Taking data-driven CX to new heights

These days, consumers are being met with increasingly personalised experiences in almost every facet of their lives, from streaming to banking. As a result, customer expectations are increasing, and insurers must find a way to keep up. With the world becoming more and more connected, incumbents and alike have the opportunity to leverage the influx of data coming their way to their advantage, and innovate with the customer in mind.

Today, we speak to James Russell (Head of Claims Transformation at esure), Gregory Burr (Solution Specialist at SAS) and Andrew Pollard (Account Executive at SAS) about the evolution of customers’ expectations, and how insurers should respond in order to meet them.

1. How have customer expectations of their insurer changed and are insurers ready to meet these rising expectations?

[James Russell, esure] We have two generations of consumers coming through who differ greatly from the profit-driven, materialistic reputation that banks and insurers are still trying to shake off. In my view, insurance and financial services is still stuck in the ‘80s in terms of being focused on profit. We look at the success of a product in terms of whether it has a positive combined operating ratio rather than whether we are actually providing good protection and good outcomes for customers. If we look at Millennials and Gen Z, they are more questioning, they want things tailored to them, they are championing the sharing economy, they are digitally native and they want to buy from ethical and sustainable companies. So, it’s no surprise to me that in various surveys, insurers and banks are still polling pretty low trust ratings among consumers.

We have to shift what we do and how we present ourselves; we need consumers to vote with their feet. Many traditional insurance companies are still caught under a cloud, and some are actively perpetuating the tarnished reputation of financial services. Insurtechs such as esure actually have a real advantage over large incumbents who are saddled with legacy systems, old practices, and an old culture. At esure, we’re on a mission to fix insurance for good by investing in a new platform and colleague capability, doing something positive for the community, and really questioning what we’re doing and how we’re actually helping people. We see the opportunity to build a proposition to meet the rising expectations of consumers. Some insurers are doing the same, but some still need to shake off some of those old-fashioned practices.

[Gregory Burr, SAS] People often want to point towards a generational divide. In my opinion, everyone has the same expectation now: customers want to have a really good experience with people, they want relevant offers, and a personalised experience. Essentially, they want their brand to know who they are. These days, most brands have a strategy in place and a customer experience function. Everyone wants to leverage real-time data and provide great customer experience. Insurers need to question whether their customer experience strategy is the right one, by using metrics such as NPS and revenue-per-customer to check whether they are going in the right direction.

[Andrew Pollard, SAS] Personalisation and flexibility are the key terms here. Outside of the insurance industry, we are all consumers, and we are used to Netflix recommending the next film to watch or Peloton telling us which training session to do next. We want the same for our insurance products. Insurers are positioned for disruption at the moment, so there is a huge opportunity for them to deliver on that. From listening to insurers today, they are all on that road to doing so, it’s just not as easy as it looks. Although consumers see personalisation every day and think it should be easy for insurers to do the same, siloes are a real issue. We need to move away from siloes in terms of claims, risks, fraud and so on, but there is a fundamental inability to do so, and we are left with a ‘veneer of the digital’ as some people say. Everything looks digital on the front, but in the back, there is lots of manual work being done. We’re here to help to move that along and give the customers what they want.

2. What threat do new entrants to the market represent and what can established players learn from them?

[JR, esure] Personally, I don’t see new entrants as a threat. Frankly, insurance needs to modernise, and as an industry we need new entrants to come in, do something different and turn insurance on its head. They will help to change customer perception and move the bar higher. One of the big things that holds us back from modernising, ironically, is that consumers are so conditioned to the status quo, thinking that their premium will go up every year, and that they will be penalised for disclosing information. We actually need new entrants to come in with a different model and say, for example, ‘if you don’t claim, we’ll put some of the money to a charity of your choice’. The industry needs players like that to shift consumers’ perception of insurance, so I welcome new entrants, personally.

[AP, SAS] For me, looking at new entrants as a threat is not the way to go about it, I see it as an opportunity. New entrants to the market that are already major players in other industries, such as Amazon or Tesla, bring with them a very different relationship with the customer, so there is an opportunity for the insurer to partner with them. On the other side of the coin, insurtechs have had the pick of the talent for many years and a vision to drive forward; there hasn’t been any legacy infrastructure holding them back. We’ve seen some really good products come from insurtechs, but incumbents partnering with these insurtechs is where we get scale.

3. How can insurers leverage their data more effectively to deliver real-time engagement that is contextual and personalised? 

[JR, esure] A former colleague of mine once said: ‘we need to get the data to flow to where it needs to go’, and I still hold onto that assertion. Whether data is needed in real time for in-the-moment, next-best-action decision making, or whether it is collected over time and used for deeper analytics, we need to help that data reach where it needs to go. That way, we can help customers make informed decisions about risk, understand what they want to protect and what options they have around how they protect that, and then ideally prevent bad things from happening. All of this modernises what insurance is and how it is perceived, but it needs data to make that happen. We’ve got to liberate that data to make some of those ideal propositions come to life.

[GB, SAS] There is technology in the market that will allow you to capture that real-time data. This data should give an idea of the life stage of a given individual, such as whether they are buying a home or having children. By being able to recognise these moments that matter, you’re able to respond with a contextualised offer in real-time, such as offering home insurance to someone who is buying a home.

[AP, SAS] Insurers talk a lot about customer-centricity: is the goal here to be there for the customer, pay out their claim quicker and give them updates along the journey? Or is the goal to upsell them to another product and extract more value from that customer? The default is to try and cross-sell and upsell to get more revenue when the ultimate goal from an insurance company should be to help when the customer needs them.

4. What are some of the innovations, like AI and machine learning, that can allow insurers to deliver this next-generation customer experience?

[JR, esure] There is publicly available data and then there is personal, privately held data. Public data can be used to help understand risk without asking loads of questions and making access to financial services difficult for customers. Customers need to attest that the data is true and accurate, but why shouldn’t we use publicly available data to make things easier for them? This is especially relevant in commercial lines, where you can create a business profile, but equally you can get details about a car, about other property, or the circumstances of a claim. All of these details are available so that we’re able to tell a customer, for example, that ‘you’re in a postcode which is a subsidence area, but we can see that you’ve had the property underpinned’, without ignoring this crucial context. Or in the case of a flood area, such as Southampton Docks, there are properties where the occupants know that they flood regularly, but they have everything lifted up off of the ground, so they have taken action to mitigate that risk. If we have the data, we can start to actually factor that in rather than just refuse to quote insurance on a given property.

This extends into privately held data. If a customer elects to share the private data through open banking, for example, then insurers can tailor the products to their needs and, in the case of commercial lines, adapt cover as the business changes. So, if a business shares data about the levels of stock they have, they can share their electronic point-of-sale data to state what they are selling and what they are bringing in and the sums insured can be adjusted dynamically. This helps to raise customers’ confidence around knowing that they are paying a premium for the risk that they have at this moment in time; it helps to build trust as a result of that transparency. An extreme example in the fleet space is a collaboration between the insurer, the broker, and the fleet management company, working together to identify leading indicators of a potential accident. For example, a driver could be dealing with some difficult personal circumstances out of work; they may not be attending their training, their morale and engagement may be low, and some of the telematics data about their driving behaviour may be a bit erratic. All of these indicators tell them that they should intervene here to prevent an accident from happening. Those are some examples of where we can use data and predictive analytics to actually improve the insurance offering. If we do that responsibly and don’t abuse that trust, then we can gain even more trust from consumers.

[AP, SAS] As we move into an age of connected cars, we’re going to have an influx of an unimaginable amount of data that cannot be managed using normal technological processes. Machine learning in those instances can really help to unearth insights where you otherwise wouldn’t be able to get them and to leverage data at scale. There’s also an opportunity for small-scale wins as well. Looking at the big picture is great, but the small-scale wins in automating those manual processes that will speed up the claims journey and get people paid quicker is something that insurers need to be watching at the same time.

[GB, SAS] With the explosion of data that has happened over the last few years, it’s important to retain a human element to the customer experience. We need to have employees and AI are working together to reach the quickest and most accurate decisions possible.

5. Finally, what about the next wave of transformation? How can insurers make sure they are ready for innovations like Web 3.0, the Metaverse, or Blockchain for example?

[JR, esure] There are already some good Blockchain applications in the insurance industry, such as parametric insurance for travel delay compensation. However, there are instances in which companies are still trying to find where new technologies actually fit and where they can solve a problem. That is the key for me – insurers really need to understand where there are gaps and opportunities in their current offerings and setup. Open data can make financial services more accessible for people who wouldn’t have previously considered life insurance, for example. There are also examples of microinsurance in certain countries around the world that have proved that you can provide a very basic, low-cost level of life insurance to people through greater transparency and access to data.

For things like the metaverse to catch on, we need to see where it can really solve a problem or unlock something we didn’t do before. We have all seen the adverts involving medicine training – the surgery simulation capabilities of the metaverse are clear. I’ve heard about applications where we could use the metaverse for scenario planning in insurance; I can see that in a corporate setting where you might be using VR or augmented reality to do risk assessments on large complex risks. That being said, I’m yet to hear of a realistic application in personal motor, for example, where the metaverse can actually play a role.

Examples like microinsurance or the use of data to automate risk profiling and claim prevention will be much more relevant than the metaverse in personal lines. There are several emerging technologies like the metaverse that we need to look at and challenge our own thinking in terms of where they could be useful, without trying to force-fit them into a certain area where they are not necessarily able to solve a problem.

SAS transforms insurers into hyperintelligent, AI-driven organisations backed by extensive insurance domain expertise. With SAS you can create personalised customer experiences whilst supporting risk solutions, managing costs, tackling fraud and remaining compliant. Their scalable, cloud-native enterprise analytics delivers a practical path to your digitalisation vision and faster time to value.