brand experience. When the messaging and services are hyper personalised to their liking, the rewards sown, and the customer loyalty gained is immeasurable. They would be remiss to miss out on this innovation.