There is a customer experience fault line running through most insurance companies based on a weakness around customer data, analytics and predictive modelling. The fault makes it difficult to develop a CX improvement plan and a measurement framework to track the business impact and ROI on initiatives.

This weakness creates two problems. Firstly, companies are unable to use insurance analytics to make data-driven decisions about how, and where, to invest. Secondly, creating ownership, accountability and reward is a challenge when it’s difficult to establish clearly defined business goals that are linked to a specific intervention.

How to use insurance analytics to drive ROI

In this article we walk you through how your organisation can avoid making these mistakes by doing two things:

1. Plugging your data into a measurement framework to identify a priority business goal and the metrics you need track to demonstrate the business value of your efforts.

2. Extracting significant, measurable business value from your data to plan CX improvements and identify operational changes needed to drive the change.

Customer experience leaders do both of these things. They are able to make a direct link between business results and their CX efforts. And they upend expectations on experience because they create products, services and experiences built on customer behaviour and preference data.

These insights give them the agility to continuously innovate and respond in a meaningful way to the wants and needs of their customers. This is what customers expect. But the evidence is all around us that customers are in many ways unhappy.

YouGov research by Laka found that UK consumers were ‘overwhelmingly’ unhappy with insurers. Just 23% of survey respondents expected their insurance company to treat them fairly. Meanwhile, almost three-quarters (73%) of domestic customers feel that the CX for insurance products has stood still for the past five years, according to FintechOS. Rewarding customer loyalty at 42% was the area that people most urgently wanted improving.

Customers only see value when something bad happens

Customers only see value in what they pay for if the worst happens. If a water pipe bursts the customer has peace of mind that they paid their premium. But, if there isn’t cause to make a claim during the policy period, then mentally they feel like they’ve paid for nothing. The reality is many customers see insurance as a ‘grudge’ purchase.

Customers only hear from their insurers once or twice each year. Outside of the transactional relationship, it’s difficult to engage and engender trust. Customers don’t think that insurers work in their best interests. Loyalty is weak. There is a competitive threat from agile, responsive insurers and InsurTechs that use data-driven insights to create measurable, improved experiences.

Joe Bloggs knows the value of his data

Added to this is the fact that consumers are now more data literate than ever before. The impact of legislation such as GDPR and privacy initiatives from the tech powerhouses have meant that Joe Bloggs knows the value of his personal information. He has an expectation that if he shares his data he’ll get a meaningful experience in return. He expects a fair value exchange.

Joe wants a frictionless hybrid experience,. He wants empathetic service, personalised insurance and non-insurance products and services offered to him at the right moment in his life. But does he think this is currently happening? The answer is a resounding no. Joe’s negative views on the insurance sector’s usage of data run deep and are entrenched. We’ve all heard Martin Lewis explain at great lengths how algorithms work and his handy hints on getting a cheaper deal. Tips such as putting ‘lecturer’ rather than ‘teacher’ as a job title which apparently knocks tens of pounds off a car insurance quote. This will not restore trust.

So, in an industry where trust is at a low ebb, loyalty is price driven and competitive advantage is king how can insurance companies better leverage their data to enhance the customer experience?

Driving improvements through the CX Measurement Framework

The answer lies in unpicking the existing customer experience and identifying the operational changes that need to be made to deliver a new and improved customer experience. The CX Measurement Framework enables insurers to do this and thereby deliver their customer experience consistently with low and predictable costs.

Setting your goal (the business result) is the starting point for the Framework below. It’s designed to help you explore six big picture questions. These questions will help you think about how and where your customer experience adds value and determine the priority metric you want to track.

cx measurement framework

1. Business results – what strategic choices will achieve our vision and financial goals?

2. Customer behaviour – what behaviour do we require from customers to achieve our financial and strategic goals? Do they need to spend more, buy more products, recommend our organisation, stay longer as a customer?

3. The customer experience – what service experience must we provide to drive this behaviour?

4. Culture and people – how do we ensure our people are engaged, motivated and capable?

5. Products and services – how do we ensure that our products and value added services differentiate us?

6. Processes and technology – how do we ensure that our enabling processes and technology are simple and easy to use?

By fully auditing the Framework questions, it’s then possible to start to apply analytics for each element. This will provide pragmatic insights to make a positive bottom-line impact.

Using data to drive change through the CX Measurement Framework

Key to the customer experience is building an in-depth understanding of how the customer buys and their usage behaviours. This results in the identification of discrete cohorts of customers defined by their behaviour – not their demographics. Real-world behaviour rather than location, age or gender is a far more powerful indicator on which to make strategic targeting decisions.

By identifying these cohorts it’s possible to widen the potential market by applying look alike and propensity modelling. This identifies high potential prospects that display similar behaviour to the already defined customer cohorts.

The next step in the journey is recommending the right products at the right time to the right customer. Through sophisticated machine learning and AI it’s possible to build recommendation engines that will target products at the most relevant point of each individual’s unique customer journey.

Recommendation engines are powerful.