The growing demand for hyper-personalization
Today’s customers are accustomed to Amazon-like personalized product and service experiences in their day-to-day life, where they are provided suggestions and recommendations aligned to specific preferences and interests.
It is only natural for us to expect the same level of attention from insurers as well. Let us see how real-time data integration can be leveraged for hyper-personalization in the insurance space.
Common roadblocks to hyper-personalization
Insurance enterprises are traditionally characterized by batch processing and legacy applications.
However, today’s tech-savvy customers expect a personalized service relevant to their context. For this, insurers need real-time access to data to make intelligent, context-aware decisions. Real-time integration also demands other capabilities, one of which is identifying and enabling the right data sources to create the required information for the right stakeholders. This becomes more challenging for large enterprises due to the inconsistency of data formats, proprietary system information, the time and frequency of a business event, and legacy integration complexities.
The need for real-time data integration
From an enterprise point of view, key aspects of real-time integration include the capability of bringing data from multiple sources to give a consolidated view and deliver to interested targets.
Since several correlated activities take place from a business process perspective, it is imperative to visualize personalized insurance products from an event-driven approach. The core power of real-time integration depends on context-aware and content-aware data, where the continuous data stream helps take corrective actions in real time. Hence, insurers must adopt enterprise systems like artificial intelligence (AI)-based platforms and analytics engines to be more effective with real-time data.
Insurers must note that there is no one solution that can cater to an enterprise's current real-time integration needs. Database techniques like change data capture (CDC) and data streaming can enable real-time integration. Further, platforms and products like event-driven solutions can be integrated to cater to real-time data processing needs.
Overcome your greatest insurance challenges
One aspect where the customer always needs help is the identification of the right product.
Today, users get the advice they need after entering all details and submitting the data. Although chatbot-based advice is available, it is not personalized. Here, businesses must analyze opportunities and provide personalized service. Some possible outcomes could be:
Based on insights from the backend, some advice may pop up on the screen
An agent is allocated to call and advise customers
Specific brochures and details are sent to the potential customer through an e-mail
Today’s customers greatly appreciate such proactive notifications and advice.
Another potential use case that provides significant cross-sell opportunities is the change of personal details such as the address. This is an opportunity for insurers to offer personalized advice about the new area along with any additional coverage in the existing policy. A revised premium or advice for a better product is a possibility. Some insights about the new location can provide advice such as flood warnings and even include services and utilities from other partners in the ecosystem.
Another example is during the claims registration process, where users can be allowed to send an audio description of the incident. This audio can be matched against the trained set to check if it is a fraud. Thus, one round of fraud checks can be carried out in real time.
The ideal solution
The solution should enable enterprises to capture and process data pertaining to specific events, thereby allowing them to respond in real time.
This facilitates organizations to act on events with relevance to the time they happened and the time they get processed or within a specific time window. The solution should include multiple plug-and-play modules that allow:
Capturing of input event data from multiple sources in real time; for example, all information associated with a claims registration process – text data, media files, audio files, and external information
Processing of incoming event data; for example, transformation of data format, application of claims rules, and business logic
Enriching the event data with contextual information; for example, claims history
Providing additional insights by integrating with analytics and AI systems; for example, claims patterns from a similar demographic
Executing the most appropriate event action, relevant notifications, and alerts; for example, proactive information about settlement delays
Today, customer expectations for personalized products and services from insurers are at par with retail experiences. Insurers are competing to provide such experiences to their customers by making the right decisions at the right time in response to a business event. This also means they can incorporate more services and products from a wider ecosystem, thus enabling comprehensive and personalized customer journeys.