Democratizing Data Through AI and Storytelling

This universal data literacy challenge created a critical usability gap in our segmentation builder. Marketing managers who clearly understood their targeting needs couldn't navigate our technical interface, forcing them to rely on customer success teams for basic segment creation and turning a self-service feature into a manual support bottleneck.

Feature analytics revealed customers heavily relied on customer success teams for segment creation instead of using the tool independently. Our segmentation builder's technical database language created an barrier; customers could articulate their business needs but couldn't translate these into database queries. This turned a core self-service feature into a support bottleneck.

Our business model required transitioning from retainer-based customer success work to self-service product usage. The current technical barriers were preventing this shift, customer success teams handled basic segment requests while customers experienced delays in campaign execution.

The three identified problems required solutions at different levels of complexity and implementation timelines. Some offered quick wins that could be deployed immediately, others would fundamentally change how data engineers worked, and the most strategic would impact our entire business model and customer relationships.

I approached these challenges through a three-tiered solution strategy, working from high-level architecture down to granular workflow improvements.

With customers creating segments independently, a new critical issue emerged: segment counts varied dramatically across different systems. The same audience showed 1,000 people in segmentation but only 800 in the original source system, creating confusion and eroding trust in our platform's reliability.

The root cause was multifaceted: manual identity merging processes, inconsistent data sync schedules, and human errors, but fundamentally, we lacked an automated identity resolution system, to handle duplicate records across data sources.

Customer confidence in our platform was deteriorating as they questioned which numbers to trust for campaign planning. Marketing teams couldn't reliably execute campaigns when facing inconsistent data, and potential duplicate targeting threatened campaign effectiveness and customer experience. This data accuracy crisis undermined our core CDP value proposition and risked customer churn despite our segmentation success.

Strategic Implementation Process Working with our product management team and CPO, we decided to implement the identity resolution feature despite knowing it would be time and resource-intensive work requiring extensive testing. We established a duplicated environment to test the new Golden Record system while maintaining the old data processing workflow, allowing us to compare results and ensure data integrity before full deployment.

Automated Identity Unification Implemented automated Golden Record system that merged customer identities across all data sources without manual intervention. The system automatically detected and unified duplicate profiles from CRM, ticketing, email platforms, and social media, while preserving original source data untouched for transparency and audit purposes.