Big data and analytics for insurance: Make it personal
Every day we continue the conversation around how insurers can use big data and analytics to personalize each and every customer interaction.
First and foremost, policyholders deserve to feel that their insurance company understands them and is approaching them with only relevant offers. Gone are the days when products were created for groups of people that all fit a similar demographic profile. Insurers are now customizing offers for each individual insured and evaluating risk in the same manner.
Doing the right thing at the right time is critical. Insurers have to get it right the first time because there aren’t as many one-on-one interaction opportunities with policyholders as there are in other industries, such as retail or a telecommunications. This individual customization isn't just focused on new products or cross- and up-sell opportunities, but it is rooted in optimizing operations, perhaps by adding a chief data officer to the team. Can the call center know that the person on the phone had been looking at adding new coverage on the internet just a few minutes before calling? Can you settle claims quickly with little risk for overpayment or fraud?
Improving retention is often a matter of optimizing these back-office operations. Mashing up data sources like never before can uncover correlations that insurers never knew to look at before embracing big data and analytics. Sometimes this journey starts with notes found in the policy file, or maybe using the voice recordings from a phone conversation. It may be that your insurer wants to learn about policyholders from public records, geospatial analysis or machine generated data, asking questions such as: What if your customer is a commercial insurer? Are they using telematics and sensors to manage risk?
Nationwide's chief data officer Wes Hunt discussed how they tackled improving their customer experience in partnership with IBM at Information On Demand 2013. Watch his mainstage presentation (at the 30 minute mark) and Q&A on theCUBE following the presentation.
Now ask yourself: What is your customer's transformation model? Do they measure customer lifetime value? Are they leveraging data and information across the enterprise to turn what they know into value?
Learn how IBM can help transform your organization with big data and analytics and read about a few of the ways that IBM can use big data and analytics in insurance.