Real-Time Personalization Platform

Delivering Real-Time Personalization Through Unified Customer Data

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Personalization Platform, Data Governance, ML Enablement, Cross-Channel Targeting, Consent Management, Customer Data Infrastructure, Scalable Experimentation

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Situation:

A large financial institution needed to support real-time personalization across its digital channels, but customer behavior data was fragmented and inconsistent. Business lines had developed independent tagging and data sharing approaches, with no unified schema or governance model. This introduced compliance risks, delayed decision-making, and limited the ability to activate downstream systems such as machine learning models and campaign orchestration platforms.

 

Opportunity:

The organization had an opportunity to unify customer data collection and sharing through a centralized behavioral data platform. This would improve compliance, reduce operational overhead, and enable personalization use cases at scale. A key goal was to establish a trusted foundation that downstream consumers, including ML and experimentation platforms, could rely on for high-quality, governed behavioral data.

 

Obstacles:

Teams used inconsistent schemas, duplicated logic, and varied in their approaches to consent enforcement. Some engineering groups were reluctant to migrate due to performance concerns or dependencies on legacy pipelines. During rollout, a downstream team attempted to bypass governance by using unvalidated legacy data for model development, reinforcing the need for clear readiness criteria and stronger alignment across compliance, engineering, and analytics stakeholders.

 

Activities:

A unified behavioral data platform was defined and delivered in close coordination with engineering, analytics, legal, and compliance teams. Shared governance practices were established, a standardized schema was adopted, and business lines were onboarded in phases to minimize disruption. To support advanced use cases, a framework was introduced to define what constituted governed data. This enabled downstream teams to assess readiness independently and reduced reliance on legacy pipelines. Internal platform teams were restructured to support cross-functional adoption, and engagement routines were introduced to resolve integration challenges early in the lifecycle.

 

Results & Impact:

The platform enabled real-time personalization across channels using consistent, compliant customer behavior data. Redundant tagging and manual workarounds were eliminated, and business lines adopted shared standards across web, mobile, and backend systems. The platform became the backbone for real-time ML personalization by delivering the high-signal behavioral inputs required for targeting, experimentation, and model development. The program provided a repeatable foundation for enabling advanced data use cases while maintaining architectural integrity and regulatory alignment.