Contact Cleaner
- Checks hard-bounce reasons such as User Not Found and Invalid Domain
- Separates email-delivery discrepancies from opt-out / consent exclusions
- Finds missing or invalid email addresses
- Detects exact duplicate rows and repeated email relationships
- Splits Customer Name into First Name and Last Name when needed
- Preserves every original row in the annotated output with clear reasons and actions
Ready to Use
0
No delivery or consent suppression rule triggeredSuppressed
0
Should not be used for emailDuplicate Review
0
Repeated-email relationships; not counted as discrepanciesOriginal records0Rows analyzed
Delivery discrepancies0Hard bounce / invalid / missing email
Source highlighted rows—Available for supported Excel highlighting
Names completed0First/last name fields filled
1
Upload your fileChoose a CSV or Excel (.xlsx) contact list.
2
Choose duplicate handlingDecide whether repeated emails should stay, be reduced, or be consolidated.
3
Analyze and downloadReview delivery issues, consent exclusions, duplicate relationships, and output files separately.
How should duplicate email addresses be handled?
Duplicate emails are treated as a separate data-quality observation. They do not increase the delivery-discrepancy count. The annotated file always keeps every original row.
Analyzing your file…
Reading file…
Cleanup breakdown
Email delivery discrepancies
These are delivery-quality issues. This is the number to compare with a source discrepancy population such as your yellow-highlighted rows.
Consent exclusions
These records may have a valid email address but should not be emailed because their status indicates an opt-out / do-not-email condition.
Duplicate & data-quality observations
Exact duplicate rows detected0
Duplicate email groups found0
Rows affected by repeated email0
Rows consolidated/removed in clean file0
Choose the output you want
Cleaned file excludes suppressed records and follows your duplicate-handling choice. Full annotated file keeps every original row and adds the delivery issue, consent status, duplicate group, cleaning status, reason, and recommended action so a person can make the final decision.
Name cleanup: existing FIRST_NAME and LAST_NAME values are preserved. When either is blank, the tool attempts to fill it from CUSTOMER_NAME. The original CUSTOMER_NAME remains unchanged.