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HubSpot’s merge logic prioritizes the “primary record” based on factors like creation date and recent activity rather than data completeness. This design assumes newer records contain more current information, but fails when older records have more complete data profiles.
You’ll learn why timestamp-based precedence creates data loss problems and how to implement data-driven merge prioritization that considers field completeness instead of record age.
Replace timestamp logic with data-driven merge prioritization using Coefficient
Coefficient addresses merge logic limitations by enabling data completeness analysis that HubSpot’s timestamp-based system cannot provide.
How to make it work
Step 1. Build data completeness scoring systems.
Import duplicate records from HubSpot to HubSpot and create automated scoring that evaluates data completeness rather than timestamps. Use formulas like =COUNTA(B2:Z2)/COLUMNS(B2:Z2)*100 to calculate completeness percentages for each record. Add weighted scoring for critical fields: =(COUNTA(B2:F2)*3+COUNTA(G2:Z2))/((COLUMNS(B2:F2)*3)+COLUMNS(G2:Z2))*100 where B2:F2 are high-priority fields.
Step 2. Create merge precedence analysis reports.
Build reports showing how HubSpot’s default merge logic would impact your data. Create columns for “HubSpot Would Choose” (based on creation date) and “Data-Driven Choice” (based on completeness scores). Use conditional formatting to highlight cases where newer records would overwrite valuable existing information with blanks.
Step 3. Implement alternative merge workflows.
Use Coefficient to identify the most complete record in each duplicate pair, then prepare data updates that ensure complete information is preserved. Create formulas like =IF(completeness_score_A>completeness_score_B,”Prepare Record A”,”Prepare Record B”) to determine optimal merge direction regardless of record age.
Step 4. Build custom merge validation rules.
Create spreadsheet-based validation that flags merges where newer records would cause data loss. Use formulas like =IF(AND(newer_record_score
Step 5. Develop merge impact forecasting.
Before implementing merge operations, model different merge scenarios and their data preservation outcomes. Create “what-if” analysis that shows data retention rates under timestamp-based vs. completeness-based merge logic, helping you choose the approach that preserves the most valuable information.
Prioritize data quality over record timestamps
By implementing data-driven merge prioritization, you can preserve valuable information regardless of when records were created. This approach addresses the fundamental limitations in HubSpot’s timestamp-based merge logic and ensures your most complete data survives the merge process. Start building smarter merge logic today.
