Why analytics alone can’t deliver customer-centric insurance

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 Why analytics alone can’t deliver customer-centric insurance

Insurers are required to move beyond the analytical experimentation and data silos that typically exist within their business today. So how high will the leading insurers set the bar?

Enterprisewide analytics and AI have the power to prepare insurers for a customer-centric future and transform the relationship with policyholders. But will only a handful realise that their current approach to analytics leaves them poorly prepared for the challenge?

As I noted in my previous post, success in the new era of insurance will demand that insurers transform to become customer-centric and better understand the needs of the individuals and businesses behind the policies. This requires insurers to move beyond the analytical experimentation and data silos that typically exist within their business today to a more mature position that allows them to compete with those born from digital. So how high will the leading insurers set the bar? Let’s consider the near-term trends and opportunities presented by the Internet of Things to personal and commercial line insurers.

In the connected home, there are a plethora of options from Apple, Google and Amazon for virtual assistants that can orchestrate a broad range of other connected home devices. These devices capture a wealth of data on the home and the behaviour of the individuals and families that live there. It’s no surprise that retailers like Best Buy in the US are acquiring businesses that complement their position as one of the biggest retailers of connected devices.

For health and life insurers, the innovation supported by the growing sophistication of wearable technology presents a depth of insight that has never been possible before. For commercial lines, fleets of vehicles and factories full of machinery are providing streams of data that can support predictive maintenance strategies. GE is already recognising the need to build ecosystems ready for the future needs of its customers.

All of this innovation presents a significant opportunity for new products and services that detect patterns and predict events before they happen. Machine parts are replaced before failure, preventing downtime and lost revenue. Diabetics can better track their blood sugar levels, providing real-time data to their GP, allowing for personalised treatment plans and avoiding hospital visits. Devices across the home provide peace of mind and protection against water damage, break-ins and fires. Interesting moves are also being made by organisations outside of insurance. US retailer Best Buy’s recent acquisition of connected health company GreatCall could position it well to influence the health and life insurance industry.

Those insurers setting the standard in the industry are already taking the early steps to find their place among these new connected ecosystems and partnerships.

How to become a customer-centric insurer

The immediate focus for many insurers is aligned to the challenges presented by their industry and legacy technology. An ambitious goal may be to break the walls that exist between product teams and operational departments, trying desperately to bring together analytics silos that exist across the business and manifest as multiple interpretations of customers: their needs, behaviours and value.

While this should remain a focus for these organisations, they should also consider what lies ahead. To remain relevant in the new connected lives of their customers, insurers should consider today how they adapt to the demands of the volumes of data they will face in the future. Partnerships with digital natives and those who operate in the on-demand world will be needed, and they will expect insurers to be able to align with their operating models.

Insurers should consider today how they adapt to the demands of the volumes of data they will face in the future. #CustomerCentricity #Insurance Click To Tweet

So how do insurers arrive at a place where they’re able to offer that kind of proactive service to customers? Analytics plays a key role, but it will need to be delivered in a more efficient operating model than is used today. Insurers need to industrialise every aspect of the analytics life cycle, systematically interrogating data, building new analytical models and then rapidly embedding those models into operational processes to realise their value. Insurers also need to quickly identify existing models whose performance is deteriorating and be able to industrialise the discipline of model management. Build a capability to deploy and manage hundreds of models, not a handful!

The role of AI: Decision management and decision augmentation

The most innovative and successful insurers will be clear in their understanding of the role of AI for their business and customers. They will cut through the hype to know exactly what machine learning and artificial intelligence can do to discern opportunities to deliver better, more informed customer service. For example, natural language processing (NLP) can be used to decode hidden cues and subtexts in customer webchat interactions, prompting chatbots to offer the customer the right service or product at the right time.

Advanced analytics and AI capabilities like these are within reach of insurers today. But applying them randomly won’t deliver results. For the outcome to be the best possible – for you and your customers – you need to orchestrate these analytical resources to ensure a consistent experience is delivered across every possible customer interaction. Carefully orchestrating the use of analytics to augment or automate the millions of decisions being made across your enterprise is key. Don’t just replace decisions with basic automation, but use analytics to optimise those decisions in every possible way.

Deriving insights from more, and growing numbers of, data sources is possible at sensible cost and timeframes.  Enterprise-wide analytics is within reach, providing you have a high data management quotient. Test it here.

Without access to full, accurate data, analytics and AI technologies could end up making offers that don’t make sense for that customer at that time – or, more likely, miss opportunities altogether. Real success will come when cultural and operational changes create a business committed to being data driven and maintaining its data assets.

If a digital acquisition department can’t access accurate data on claims, for example, decisions will lack the full context of that customer’s needs. But when that data is shared across the business, digital acquisition can use the insights to ensure every chatbot, claims handler or customer service agent have the most complete and up-to-date understanding of the customer.

So before you implement any analytics or AI initiative, it pays to get your data in order first. You also need to do this in a way that builds trust with customers to ensure that you and your analytics community are benefiting from GDPR.

Are you ready to move beyond the analytics prototype?

Like many other insurers, you may be seeing the early signs of value from analytical experimentation and investment in skills, but how do you go from here to a truly customer-centric organisation?

By breaking down data and organisational silos, you can start to see your customers for who they really are – whole, rounded people with rich, interconnected lives and complex needs. And when you can see all that, you’re in a position to offer them very valuable services indeed.

At SAS, we help insurers to industrialise analytics with a platform that can create a solid foundation for customer-centric AI and analytics initiatives. If you’re ready to start that journey, get in touch. We will also be sharing our views at the Digital Insurance World event in October.

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Paul Ridge

Client Manager, SAS UK & Ireland

Paul Ridge, Banking & Insurance Specialist, SAS UK & Ireland, assists clients to maximise the value of data and analytics. He is an expert in helping them move through the analytics lifecycle at speed to deploy insight within their operational processes to change outcomes and to do this in a repeatable and governed process.

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