Solving the Customer Data Management Conundrum
Insurance providers are experts in understanding statistical data but face multiple barriers when it comes to leveraging their own data to achieve true Customer Data Management (CDM). A study by the Managing General Agents Association found only 30% of MGAs use their data for cross-selling[i]. Separated database infrastructures, high levels of switching plus merger and acquisition activity makes CDM a challenge for the market. With 2021[ii] expected to bring a further increase in M&A activity, insurance providers can overcome these barriers leveraging over two billion pieces of insurance data.
Innovation is a business imperative
Mergers and acquisitions are largely driven by insurance providers looking to grow their portfolios, accelerate business transformation and improve the customer experience[iii]. As innovation has become a business imperative, insurtech partnerships and M&A activity are providing routes to gain a competitive edge. Ironically, however, this activity brings reputational risks by sheer virtue of the fact that it often brings a fresh set of customer data to assimilate and integrate within the business.
The headache of data integration
Integrating data for consistent use across the business is far from easy. Consumer data can end up being stored in multiple silos – application, quote, claims, marketing - where it risks becoming outdated, incorrect and inconsistent. It means individuals can appear multiple times across disparate customer databases within the same insurance group with no link being made between records. To compound the challenge, the customer might be listed at different addresses or even have different representations of their name due to life events or simply because of input errors.
John Smith-Jones, JK Smith, Jon K Smith?
This makes it difficult to know with confidence that Mr John Smith-Jones at 10 Elton Avenue, London, who has applied for motor insurance today, is the same JK Smith 10 Elton Avenue, who had a home insurance policy with the brand 4 years ago and is the same Jon K Smith at 1 Elton Avenue, London who had a small commercial van claim against one of your policyholders last year.
Aside from the potential this lack of clarity creates for poor customer service, duplicate or outdated consumer information can lead to inaccurate pricing, the risk of fraud, wasted marketing budgets, lost cross-sell and upsell opportunities and unnecessary data storage costs. This can all have a detrimental impact on business profits and growth which is of course counter to the reasons for the merger or acquisition activity that may have given rise to these problems.
Linking and matching disparate customer data
Linking and matching customer data to create a single customer view can feel like an impossible task but now insurance specific data, analytics skills and technology have combined to solve this problem for insurance providers.
LexID® is the unique identifier built from proprietary linking technology and using vast policy history rich data resources to match disparate identity information.
This proven, ID matching solution sitting behind many LexisNexis products is now available to insurance providers to link all their data assets together. It means they will know if they are quoting a home policy to an existing motor customer; if someone making a claim against them used to be a customer and that a named driver being added to a policy used to be insured by them. That’s just a start, the opportunities created by this data matching and linking capability are immense.
Data matching solution provides the magic ingredient
LexID® is derived from LexisNexis patented Scalable Automated Linking Technology or SALT which links customer records with intersecting data points. It in effect, joins the dots – finding common threads between records to match up disparate data.
To more precisely match data and build an accurate picture of the individual, it pulls on a wide range of data sets comprising circa 2.3bn records including public and policy history data gathered from across the market. Records with commonalities are linked together and are then assigned the same LexID®.
It is this powerful combination of vast data resources and complex probability modelling that can produce superior matching results over rules-based and fuzzy matching using an insurance-provider’s data alone.
SALT uses advanced concepts such as term specificity to determine the relevance/weight of a particular field in the scope of the linking process, and a mathematical model based on the input data, rather than the need for hand coded user rules – this is key to the overall efficiency of the solution.
A single customer view
By pulling together data from multiple touch points, insurance providers gain a comprehensive and accurate representation of a customer’s identity, at whatever point they are in their dealings with the brand - customer, policyholder, claimant, applicant, prospect.
This single, consolidated, instant view based on every contact they have had with that individual then creates the basis for all future dealings with the customer. Identity profiles are continuously updated ensuring a dynamic and accurate picture is created accounting for identity changes over time.
Marketing, customer service, pricing, underwriting and claims can all benefit. With an immediate understanding of all points of a relationship with that person, insurance providers are able to provide a more relevant and customised service experience. It stands to reason, an insurance provider who knows the details of a customer’s wider insurance requirements, renewal dates and up-to-date contact information is also much more able to carry out effective communication at all stages of the customer journey, and in-turn cross sell relevant products at the right time.
Customers shouldn’t have to repeat their details
A recent White Paper by ResponseTap[iv] with input from LexisNexis Risk Solutions underlines the value of the single customer view when it comes to customer service and ensuring customers are not having to repeat details about themselves unnecessarily. When people were asked why they chose not to buy insurance over the phone in the last 12 months, 42% said: “I don’t like going through the automated IVR system (e.g. press 1 for motor, press 2 for home)” 14% said: “I thought it would be more expensive.” A further 49% said: “I don’t like having to repeat all my details again over the phone.”
In the same survey, when consumers were asked whether they would pay more to speak to an insurance specialist on the phone, 40% of respondents said they would, if the experience was as quick and easy as buying online.
A similar sentiment was expressed in a recent PwC report “The Future of CX”[v] ,which highlights an 18% gap between customer experience and expectation. The study found that people buying car insurance would be prepared to pay a 7% premium in return for good customer service.
Understanding lifetime value
Clearly, access to more accurate information helps insurance providers understand the lifetime value of a customer, supporting more targeted and efficient direct marketing programmes, as well as accurate pricing based on a fuller understanding of the overall risk of the individual and their assets at point of quote.
Insurance providers are under significant pressure to understand the changing needs of their customers and to price fairly. That starts with maximising the data they already hold. ID matching solutions such as LexID can provide the key ingredient to perform true customer data management. By creating the elusive single customer view, insurance providers can better understand and better serve both new and existing clients.
[i]
https://www.postonline.co.uk/technology/7717331/blog-ecosystems-will-be-the-saviour-of-brokers
[ii] https://www.ft.com/content/a022bd8b-0cdf-46d6-a4b9-ba1aad5a2ad2
[iii] https://www2.deloitte.com/us/en/pages/financial-services/articles/insurance-m-and-a-outlook.html
[v] https://www.pwc.com/us/en/advisory-services/publications/consumer-intelligence-series/pwc-consumer-intelligence-series-customer-experience.pdf
