Automated Contact Data Cleansing System
Table of Contents
Clean data = healthy delivery
The spec emphasizes: Dirty data can ruin inbox placement, experiments, and budgets. industry experience automates the entire process, and the operation only needs to set the rhythm. The AI agents enrich customer information, analyze personas and signals, craft personalized copy, and reply instantly.
Module
- Verification Waterfall: Upon import, the syntax, domain name, MX, and mailbox status are detected, and catch-all/risk records are isolated.
- Duplication removal and correction: Unify the company/position writing method, correct common typos, and clean up duplicate or role-based mailboxes.
- Sleep Management: Automatically mark contacts that have not been interacted with for 90/120 days, send a wake-up sequence, and suppress them if they do not respond.
- Bounce/Complaint Monitoring: Weekly dashboard (target <1% / <0.3%), once the target is exceeded, the list/activity can be located.
- Inbox Placement test: Automatically run before large batches to ensure it falls on Primary.
Suggested rhythm
- Before importing: Verification + deduplication + error correction.
- Weekly: Check bounced letters/complaints, delivery health, and remind relevant teams with smart replies.
- Quarter: Conduct deep cleaning of large lists to reduce ESP costs.
KPI
- Verification pass rate and proportion of quarantined records.
- Bounce/complaint trends, Inbox Placement changes.
- Restart ratio of wake-up activities and new Pipeline.
AI route
- Corruption prediction model, locking risk segments in advance.
- AI gives suppression suggestions, automatically adjusts the list and outputs smart writing/smart replies.
- Workflow Assistant: Use natural language to describe the cleaning target and generate operation steps, allowing the AI agents enrich customer information, analyze personas and signals, craft personalized copy, and reply instantly.
With this automation, the team can keep all thresholds in the specification in the green zone over time.