Commercial data is at the heart of decision-making in consumer-facing businesses, and the insurance sector is no exception. Leading insurance service providers have been using commercial data analytics to analyze the risks involved in insuring people and assets for years now. However, recent advancements in technology have enhanced the computing power and scope of commercial analytics, helping businesses to build and deploy robust solutions that play a crucial role in improving the customer experience through segmentation, advanced risk assessments, claims analysis, and insight generation.
Collaborations with insurance clients from across the globe have helped us identify three key areas that play a crucial role in transforming the way insurance service providers use commercial insurance data analytics:
At Quantzig, we’ve designed and developed an insight-driven organization framework that helps insurance service providers to develop and organize their analytics capabilities to make the most of the available data sets. Request a FREE proposal to learn more.
Integrate data and insights
In today’s complex business environment, the effective use of commercial analytics relies on the ability of an insurer to gather and integrate commercial data from disparate sources. The need to classify and prioritize information sources is also turning out to be a focus area since data that is relevant to decision-making is limited and difficult to analyze.
Scale analysis and insight generation
To make the most of your commercial analytics investments it’s crucial to take necessary actions to ensure continuous insight generation across the organization. A one-off analysis or an ad-hoc project can be useful. But to drive maximum value, you must create a reliable approach to scale analysis and insight generation.
Turn Insights into Actions
Turning insights into real-world commercial decisions require a lot of skill and commercial analytics expertise. One way to go about is to build a unified commercial data analytics team to tackle common business issues that involve managing revenue growth, developing productive marketing strategies, and analyzing datasets to drive more value and growth.
Our commercial analytics solutions can help you integrate data from disparate sources into a single analytics platform. Get in touch with our experts to learn more.
Commercial Analytics in Insurance: Turning Data into Insights
Web Crawling
A wealth of information exists both inside and outside the organization – the website is a major source. Web crawling can help insurance companies enhance risk assessments by offering granular insights and current information that is typically used today. Insights such as these can help inform decision-making by offering information on the likelihood of losses from relevant perils at the property location, the density of existing policies, and other risks.
Data Collection & Integration
Developments in data storage, data pre-processing, NLP, and machine learning have paved the way for new opportunities to drive better business outcomes. The insurance industry, being a data-heavy one, must capitalize on new opportunities and learn to collect, integrate, and process web traffic data quickly to gain a competitive edge.
Web Traffic Analysis & Forecasting
The insurance industry has been at the forefront of commercial data analytics and forecasting for quite some time. This is, in no way surprising given the amount of data generated and the meaningful insights that can be gleaned from them. Advanced commercial data analytics and forecasting can help reveal the much-needed information from the raw datasets, which can be refined further to reveal meaningful insights that drive decision-making in the insurance sector.
Request a FREE demo to explore our commercial insurance data analytics dashboards.
The abundance of web traffic data available to insurers may not be new, but with the technological advancements and the development of high-performance computing tools insurers now have unprecedented visibility and ability to access data throughout their organizations. As a result, more data can now be generated, stored, pre-processed, and utilized to improve processes and enhance outcomes.