Overview
Industry
Healthcare
Region
North America
Company Size
201-500
Featured Solution
AI-Powered HubSpot Data Deduplication, Standardization & Enrichment
About the Client
The client is a U.S.-based healthcare organization focused on transforming primary care through a whole-person, value-based approach. Its advanced primary care model combines accessible care, behavioral health, and data-driven insights to address patients’ needs. Operating at scale across multiple sites, the organization relies on a unified CRM to manage patient and partner relationships.
When CRM Data Quality Becomes a Growing Operational Burden
A CRM is only as reliable as the data inside it. For marketing and sales teams, duplicate contact records aren't just a data-cleanup issue; they affect day-to-day processes and outputs.
For this healthcare network, the problem had become difficult to manage. Their HubSpot database contained duplicate contact records that could only be identified and merged one-by-one, manually.
Each deduplication effort required someone to find the duplicate, determine the correct primary record, and complete the merge. The process was time-consuming and carried the risk of incorrectly merging two different contacts.
The Challenges of Managing CRM Data Manually
As the volume of CRM data increased, the client faced several challenges:
Time-Consuming Data Cleanup
Every duplicate had to be spotted and resolved individually, consuming hours of ops time each week.
Risk of Incorrect Merges
Without a structured review process, merging records carried the constant risk of combining two distinct contacts, thus corrupting data.
Degraded Data Quality
Duplicate records skewed segmentation, inflated contact counts, and made reporting unreliable for both marketing and sales.
No Auditability or Control
There was no systematic way to track which records had been reviewed, flagged, or merged, making governance difficult.
The Solution: AI-Powered CRM Data Management
Grazitti implemented an automated HubSpot framework to identify, review, merge, and enrich contact records while maintaining control over the data management process.
- Automated Duplicate Identification
The framework continuously scans the HubSpot database using strict matching logic across name, email, company, and phone data to identify potential duplicate records.
- Intelligent Primary Record Selection
The system applies predefined criteria based on data completeness and contact engagement history to automatically designate the authoritative record.
- Tag-Then-Review Workflow
Identified duplicates are tagged and held in a 1-3-day human review window before any merge executes. This preserves oversight without adding operational friction.
- AI Agent-Powered Contact Enrichment
Missing or incomplete contact fields are automatically filled using AI-driven enrichment. The merged record doesn’t just eliminate redundancy but also comes out more complete than either source record.
- Automated Merge with Safety Controls
Once reviewed and approved, HubSpot’s native merge functionality executes the consolidation. Edge cases, such as bounced emails, conflicting field values, and merge conflicts, are handled through built-in exception logic.
- Custom CRM Properties for Tracking and Auditability
Every action is logged through custom HubSpot properties, giving ops teams a full audit trail of what was flagged, reviewed, and merged.
- Purpose-Built Tech Infrastructure
The solution was built on HubSpot CRM and Workflows, including Custom Code Actions powered by Node.js, and connected to HubSpot’s APIs via a Private App configuration.
The Impact: Cleaner Data With Less Manual Effort
The shift from manual deduplication to an automated, governed framework transformed CRM data management into a controlled and scalable process. Automated duplicate identification and merging significantly reduced the team's data-cleaning workload. AI-powered enrichment improved contact completeness, while human review helped maintain confidence in the accuracy of merged records.
"The automated HubSpot deduplication framework has significantly improved the way we manage contact data. The combination of strict matching rules, human review, and automated merging gives us greater confidence that duplicate records are handled accurately while reducing the manual effort required by our team. The solution provides a scalable and controlled approach to maintaining high-quality CRM data."
Highlights
8-10
Hours Saved Per Week of Manual Data Review
100%
Automated Duplicate Data Management
1-3
Day Human Governance Window
80%
Improvement in Data Quality
Conclusion
What started as a data quality problem became an opportunity to rethink how CRM governance works at scale. By replacing a purely manual process with an automated, auditable, and safety-conscious framework, Team Grazitti didn't just fix the client’s duplicate problem but also built the infrastructure that prevents it from returning. Clean data, governed merges, and enriched contact records are now defaults, not outcomes of periodic cleanup sprints.
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