HubSpot’s data cleaning capabilities are limited to standard properties and require manual intervention for custom field duplicates. This leaves you with time-consuming manual processes that are prone to errors and inconsistencies when dealing with duplicate custom field values.
Here’s how to implement sophisticated automated data cleaning workflows that systematically identify, categorize, and resolve custom field duplicates.
Transform manual cleaning into automated workflows using Coefficient
Coefficient enables sophisticated automated data cleaning workflows that identify, categorize, and help resolve custom field duplicates without manual processes, going far beyond what HubSpot can handle for HubSpot custom properties.
How to make it work
Step 1. Set up comprehensive duplicate detection.
Import all HubSpot objects with custom fields and implement multi-layered duplicate detection using exact matching, fuzzy logic, and pattern recognition algorithms in spreadsheet formulas. This catches duplicates that simple matching would miss.
Step 2. Build intelligent cleaning rules.
Create automatic classification that categorizes duplicates by confidence level like exact, likely, or possible matches, integrate business logic that applies cleaning rules based on record age, completeness, and activity levels, and set preservation priorities to identify primary records to retain based on data quality scores.
Step 3. Configure automated cleaning actions.
Set up data standardization to automatically format custom field values for consistency, generate merge preparation with merge recommendations and field mapping suggestions, and use update automation through Coefficient’s export functionality to update secondary records with primary record references.
Step 4. Implement quality improvement workflows.
Create missing data enhancement to flag incomplete records that may be causing duplicates, apply validation rule application with business rules to prevent future duplicate creation, and set up cleanup tracking to monitor cleaning progress and measure data quality improvements.
Step 5. Schedule regular cleaning operations.
Configure daily maintenance for automated detection and flagging of new duplicates, set up weekly deep cleaning with comprehensive duplicate analysis and resolution recommendations, and create monthly quality reports that summarize cleaning activities and data quality trends.
Step 6. Integrate cleaning results with HubSpot.
Export cleaned data back to HubSpot with standardized formatting, add quality scores as custom properties to indicate data quality levels, and export cleanup instructions with specific merge and cleanup guidance for manual review when needed.
Maintain high data quality standards automatically
While Coefficient can identify and prepare duplicates for cleaning, complex record merging may require HubSpot’s native merge functionality for complete automation. This systematic approach transforms manual, error-prone duplicate cleaning into a repeatable process. Start automating your data cleaning workflows today.