Data-Driven Insurance: The 3 steps Insurers must take
Published March 16, 2024
- Data & AI
- Insurance
Data is the new cornerstone of success, and this is especially true for insurers. The data-driven insurance of the future requires a comprehensive and integrated data strategy to unlock its full potential. However, to realize these opportunities and transition to data-driven insurance, insurers must dismantle outdated structures while leveraging their historical strengths.
Insurers and its Data
Data is everywhere. In our daily lives, we create it almost constantly—how we move, when, how, and what we spend money on, how much we exercise, and even the music we listen to. All this data is collected and utilized, sometimes intentionally, sometimes less so. Data has given rise to new industries and revolutionized old ones, presenting insurers with fundamental challenges.
However, insurers have a historic advantage when it comes to working with data. Since their inception, insurance companies have been gathering and analyzing data, such as:
- weather events
- traffic statistics
- health data
- and mortality tables.
Insurers are data specialists. In fact, insurers employed data scientists decades before the term even existed. And the “data gold” of insurers extends far beyond the structured recording of historical events or trends. For example, traditionally insurance sales representatives had insights about their clients at their fingertips – they knew their neighbors and have insights on who is building a house, buying a new car, or starting a business. Which family is expecting a baby, or who just got a dog. It’s essentially “Data Lake 1.0”.
Data Drives Customer Proximity: 3 Steps to Data-Driven Insurance
Insurers have always been data-driven organizations. However, they now face the challenge of leveraging this historical strength to stay competitive in the future. The reality is that the long-term focus of their business model has often resulted in a lack of agility, leaving much of their data advantage underutilized.
The principle that applied 100 years ago still holds true today: the better the data, the more precise the engagement and support for customers. This proximity is crucial, especially in the age of hybrid customers, where maximum customer satisfaction is critical for success and long-term viability. So, how can insurers transition to becoming data-driven organizations?
Step 1: Customer-Centricity Through Data
Insurers must prioritize tailoring their services to individual customer needs and anticipating their desires. The goal should be hyper-personalized customer engagement powered by data. In the future, technologies like AI-driven language models can play a valuable role here.
To optimize the customer lifecycle, insurers must engage with their customers seamlessly across all channels, adapting to specific needs. This is only achievable if the right data is properly utilized and interpreted. Customer centricity, therefore, becomes both the foundation and the prerequisite for the successful transition to a data-driven company.
Understanding customers across their entire journey—how they seek information, what their needs are, how they prefer to be advised, and how they utilize services—is key to providing optimal support and retaining loyalty. Meeting these demands requires not just a solid data strategy but also its consistent and comprehensive integration.
Step 2: Integrating the Data Strategy
To succeed, a data strategy must be holistically embedded into the overarching business strategy. While insurers already have access to valuable data, successful implementation requires more than merely collecting and using it. Data is often highly diverse in both type and location.
For instance, the knowledge held by sales representatives cannot be seamlessly or meaningfully combined with historical weather data stored across various cloud servers. A successful data strategy must account for these disparities and be thoroughly integrated into the company’s strategy from the ground up. If treated as an afterthought, it will not deliver results.
Every business defines fundamental objectives and strategic goals. The subsequent question must always be: how can data contribute to achieving these goals? It is equally critical for insurers to continuously revisit these questions, reassess priorities, and make adjustments.
Step 3: Leveraging Touchpoints
A well-executed data strategy allows for efficient utilization of data. Every customer relationship presents multiple touchpoints, and the data generated from these interactions can be directly used to assess and improve the relationship.
Through data, insurers can answer key questions such as:
- What is the likelihood of a customer signing a policy when they begin exploring options?
- How has the business relationship evolved, and where is untapped potential?
- Are there identifiable life events, such as marriage, relocation, or a new job?
- What are the signs of customer dissatisfaction?
- What is the risk of cancellation, and how can it be mitigated early?
These insights not only enable more precise cross- and upselling opportunities but also foster stronger customer retention.
Example: How a Data-Driven Insurance Operates
If an insurance company identifies a need to improve customer satisfaction, data becomes a key component. Information from claims management, sales and customer service can be combined, analyzed and utilized to tailor the customer journey across all channels, making it more personalized and convenient.
Data-Driven Enterprise? The Questions That Define Future Readiness
What may seem simple in theory represents a fundamental shift in internal structure and culture for insurers in practice. At the same time, core governance questions arise: Is there a Chief Data Officer at the executive level, or has the data strategy been delegated to a sub-team within the IT department? The answers to these questions serve as indicators of an insurer’s future readiness.
For a data strategy to be truly comprehensive and effectively implemented, traditional silo thinking must be overcome. This also requires a shift in mindset among employees. Tensions often exist between those who produce data and those who use it—for example between sales and claims management—because both sides often lack a broader perspective beyond their own areas. However, when all departments, divisions, and teams strive to understand one another, this can unlock significant synergies in the future.
“Omnichannel is also Omnidata.” Dennis Glüsenkamp, Lead Data Strategist at qdive
Additionally, data-driven language models in the field of artificial intelligence, such as ChatGPT by OpenAI or Bard by Google, offer new opportunities with tremendous potential. Even today, programs of this kind could be used to answer individual customer service inquiries. For example, if a customer sends an email with a question about their insurance, language models can understand the question, review the customer’s documents, and provide a specific response. The same applies to inquiries via WhatsApp, chat, or even voice input over the phone. It’s easy to imagine that fundamental changes in customer service are on the horizon, with significant impacts on all internal operations. This makes it all the more important to integrate these changes into data and corporate strategies.
Already, some insurers are experimenting with language models on their websites to answer questions about publicly available information. However, handling more specific inquiries, such as those about personal insurance conditions, is not yet feasible. Models like ChatGPT do not meet GDPR data protection requirements and, therefore, cannot be used for these scenarios. However, it is likely to be only a matter of time before GDPR-compliant language models become available and widely adopted by insurers.
Another notable aspect of language model development is the speed of implementation. Once a GDPR-compliant solution is available, insurers will be able to implement it with relatively little effort. This presents exciting opportunities, even for insurers that are not at the forefront of digital optimization.