Big Data and Analytics in the service of customer centricity

Among the Advanced Analytics technologies that are emerging in the insurance industry, Big Data analytics are the ones receiving the most attention and study from the market with a view to value creation and centralization of customer needs, now a prerogative of the environment in which Companies operate.
We can define analytical and predictive analytics as the practice of extracting information from data collected on past events in order to identify recurring patterns that allow predictions to be made about future events and trends.
The main drivers that have led to an increasing interest in Advanced Analytics issues in recent years can be identified in an ever-increasing availability of data and a growing interest in wanting to exploit an ever-evolving wealth of information, an availability of computers and networks that are evolving in terms of performance, the release of more intuitive and user-friendly software that does not require an effort in terms of programming, an increasing focus in issues of in-depth study and study of increasingly high-performance and intuitive modeling, and last but not least, an increasing focus on competitive differentiation driven by difficult economic conditions.
To understand the state of diffusion of Big Data and Analytics tools in Italian companies, CeTIF analyzed business plans, websites and news of 36 insurance groups and companies operating in Italy. The objective was to analyze and identify the main Advanced Analytics projects initiated and those completed. In particular, it was found that 35 percent of the companies analyzed have initiated projects related to the implementation of these technologies, an indication of the growing interest in pursuing greater customer centricity.
For example, it is noted that combining coverage with IoT devices can be an opportunity in terms of customer loyalty and offering integrated services that generate high added value.
Recall in this sense that we are witnessing an evolution on the ways of first contact with potential new policyholders, often from the autonomous purchase of an IoT device comes an insurance need. A classic example in this sense is the purchase of a wearable device by a sports person who might receive an accurate notification or proposal for a health policy targeted with respect to the time or physical activity performed.
Companies are challenged to develop analytical engines capable of intercepting from the moment of truth to the insured's churn through Big Data analytics that, leveraging supervised and unsupervised algorithms, can predictively identify customers' life cycle moments and accurately segment their needs by ensuring customer centricity services.
In conclusion, Big Data analytics algorithms are, arguably, a disruptive technology serving Companies. The real challenge will be to be able to seize all the potential that such innovations can generate, not forgetting that, from the drivers outlined above, at the heart of any business decision-making is to remember that customer needs and requirements are probably the only operational levers on which to push.