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Linking and matching to achieve a single customer view

News article

Publication date:

24 September 2021

Last updated:

11 October 2021

Author(s):

James Burton

In the region of 20 million households in the UK have motor insurance.

In the region of 20 million households in the UK have motor insurance[i] and around 19.3 million have home contents insurance[ii].

The volume of customer data insurance providers typically manage is vast. The insurance market is built on its use of statistical data to understand the probability of a claim, but separated database infrastructures, high levels of switching, plus merger and acquisition activity has made customer data management (CDM) a major challenge. The urgency to find a solution to the CDM challenge has risen as the market prepares for new pricing regulations[iii].

Insurance providers will need to know their existing customer to a far greater degree so that they can deliver fair pricing and fair outcomes to those customers at renewal. In essence, they need to create a single customer view based on all the previous touchpoints and history the brand or brands has had with that customer.

By pulling together data from multiple touch points – quotes, renewals, claims and marketing - insurance providers can build a comprehensive and accurate representation of a customer’s identity, at whatever point they are in their dealings with the brand. It also means they can utilise a consistent methodology for standardisation and matching of customer data across multiple databases.

Perhaps most importantly, it can help determine that the right product is being offered for the risk presented at renewal, and at the right price.

The problem is that integrating data for consistent use across the business can be complex when you consider that consumer data can end up being stored in disparate databases in application, quote, claims, marketing - where it may become outdated, incorrect and inconsistent. It is easy to see how individuals may appear multiple times across separate customer databases within the same insurance group with no link being made between records.

Linking the data becomes even more of a challenge when you factor in address changes, name changes or input errors. Aside from the negative impact on customer service which runs counter to how the market is constantly striving to improve the customer experience, data inaccuracy can lead to inaccurate pricing at renewal and a lack of understanding of the best product for the customer’s needs. It can also create the risk of fraud, leading to wasted marketing budgets and increased operational costs, as well as lost cross-sell and upsell opportunities.

Solving the challenge of linking and matching customer data to create a single customer view in the insurance market comes down to using insurance specific data, analytics skills and technology.

Patented linking and clustering methods can now help insurance providers link all their data assets together. This means one ‘true’ record can be created for one customer using a unique identifier, giving insurance providers a consolidated view of their customer based on every contact or policy they have had with that person. This then creates the foundation for all future dealings with that customer. It means when it comes to enriching the data using external datasets, that can be done in confidence that the core customer data is accurate. The picture of the individual’s risk can then be enhanced as more data is accumulated.

The way linking and matching works is to find common threads between records to match up disparate data. This process pulls on a wide range of external data sets including public records and insurance policy history data gathered from across the market. Records with commonalities are linked together and are then assigned the same unique identifier. This process can be done in batch form for all existing customer records and at the point of quote so that new customers are also assigned a unique identifier.

Marketing, customer services, pricing, underwriting, portfolio management and claims can all benefit. By consolidating details about a policyholder, insurance providers can see all points of a relationship with that person and provide more relevant and customised products and services. Fundamentally, it can support accurate pricing based on an accurate understanding of the overall risk of the individual and their assets for new business and more pertinently, renewal.

Also, knowing that approaching policy renewal, there will be some existing customers that shop around and request new business quotes from their current provider, and the unique identifier can flag these customers to support pricing consistency in line with the new FCA led pricing rules[iv] during the quote process.

Insurance providers are under more pressure than ever to ensure they are using the data they already hold and have access to the information they need to deliver fair pricing and outcomes to their customers. The LexID® unique identifier can help insurance providers make sense and make use of the vast volumes of customer data held across their business. It can provide the foundation for building a clearer, more informed picture of the customer to help ensure the product and price are appropriate for their individual needs.

 

[i] https://www.statista.com/topics/4560/car-insurance-in-the-uk/

[ii] https://www.statista.com/statistics/829952/households-with-contents-insurance-united-kingdom/

[iii] https://www.fca.org.uk/publications/policy-statements/ps21-11-general-insurance-pricing-practices-amendments

[iv] https://www.fca.org.uk/publications/policy-statements/ps21-11-general-insurance-pricing-practices-amendments

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This document is believed to be accurate but is not intended as a basis of knowledge upon which advice can be given. Neither the author (personal or corporate), the CII group, local institute or Society, or any of the officers or employees of those organisations accept any responsibility for any loss occasioned to any person acting or refraining from action as a result of the data or opinions included in this material. Opinions expressed are those of the author or authors and not necessarily those of the CII group, local institutes, or Societies.

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