Understanding customers before investing in experience transformation
When a professional horse racing association began planning a major redevelopment of one of their racing parks, leadership faced a critical question: how do you invest in a better guest experience if you can’t clearly identify your guests?
The association had decades of customer activity across wagering, ticketing, concessions, and horse owner systems. Each system captured valuable information, but none were originally designed to work together. As a result, the organization lacked a reliable way to connect behavior across channels or understand the full relationship customers had with the brand.
Before improving the experience, the association needed to understand who its customers actually were.
Fragmented data limited strategic insight
Revenue flowed through multiple platforms, including:
- Wagering systems
- Ticketing platforms
- Food and beverage vendors
- Horse owner and participant management systems
Each environment operated independently. Identity fields varied widely in completeness and format, and there was no consistent way to confirm whether records represented the same individual across systems.
This created uncertainty around important business questions:
- Are ticket buyers also wagering customers?
- Which guests engage across multiple tracks?
- Who are the highest-value customers over time?
- Where should capital investments prioritize experience improvements?
As the association prepared to make significant investments in facilities and customer engagement, leadership needed confidence that decisions were grounded in real customer behavior, not assumptions.
Discovery revealed identity and data quality challenges
Rather than starting with technology, Resultant began with a focused discovery phase to understand how data was captured, managed, and used across the organization.
Through stakeholder interviews, vendor engagement, and data profiling, several key insights emerged:
- Identity fields were incomplete in some systems, limiting deterministic matching.
- Certain vendor datasets contained minimal useful customer information.
- Data formats and standards varied across platforms.
- Governance practices differed between systems and teams.
The core challenge was the absence of a reliable method to connect the data to accurately provide a clear view of the association’s customers. This insight shaped the entire approach.
Building a foundation for customer identity
Resultant designed and implemented a scalable data platform using Microsoft Azure and Databricks, structured around a Medallion architecture that organized data into raw, refined, and business-ready layers.
Because the association didn’t have an existing centralized cloud data platform, the engagement included provisioning secure infrastructure, ingestion pipelines, governance controls, and automated processing workflows from the ground up.
At the center of the solution was identity resolution.
Traditional matching approaches, like those used in public sector systems, rely on strong identifiers such as Social Security numbers or universal IDs. This organization’s environment required a different strategy.
Resultant implemented a Privacy Preserving Record Linkage (P3RL) approach combined with hybrid matching techniques:
- Deterministic rules for high-confidence matches
- Machine learning models for probabilistic matching
- Confidence scoring and monitoring to balance accuracy and coverage
- Transparent logic that allowed stakeholders to understand how matches were generated
Many products on the market have “black box” characteristics, lacking visibility and flexibility in matching processes. Our “glass box” approach provided both accuracy and trust, enabling the association to maintain control over identity logic rather than relying on opaque third-party tools.
Measurable results and executive visibility
Within approximately 15 weeks, the association had a production-ready data pipeline that automatically integrated data from multiple source systems and maintained a consolidated customer dataset.
Key outcomes included:
- Consolidation of customer records by more than 11 percent through intelligent deduplication
- Visibility into cross-channel customer engagement across wagering, ticketing, and other activities
- Identification of data quality gaps that informed vendor and process improvements
- Executive dashboards that quantified overlap, confidence levels, and remaining opportunities
For the first time, leadership could see how customers interacted across the organization’s full ecosystem and evaluate decisions using evidence rather than assumptions.
Enabling experience transformation and future growth
With a unified customer view in place, the association can:
- Define financial KPIs based on real customer behavior
- Develop targeted marketing and loyalty strategies
- Identify high-value multi-channel guests
- Align capital investments with measurable customer impact
- Support future analytics, personalization, and AI-driven recommendations
The platform also establishes a long-term foundation for continued innovation as the association evolves its customer experience strategy.
A strategic shift, not just a technical project
This engagement represented a shift in how the association understands and serves its customers.
Powered by Databricks and modern cloud architecture, the platform connects previously fragmented data and establishes a reliable identity foundation. The association can now invest in facilities, experiences, and engagement initiatives with greater confidence and clarity.
Unifying datasets resulted in a stronger basis for strategic decision-making and intentional growth.
Find out what a Unified Customer View could do for your business.
