“Big data” is the buzzword heard all over the insurance industry, but how can agencies use big data to better help their customers, make more sales, and improve retention rates?
With intelligent digital management systems, insurance agencies are finally able to harness the power of data analytics in a way that directly affects their business. The question is, apart from the obvious ways insurance agencies leverage data, what other uses are there for data analytics?
Here are 3 lesser known, yet just as helpful, uses of data analytics for insurance agencies:
"Everywhere Risk Analysis" For Smarter Insurance Agency Management
Better insurance agency management often boils down to better risk assessment and field underwriting. This means coming up with better and more all-encompassing ways to calculate risks as well as securing access to more accurate data.
Traditionally, actuaries had to dive into historical data to construct their predictive models. Today however, those same models can be constructed much faster with real-time everwhere data via connected devices.
For example, policyholders are now being offered the option of driving their vehicles with a special monitoring device onboard that measures speed and other key metrics. The objective of this device is to better assess the risk of the driver and reward good driving practices.
There are also less intrusive ways of monitoring vehicles — through smartphone apps, for example.
Drivers who agree to the installation or app download aren’t guaranteed to see savings on their insurance coverage, but they may qualify for better rates because their insurance agent can formulate coverage based on a more accurate assessment of their risk. (The awareness that their driving habits are being monitored is also thought to actually improve those habits, à la the observer effect.)
Obviously, this practice may introduce a sort of reverse adverse risk selection in that people who are most likely to agree to the monitoring device are more likely to be safe drivers. But that potential sampling distortion is also largely offset by a cognitive bias called illusory superiority, here expressed in the fact that the vast majority of drivers believe themselves to be “good drivers”.
In other words, this sort of telematics research rewards and encourages safe driving while allowing insurance companies to be considerably more accurate in their risk assessment and subsequent pricing models.
Laser-Guided Prospect Targeting & Direct Customer Insight
Insurers can also pull data from things like Fitbit bracelets and other health tracking apps and wearables. This gives insurers, and potentially agencies too, never-before-available insight into their target demographic. That information can then be tied into actuarial analyses and policy pricing schemes.
Additionally, with Facebook boasting over 1 billion users worldwide, insurance agents have quite a lot of social media data at their fingertips — if only they have the will and wherewithall to leverage it. One smart place for insurance agents to begin acting on the data available to them through social networks is by identifying specific buyer personas and targeting them in a scalable way via lookalike campaigns.
Lookalike campaigns refer to a social media marketing strategy whereby “lookalike audiences”, or audiences that are highly likely to be interested in your offering (because they’re similar to your best existing customers), are identified and reached with a tailored message through largely automated social network data analytics.
Original image source: SEO Freelancer Mumbai
By investing in lookalike marketing strategies today, insurance agencies can collect campaign data that can be used to refine and intelligently iterate upon the structure of those campaigns tomorrow — improving results and informing on market identification and reach strategies well beyond the sphere of social media.
Social media can also keep insurance agencies up to date with what consumers expect from their insurance providers. Do people want online access to their policies? Do they want to speak with chatbots instead of agents? Do they want more specialized agents who can handle complex cases for them?
Direct feedback from social media — properly parsed and contextualized using data science best practices — can give insurance agencies quicker and deeper insight than even expensive market surveys.
Using AI to Craft a Smarter Human Touch Strategy
How do customers want to be contacted and how do they want to contact and interact with their insurance agency? Seems like a simple question, but it’s one with a complex answer.
A lot of data is collected through chatbots, call notes, and call recordings these days. Naturally, insurance agencies often have a hard time figuring out what to do with all that data.
So, what should be done — what’s the best practice?
Some companies are working on unique Artificial Intelligence programs that employ natural language processing. These programs can decipher the context behind the textual information in a chat message, email, or transcribed phone call. Then, the program will attempt to “understand” the intent and sentiment of the customer.
Was the customer satisfied with the call? Are they upset with the company? Do they wish they had more insurance? Better coverage? This information can then be used to craft better, smarter, sales messages to help customers get the insurance products they want and need from their agency.
Where To Go From Here
You know the benefits of leveraging data analytics to make wiser choices for your insurance business, but focusing only on the traditional techniques of doing so means you’ll miss out on all the additional data available to you.
By taking advantage of tools such as telematics, wearables, social media and artificial intelligence, insurance agencies can tap into a pool of unique data they were previously unaware of. And ultimately, in the hyper competitive insurance marketplace — where disintermediation is a serious risk to even the best agencies — making business decisions without the proper data to back you up can be highly detrimental.