By Neer Rama, Force Solutions Product Manager at thryve
Insurance loves data. It uses data to determine risk and carefully balance its models for maximum effect. This data culture is the key to overcoming a big problem for the industry: its lack of customer-centricity. Yet that is easier said than done, something that this article articulates nicely:
“In smoothing the customer journey, insurers must make a complicated financial product grounded in actuarial science easy to sign up for, understand and use. They must capture and sustain customer attention, gather critical customer data to calculate risk profiles and premiums, propose attractive and personalized insurance quotes, and make it easier for customers to file claims and receive payment.”
We don’t lack data in the insurance space. We lack the ability to create meaningful insights and feed them downstream. These aren’t my words, but ones I lifted from this webinar. It’s an insightful compilation of views from a discussion by several top insurance executives. But the sentiment is one that my colleagues and I at thryve have been putting forward for some time.
Data analytics is often put forward as the solution to creating customer insight. After all, insurers have data, but that data is not being used most effectively. Still, what qualifies such a statement? Why is data analytics important if you want to create a customer-centric culture?
I’d like to highlight some of those points:
Data-driven businesses are proactive
Insurance tries to be proactive, to anticipate problems before they arrive. The more traditional ways of using data fit with actuarial practices that can detect certain risks. But these practices don’t create flexibility across the organization and particularly for customers. They don’t make anyone more flexible to manage sudden changes and anticipate new risks. In some quarters, such flexibility is referred to as shifting from insurers, who react once an incident occurs, to ‘ensurers’, who protect customers during incidents or even help prevent them. That fitness tracker linked to your healthcare provider is an example of flexible ‘ensurance.’
Pooled data is a competitive edge
According to this Aberdeen study, companies with data lakes outperform similar companies by 9% in organic revenue growth. A data lake is a central pool of raw data, from which information is drawn and analyzed. But there are many ways data can be aggregated or federated. What is important is that data kept in silos isn’t adequate and pretty much useless for robust analytics. In contrast, a data environment that supports analytics also embraces the concept of data pooling, and pursues a single view of that data to reduce confusion and contradictory information.
Data analytics leads to personalization
Have you ever received a piece of correspondence from your bank that tries to be personal, yet is just a template? Worse: how often does it offer something you already have? How can you spend so much on a company, yet they still can’t tell you apart from other customers? That’s due to a lack of good analytics. An analytics engine powered by good data, a strong model, and clear output goals can create a rich customer profile. That profile can then help generate correspondence that genuinely relates to them. It’s just good practice: a successful salesperson will know the name of their best client’s dog. Your correspondence can too, if you use data analytics to build those profiles.
More than customer-centricity
A customer-centric culture reflects where you can improve other areas in your business, specifically how systems can be enhanced to provide more support for your workforce. An example of this is process automation, which can speed up tasks, free staff time and enable self-service features for customers. Data analytics environments also give extra capabilities to actuaries and data scientists, and enable you to do things such as fraud analytics. Once you have systems and processes that support the spirit of customer-centricity, you open the way to many other essential capabilities. You don’t even need to start with the explicitly customer-related areas. You might want to automate inspections or give more flexibility to your risk prediction. But they all drink from the same pool dug and filled by a customer-centric strategy.
Here’s the sales pitch. At thryve, we specialize in establishing and deploying these culture-based systems. We leverage modern products as Salesforce, Tableau, Riskonnect and Flowgear, alongside our business experience and understanding of your requirements. These products are very potent: they are flexible, they natively aggregate data, and they pack in many different services to serve up analytics and support different business lines. They can start small as a proof of concept, focused on a specific need, and scale out as they prove their worth.
Customer-centricity in insurance is crucial and a competitive watershed. Though it’s not a culture that develops overnight, it will have a profound effect on any company that adopts the practice. Data analytics creates that effect and is the secret to establishing such a culture. You are not gaining anything by not trying it. Rather, as Jeremy Trott from Allianz said in the webinar:
“Customer expectations are growing exponentially around how quickly we can deliver, particularly in the claims space. Whether you’ve had a fridge freezer break down or need repairing, you can replace that. And if you can go on Amazon and order it in 24 hours, but the claims journey takes four weeks, that isn’t good enough.”