Insurers have invested significantly in customer data, segmentation and behavioural analytics to improve engagement. However, many still face challenges when turning those insights into personalised product recommendations that clearly communicate value to customers.
Earnix’s recent analysis highlights that the issue is not a shortage of customer understanding, but the difficulty of linking customer insights with product knowledge in a way that is accurate, scalable and easy to explain.
Insurers already hold detailed information about their products, including coverages, exclusions, limits, conditions and benefits. However, this knowledge is often stored within lengthy documentation, making it difficult to maintain and apply as product portfolios become increasingly complex.
This has meant that recommendation processes have remained largely manual. Teams often need to review product documents, identify relevant benefits and adapt messaging for different customer groups, creating additional time requirements and increasing the risk of inconsistent interactions.
Earnix has introduced new AI capabilities within its Engage-It platform designed to transform product documentation into structured intelligence that can support recommendation workflows.
The platform’s AI agents analyse insurance product documentation and organise information on plans, coverages, exclusions, limits, conditions and product strengths into structured, editable knowledge. Insurers can review, validate and amend this information before it is used, ensuring recommendations remain aligned with approved product details.
By connecting customer profiles with relevant product strengths, Engage-It enables insurers to tailor recommendations based on individual needs while keeping messaging aligned with the actual features of the product. The same policy can be positioned differently depending on the customer segment, such as a young professional, a family or a retired homeowner.
Earnix said the approach enables insurers to create more recommendations in less time, improve consistency across customer interactions and identify commercial opportunities while supporting customer needs.
The release also includes the Product Expert Agent, which provides natural language responses to questions from advisors, service teams and customers about coverage details, exclusions and plan benefits.
Instead of relying on manual searches through product documentation, users can access responses based on the relevant recommendation and customer context.
Earnix’s analysis suggests that making product knowledge more structured and reusable could help insurers scale personalised engagement by reducing reliance on manual document searches and improving access to product information throughout customer interactions.
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