Overview

Deciphex, a global innovator in digital pathology and AI-driven diagnostics, was experiencing increasing friction in its use of HubSpot due to inconsistent property structures and poor data standardization. Key fields like State and Country were recorded in multiple formats, often using free-text entries, which created confusion, broken workflows, and unreliable reporting.

RevOps Automated partnered with Deciphex to implement a comprehensive CRM data model cleanup, introducing standardized dropdown fields, building automation to ensure accurate field population, and establishing clear governance over property usage. The result was a significant improvement in data completeness, consistency, and workflow performance—freeing the team from time-consuming admin and enabling reliable reporting and automation at scale.

Challenges

Before engaging with RevOps Automated, the Deciphex team faced several structural and operational issues that limited CRM performance and slowed down critical sales and marketing functions:

• Duplicate or conflicting properties – Multiple fields captured the same information, like location, but with different formats and logic.

• No data governance protocols – Users were unsure which fields to update, leading to incomplete or inconsistent data across Contacts and Companies.

• Broken workflow logic – Automation depended on specific values, and text mismatches caused failure in workflow triggers.

• Disrupted lead routing and segmentation – Conflicting field values made it difficult to route leads correctly or segment by region, impacting Revenue Operations.

The Solution

RevOps Automated implemented a full CRM data governance and hygiene strategy for Deciphex, focused on simplifying and standardizing data inputs and usage within HubSpot.

Key improvements included:

• Replacement of free-text fields with standardized dropdowns – Created new custom properties (e.g., Country, State) with consistent formatting.

• Workflow-based auto-population – Developed logic to backfill and maintain clean data using known points of information (like IP or inferred region).

• Governance on visible properties – Deprecated “red herring” fields from user views to prevent outdated or incorrect property usage.

• Automation testing & repair – Identified and fixed broken workflows caused by inconsistent data structures.

Together, these changes strengthened the entire data model, improving not only operational accuracy but also confidence in lead management, segmentation, and reporting.