Customer data management for insurance companies

Reliable customer data accelerates the go-to-market of digital offerings.

Customers are demanding digital closing paths that lead quickly and easily to the conclusion of an insurance policy. Insurtechs are living the data-based customer journey, while established insurers are challenged. The quality of customer data at digital touchpoints is becoming increasingly important.

Today, insurance companies operate in a digitized world in which customers demand the highest standards, for example in terms of personalization and service, but also the ability to communicate at any time via any channel and any device on any matter. In this respect, the role of industry companies is changing from pure insurers to true life companions. They must be able to recognize life moments at an early stage and provide 'predictive' support, so to speak.

But digitization also means that personal contact is declining and shifting to the virtual world. In this respect, the data of customers and prospects at digital touchpoints is of enormous importance. After all, only this data is available to understand customers in terms of their needs, desires, and expectations. That's why the data must be quality-checked and reliable. Otherwise, just about everything that can go wrong on the basis of data will go wrong, be it an unsuccessful approach or incorrect advice and billing.

Challenges of the industry

"Digitizing the insurance business means using customer data intelligently to implement a data-driven customer journey through knowledge."

How data quality supports:

Efficient customer processes:
If customer and prospect data is well-maintained, reliable, and fit for purpose, the processes that handle it will also work.
Customer Insights:
Having lots of data is all well and good. But you have to know how to use it. Only when data is well maintained does it reveal its knowledge. From this comes information that can be used for business, such as upselling and cross-selling.
Relationship management:
High data quality enables personalized offers and services. By being able to identify needs, preferences, habits and life phases, it is possible to act individually and carefully.
Efficiency booster:
Internal processes and workflows can be improved and automated. Data analyses help to identify and eliminate bottlenecks and inefficient processes. Automation also helps to reduce costs.
Holistic view:
If data can be consolidated and made available centrally, data silos can be broken up and, above all, redundancies eliminated. On the basis of multiple Golden Records, if necessary, various aggregations lead to expanded views and options for action.

Insurtechs are ahead of established insurance companies, especially when it comes to digitalization. This is because their business model is based on digital processes from the outset. In this respect, for example, digital sales channels without media discontinuity, embedded in a lean website and using analytical techniques, are part of the basic equipment. It is well known that data is crucial. But not only that. What is also needed is the corresponding competence to deal with the data and to use the knowledge it contains in a useful and value-adding way.

"The consolidation of the data of our group partners and the creation of a group partner ID were decisive target factors for us in the project. Here, we could fully rely on the expertise of Uniserv."

Sven von Dirke, Project Management, W&W

Read success story

Sven von Dirke, Project Management, W&W

Read success story

Key challenges in the insurance industry

In the field of tension between digitization, changing customer expectations, competitive pressure and profitability, insurance companies are confronted with these tasks in particular, among others, in order to be data-driven and equipped for the 'phygital customer journey', i.e. the combination of the physical and digital worlds to create a hybrid customer journey:

Digital closing distance for insurance companies. Reduce to the max.

The younger Generation Z in particular, i.e. those born between 1995 and 2010, are increasingly pushing into an age when they have to leave their parents' co-insurance policies and insure themselves. This generation, also known as digital natives, has grown up with digital technologies. They demand the ability to take out insurance digitally. The focus is primarily on composite insurances such as motor vehicle, household contents, accident or personal liability.

Stable processes are needed to ensure that the necessary digital closing processes work reliably. These in turn are based on valid, i.e. quality-assured, data. Smart processes in digital models are what insurtechs are already demonstrating and what established insurance companies need to catch up on as soon as possible. If the digital closing route meets the requirements, prospects will stay on board and actually take out the insurance. The proverbial battle for customer contact is on; it is shifting to the digital world. 24/7-Insurance, so to speak. Anywhere, any time, any channel, any device. Consolidated data with a 360-degree view of each individual customer is crucial.

Because digital is good. But it's even better to recognize life moments and be present with a personalized offer, predictively so to speak. This is exactly where our Customer Data Hub comes in: It breaks down data silos and consolidates existing data into a unified, centralized dataset with a 360-degree view of each customer as complete data truth. This enables you to offer your customers personalized and completely digital new services.

Away from silos, toward a cross-line-of-business view

Large insurance companies in particular are characterized by insurance lines. Life insurance, health insurance, property/casualty insurance, and perhaps asset accumulation products are all neatly separated. A cross-line of business view is therefore rather difficult. However, a line-of-business view leads to isolated silos of customer data. There are most likely redundancies if customers have multiple insurance policies. Evaluations and analytics thus hardly lead to reliable results.

For example, if there is a growth, target and insurance policies are to be sold digitally. This is hardly feasible if customer and policy data are distributed across different lines of business and managed in isolation. Only if a common view of the data is available can insurance be sold digitally in a meaningful way and growth targets be advanced. For example, it is now possible to introduce a Unique Customer ID, which can be used to unify data and silos.

Customer Centricity comes to town. But before that, data from the individual divisions must be extracted, transformed, harmonized and unified, and loaded into the new system. This is the ideal time to address data quality and discard unnecessary ballast. A 360-degree view of each individual customer is ideal. This can be achieved with the Customer Data Hub. Dissolve data silos, merge and consolidate data, including back-synchronization into the source systems.

And if you are not sure who is who or who belongs to whom, or who lives with whom in a household and has what insurance, then the error-tolerant precision identification of Identity Resolution can help.

Process revolution: automation based on valid data

Digitalization is driving change. Artificial intelligence methods in particular are accelerating the implementation of new process models. In this respect, the insurance sector is in a phase of consolidating transformation. The aim is to further automate its own processes, if this has not already been done.

Machine systems can often complete standard tasks much more quickly, especially the preparation of quotations and invoices. As a result, symbiotic systems are increasingly emerging in which there is a division of labor between humans and artificial intelligence. The better the quality of the data within the data-carrying processes, the more efficient the processes will be in the end.

An artificial intelligence is only as good as the quality of the data with which it has been trained. And since good training data in particular is becoming increasingly expensive, it makes perfect sense to build up a high-quality training pool by investing in the quality of your own data. Professional customer data management becomes necessary, in which data is understood as an asset. For this, the constant optimization of the data should be mapped in a continuous process. The Uniserv Customer Data Hub generates decisive added value for this by dissolving data silos and consolidating existing data into a unified, central data stock with a 360-degree view of each customer as complete data truth.

The starting point for automation: up-to-date, complete, correct and unambiguous data.

Address validation: postal valid addresses.

Identity resolution: track down duplicate and multiple data records.

E-mail validation: communicate digitally at any time.