In the United States alone, there are close to 200 million customers of the pension/insurance industry, as per 2013 figures [Bureau of Labor Statistics, US Dept of Labor]. The numbers are increasing progressively as well. There are large amount of data collected from the million customers through various source. The sources of data are:
• Customer’s interaction with insurance companies, friends, family and on consumer forums
• Customer responses to companies marketing campaigns
• Customers interacting with the companies through Social media – online forum (especially facebook). These interactions get further comments, shares, likes etc which can be interpreted as a pointer to customer sentiment. Also, one ‘share’ from an ‘influencer’ can lead to a sharp jump in customer reach too. Uploaded photos and videos related to the product or service
• Customer tweets and re-tweets through micro-blogging revolution
• Customer queries, requested actions and grievances through a call center
The information captured across websites from the above listed sources is lying idle as an unstructured data. This information is increasing worldwide with every passing hour, and thanks to the design of social media sites. Now this unstructured data from the users across the pension/insurance industry poses a challenge.
Here is where, using BIG Data analytics can be of great importance for pension/insurance companies. This unstructured data are commonly referred as BIG Data. A short explanation on BIG Data:
• Data that runs increasingly into massive volumes- petabyte, exabyte and beyond
• Data that are exponential at very high velocity
• Structured or unstructured data
“Our time is increasingly occupied by data, and that data is increasingly trivial”–Andre Mouton
These BIG Data (and associated tools to analyze it) aides in data management by tapping into such massive sources of information and to pick up real information on how the products are being perceived, and use the information to pave the way forward.
All this unstructured information, if put together and analyzed could be of tremendous value to marketing departments of companies and product developers. It would support in generating key customer insights and understanding the sentiment of their target groups. These insights would aide in predictive analysis of the company’s sales growth and inventing more customer-oriented insurance products.