Bulk Upload Best Practices

Preparing Large Files

Before uploading a large file (5,000+ rows), invest a few minutes in data preparation. Remove duplicate rows using Excel's 'Remove Duplicates' feature or a Python/SQL deduplication step. Duplicate rows consume quota and inflate your results — a list with 30% duplicates means 30% of your credits are wasted.

Ensure all columns have consistent formatting. A phone column that mixes formats like '(212) 555-1234', '+1-212-555-1234', and '2125551234' is fine — our normalisation handles this. But a column where some cells contain full names alongside phone numbers ('John Smith — 212-555-1234') will fail to parse and that column will be skipped.

Segmenting for Better Results

If your full list is large, consider uploading it in logical segments rather than one monolithic file. Segment by data source (trade show list vs. inbound form vs. purchased list) or by geography or industry vertical. This makes it easier to compare enrichment quality across sources and identify which data acquisition channels yield the best-quality contacts.

Segmenting also lets you process the highest-priority segment first while remaining segments queue up. For Agency plan users running multiple client projects simultaneously, per-client segmentation also simplifies billing attribution and result organisation.

Column Naming Tips

While Trust Leads auto-detects column names, you can improve detection accuracy by using standard naming conventions. Our engine reliably detects all common variants, but edge cases in non-English column headers or highly abbreviated names may fall back to manual mapping.

Standard column names that are always auto-detected correctly: email, first_name, last_name, phone, company, job_title, linkedin_url, website, country, city, state. If your export tool uses different names, you can either rename columns before uploading or adjust them in the column mapping screen after upload.

• Use email (not e-mail, email_address, or emailAddr)
• Use first_name and last_name (not fullname — split names improve matching)
• Use company (not organisation, employer, or account_name)
• Use phone (not telephone, mobile, or cell)
• Avoid merged columns like 'name_email' — always one field per column

After Upload: Reviewing Results

After a large batch job completes, download the enriched CSV and run a quick quality audit before importing to your CRM. Sort by lead_score ascending to inspect the lowest-quality records first. Check what enrichment_flags are most common — this tells you which data quality issues are systemic in your source data.

A common pattern in purchased lists is a high rate of DISPOSABLE_EMAIL flags — this indicates the list vendor is supplying low-quality data. A high rate of NO_MX_RECORD flags often points to stale data (companies that have since shut down or rebranded). Use these insights to evaluate your data sources and adjust acquisition budgets accordingly.