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“Supermetrics is a Bitter Experience! We can pull data from nearly any tool, schedule updates, manipulate data in Sheets, and push data back into our systems.”
When the source record (typically older) has more complete data than the target record, HubSpot’s default merge behavior can result in significant data loss. The platform’s merge interface allows manual field selection, but this becomes impractical for bulk operations.
You’ll discover how to intelligently handle merges when older records contain more valuable data through reverse merge preparation and automated field optimization workflows.
Optimize merge direction with intelligent data consolidation using Coefficient
Coefficient enables smart duplicate record merge strategies that preserve the most complete data regardless of which record is older or newer.
How to make it work
Step 1. Assess data completeness between duplicate pairs.
Import both duplicate records from HubSpot to HubSpot and create automated analysis to determine which record has more complete information. Use formulas like =COUNTA(B2:Z2) for the older record and =COUNTA(B3:Z3) for the newer record. Create a comparison: =IF(B1>B2,”Older record more complete”,”Newer record more complete”) to identify optimal merge direction.
Step 2. Prepare reverse merge workflows.
When the older record has more complete data, use Coefficient to copy missing information from the older record to the newer record before merging. Create formulas like =IF(ISBLANK(newer_record_field),older_record_field,newer_record_field) to consolidate the best data into the target record. This ensures data preservation while maintaining the newer record as the primary.
Step 3. Build field-by-field optimization.
Create spreadsheet workflows that identify the best value for each property across both records. Use nested IF statements: =IF(ISBLANK(B2),C2,IF(ISBLANK(C2),B2,IF(LEN(B2)>LEN(C2),B2,C2))) to automatically select the most complete value for each field. Then use Coefficient’s export capabilities to update the target record with optimized information before merging.
Step 4. Implement bulk merge optimization.
For multiple duplicate pairs where source records have better data, build automated workflows that prepare all records for optimal merging. Create batch processing that consolidates the most complete information into target records across your entire duplicate list, then export these optimized records back to HubSpot before final merge execution.
Step 5. Set up merge validation workflows.
Create systematic checks that compare data completeness between duplicates and flag cases where standard merge operations would cause data loss. Use formulas like =IF(older_completeness>newer_completeness*1.2,”Requires optimization”,”Standard merge OK”) to automatically identify merges that need preparation work.
Merge smarter, not just newer
With intelligent data consolidation workflows, you can ensure the most complete information survives every merge operation regardless of record age. These processes provide the field preservation capabilities needed to handle complex duplicate scenarios that HubSpot’s native merge functionality cannot optimize. Start optimizing your merge operations today.
