The insurance provider operates across multiple regions and lines of business, serving customers worldwide. Its scale places it in a complex, highly regulated market where modernized processes are increasingly important for efficiency and competitiveness. This expansion has resulted in a diverse and fragmented customer data landscape, which the company is now transforming into an insight-rich data foundation with the support of a master data management (MDM) platform.
Challenges
Defining the scope for customer data transformation
Along with other insurance providers, the company was experiencing a soft insurance market, with more customer demand than available supply. As part of its customer data transformation project, the organization wanted to take advantage of this market opportunity, improve customer retention, grow upsell and cross-sell opportunities and make more informed, strategic decisions on risk mitigation.
A growing business, with disconnected customer data
With customers and operations spanning multiple regions, the company managed more than a million customer records across several systems without a single, standardized data management approach.
Customer data ownership was decentralized across the organization. While individual teams performed well and demonstrated strong analytical capabilities, establishing consistent data practices at a global level proved difficult. This fragmentation limited the organization’s ability to fully leverage customer data at scale.
Leadership saw customer data as key to future growth. While existing customers offered significant cross-sell and upsell potential, inconsistent data practices across regions and teams limited the company’s ability to act on those opportunities.
Rather than focusing solely on technology, leadership prioritized understanding how customer data was being used across the business and where pain points existed. To build that understanding, a survey was conducted across more than a dozen business teams, creating a clear organization-wide view of challenges and priorities.
Survey results reveal data quality concerns
The survey identified several key issues. A majority of respondents expressed concern about data quality, while most participants described data collection and management as challenging. Many respondents indicated that they did not have a complete, end-to-end view of customers.
The survey also showed that the business clearly understood the value of high-quality customer data. Most respondents wanted to reduce manual effort associated with managing customer data, improve analytical capabilities and enhance cross-sell and upsell opportunities.
As the company embarks on its customer data transformation initiative, it aims to address the following challenges:
- Previous efforts to achieve a single customer view had not delivered the desired outcomes
- Customer data was scattered across teams, regions and systems, limiting insight and growth opportunities
- No single team owned customer data, slowing progress and reducing accountability
Solution
Delivering value quickly through a phased approach
The company selected the Stibo Systems Customer Experience Data Cloud solution to address its customer data challenges. The initial phase focused on delivering a minimum viable product (MVP) to demonstrate value quickly and establish a clear business case, with a six-month timeline from design to go-live.
Early efforts focused on integrating data across multiple systems and geographies, including standardizing geographic and industry classifications. Approximately 1.3 million customer records from recent years and multiple systems were consolidated and unified. Through careful match tuning, more than a third of records were automatically deduplicated during data loading into the platform.
The next phase focused on improving data completeness. By integrating the Stibo Systems solution with external business data sources, including Dun & Bradstreet, the company increased record completeness by 25%.
Changing the data culture
After deploying the MVP, the organization began using customer data across three key business areas:
First, customer data was incorporated into an analytics dashboard using Microsoft’s Power BI, making it easier for underwriters to uncover customer-related KPIs before client meetings.
Second, technical users, including analytics, data science and risk modelling teams, use the platform to integrate customer data into existing models and business cases. This supports more informed decision-making, one of the primary drivers behind the initiative.
The third use case supported the underwriting process and brokers’ face-to-face meetings with customers. Given the breadth of the company’s insurance portfolio, customers often held multiple policies for long periods. However, customer history was fragmented across systems, making it difficult for underwriting teams to see the full picture. In some cases, brokers and customers had a better understanding of past relationships than internal teams. By creating a unified customer view, the company closed this information gap and enabled more informed customer conversations.
As one leader notes:
“We wanted to make sure our teams had as much information as possible to feel confident going into customer conversations.”
Master data management (MDM) became a catalyst for broader data modernization, helping the organization scale strong local data practices to a consistent global level. Around 50 teams were making use of the company’s data in regional and organizational silos, and while they executed well locally, the goal of the Stibo Systems implementation and the wider architectural transformation was to elevate those capabilities across the enterprise.
Over time, the initiative expanded beyond customer data. The company later added reference data, such as industry classifications, geographic hierarchies and customer types, to support greater consistency and shared definitions across teams and systems.
As one leader notes:
“We saw the customer as the natural entry point for improving both the customer experience and our internal data culture, and that approach is paying off. Data ownership and stewardship are gaining real momentum and while an MDM tool cannot solve every problem, the Stibo Systems Platform has helped us reach a stage where we can truly start transforming how we work with data.”
As a result, the company realized the following benefits:
- A unified, trusted and complete customer view across all locations, teams and system worldwide
- Improved data quality and a 25% increase in record completeness, with more than a third of records automatically deduplicated
- Enhanced analytical capability, providing rich data insights for the actuarial, data science and customer-facing teams
- Stronger data ownership and stewardship, establishing a more consistent enterprise-wide data culture
- More informed customer conversations, enabling cross-sell and upsell opportunities across the customer base
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Summary
By implementing a strong multidomain master data management (MDM) foundation for customer and reference data, the company empowers business teams with the data and insight they need to support customers and unlock further growth.
A global view of customer relationships and geographies equips the organization with accurate, timely and granular insight, connecting previously disconnected systems, teams and regions.
Looking ahead, the organization plans to expand its use of the platform to support additional use cases, including business partner data, advanced analytics, and AI-driven and counter-fraud applications.
