How capitalizing on your customer data can turn ‘big data’ into gold

Craig Sharp

Commenters and pundits have been billing big data as the Next Big Thing for Insurance for a couple of years now, but just how can big data assist insurance firms and promote business growth? Well it isn’t enough just to hold terabytes of customer data, the key is in how you analyse that information and make use of it.

Capitalizing on your customer data will ensure you’re able to compete in today’s digital, data-driven world, so invest in decent infrastructure and analytics software and you’ll be able to…

It's not just about quality of data, it's about quality of execution

… Cross-sell & up-sell much more effectively

Imagine a platform that can scan customer purchasing and social media activities, actively searching for key indicators such as posts on a holiday destination, or the purchase of flight tickets, before transmitting that information back to you. The technology and the potential are there, you just need the right infrastructure.

Using multiple data points across multiple industries (retail, health, finance etc), big data analytics makes it possible to proactively offer one-time insurance (OTI) for items such as mobile phones, holidays and day trips to your customers.

Similarly, your customer may be starting a family or may have just brought a new pet into the home – do they have life or pet insurance? Big data analytics can provide you with this information, making full use of it is up to you.

… Improve customer retention

Insurance today is a far more competitive industry than in years past. Whether you’re a business or a private customer, it’s incredibly easy to log on, check one of any number of websites and find the best deal for you – cutting out any sales conversation entirely. Very often, insurance companies will not hear that their customers are leaving until it’s too late.

Enter predictive analytics. Through predictive analytics, and once again utilizing numerous data points, you’re able to predict when a customer may be having difficulty with a product or service, and act accordingly to improve that customer relationship. The advantage that predictive analytics provides here is that your company will be fully aware of the potential pain points the customer may be experiencing, meaning that before your call or communication with them, you can shortlist properly considered options and resolutions for that customer.

An extension of this is the ability to mark personalized offers to the customer. With data such as age, sex, location and social data from platforms such as Facebook and Twitter, you’re able to send through vouchers or other offers for local businesses, to keep your customer’s loyalty.

… Better determine customer risk and fraud

Risk assessment is incredibly data-intensive, making it a ripe environment for data analytics to gain a foothold. Again dependant on reliable data points, information that looks beyond the usual can help insurers to properly assess a risk, providing a more aggressive package and thus mitigating any risk exposure.

In the same way that data will tell you if your customer has booked a holiday or bought a new pet, that same data can be used to assess financial situation, political affiliations, claims and billing history and a number of other data points that provide a much broader picture of your potential customer.

As well as risk assessment, big data analytics can also be used to combat fraud. Fraud is one of the biggest of big ticket items for the insurance industry today – with some reports claiming it cost the industry £2.1 billion in 2013.

Because big data analytics allows you to monitor claims in real time, organised fraud (such as car accident claims amongst a circle of friends or colleagues) is far easier to spot. Again, social media also plays its part. For example look at house insurance – if a customer is claiming for fire damage, while attempting to sell white goods online, the information is much easier to pick up on and flag if an analytics system is joining those dots for you.

Combine these with the same predictive analytics models mentioned earlier, and analytics profiles fed with the right data can be a powerful tool indeed in the fight against fraud.

… Gain a clearer picture of customer lifetime value

No two customers are the same, so why price the same when considering their businesses? Those companies who look at customer lifetime value (CLV) have been shown to be far more profitable than those that don’t. And big data analytics makes this task far easier than it has ever been.

By aggregating historical data including medical background, claims history, employment history and financial history to name a few, your company can more accurately predict the true value of the customer over the lifetime of their policy and thus price competitively to ensure you win that business.

In short, big data is just the beginning of the process. Information is entirely valueless unless it’s analysed and used effectively. This is where analytics, data scientists and a sound strategy come into play – only with these fundamentals will you realize the true value of this ‘big data’ stuff that everyone is talking about.