What success metrics do we track for deduplication?

Percent fewer duplicates after dedupe runs, days to resolve a contact mismatch, and routine scheduled CSV or JSON exports of AI audit trails for governance.

Which systems typically feed the integration backbone?

Typical sources include CRM, property platforms, pest tickets, campaign platforms, AI enrichment layers, billing systems like HubSpot or QuickBooks, and field platforms such as ServiceTitan or Jobber.

How are stable IDs and provenance handled in the data flow?

Use stable IDs as the primary join key. Every record receives a timestamp and a provenance tag that identifies the source system and job id.

What deterministic rules do we use to reconcile duplicate service accounts?

Apply rules in order: account ID then address then owner contact then a normalized fallback. Keep rules short, testable, and preserve ticket history; flag owner confirmation where contact mismatches exist.

How are audit trails generated and who can export them?

Every matching decision is recorded with actor, action, recordId, ISO8601 time, and confidence. Audit trails are exportable as CSV and JSON for authorized roles only, and all exports are logged.

How do we prepare records for direct-mail and measure attribution?

Export a vetted CSV with canonical address fields and send to a provider like PostcardMania. Use tracking codes on postcards that map back to recordId and check the audit trail for verification and campaign timing.

What governance and testing practices are recommended?

Maintain versioned data models and transformation scripts, run unit integration and end to end tests, document rollback steps, run daily monitoring and nightly duplicate checks, and perform quarterly governance reviews.

How should merges preserve history and handle conflicting records?

Preserve historical tickets and aliases when merging, update canonical fields after verification, flag records with conflicting owners for manual review, and keep merge rules in version control with scripts that propose diffs.

Clean contact data, dedupe, and AI audit trails

TLDR

Practical, auditable data hygiene: dedupe, canonical IDs, and exportable AI logs for governance.
Automated data flow from CRM/property/pest systems with provenance and stable keys for fast mail lists.
Direct-mail readiness: vetted, canonical addresses; track ROI by linking postcard responses to recordId in audit trails.
Operational governance: versioned models, tests, scheduled exports, and secure access controls to keep campaigns timely and reliable.

Objective and Success Metrics

The goal is clear. Clean contact data. Fewer duplicate service accounts. Auditable AI logs that can be exported as CSV or JSON. These steps make direct-mail campaigns run faster and return clearer results.

Success is measured simply:

Percent fewer duplicates after dedupe runs.
Days to resolve a contact mismatch.
Routine, scheduled CSV/JSON exports of AI audit trails for governance.

The team tracks these numbers daily. Metrics guide the next actions.

A laptop displaying a deduplication dashboard with postcard campaign cards, a notepad, and a cup of coffee beside it.. Photo taken by Erik Mclean

Integration Backbone and Data Flow

Map every source system. Typical sources include CRM, property platform, pest tickets, campaign platform, and the AI enrichment layer. Stable IDs flow from these systems into an ETL or ELT pipeline. Each record gets a timestamp and a provenance tag.

Simple rules keep data steady:

Use stable IDs as the primary join key.
Normalize addresses and phone formats on ingest.
Tag every change with the source system and time.

Tools that fit this approach: Python scripts for batch jobs, Make or Zapier for lightweight automations, and AWS Lambda for serverless tasks. HubSpot or QuickBooks remain source systems where billing or contact data originate. ServiceTitan, Jobber, and other field platforms often feed property and job history.

Example pipeline steps (click to expand)

Pull new records hourly from CRM and property system.
Normalize names, addresses, phones, and emails.
Run deterministic matching rules and mark matches for review.
Write canonical records to the single-source store with provenance tags.

Google Sheets can be used for small audits. For scale, push outputs to a data warehouse and surface subsets to direct-mail systems like PostcardMania through secure exports.

Reconcile Duplicate Service Accounts

The team applies deterministic rules in order: account ID → address → owner contact → normalized fallback. Rules are short and clear so they are easy to test.

Current duplicate candidates and core differences
master_id	dup_id	diff
A-1001	D-202	address normalized, phone mismatch
A-1002	D-203	owner email alias
A-1003	D-204	address formatting only
A-1004	D-205	two different owners listed; job history overlap
Considerations: Preserve ticket history when merging. Flag owner-confirmation required where contact mismatches exist. Search keywords: dedupe, canonical id, provenance, merge audit, exportable logs.

Fixes applied and notes

Merged D-202 into A-1001. Historical tickets kept. Canonical phone updated after vendor verification.
D-203 aliased to A-1002 pending owner email confirmation. Alias preserved for mail merges.
A-1003 normalized address formats; no merge required.
A-1004 flagged for manual review due to conflicting owner records. Job history compared from ServiceTitan and internal tickets.

Nightly checks run to detect spikes in duplicate density. If duplicates exceed thresholds, an alert is sent to ops.

Automation tip: Keep merge rules in version control. Small Python scripts can run diffs and create merge proposals. Use Make or Zapier for light-weight alerts and PostcardMania exports when mail lists are ready.

Repair Contact Mismatches and Activate Audit Trails

Link contacts to stable keys. When a property or owner changes, migrate linkages and record the reason code. Every matching decision gets an audit record.

Single Source of Truth: One canonical record per account or property. All systems reference this ID for mail and billing.
Deduplication: Rules that reduce false merges. Deterministic first, probabilistic second, manual review last.
Audit Trail: Time-stamped entries that include actor, action, recordId, ISO8601 time, and confidence score.
Provenance: Which system made the change and why. Includes source system name and job id.

Exportable AI audit trails are available as CSV and JSON for governance reviews and dashboards.

Download latest AI audit CSV

Export rules:

Only authorized roles may download exports. Exports are logged.
Exports include reason codes and change provenance.
Retention policy: keep exports for the governance window required by the business.

AI match confidence: 92%

Direct-Mail Readiness and Attribution

Verified, mail-ready profiles go to the direct-mail platform. Segments use property type, pest risk, and service history. Postcard tracking ties responses back to the integration run that prepared the list.

Practical steps:

Export a vetted CSV with canonical address fields only.
Send to a provider like PostcardMania or an internal print partner.
Track response rates and tie them to the original recordId in the audit logs.

Attribution example

When a postcard generates a call, the tracking code on the postcard maps to the recordId. The audit trail shows when that record was verified and by which automation job. This makes ROI and campaign timing visible.

Operational Governance and Testing

Maintain versioned data models and transformation scripts. Run unit, integration, and end-to-end tests. Keep rollback steps documented.

78% clean-contact rate

Monitor these dashboards daily:

Clean-contact rate
Deduplication count
Mismatch resolution time
Export activity and who downloaded logs

Quarterly governance review items:

Adjust matching rules based on false positives/negatives.
Review audit schema for clarity and coverage.
Test export permissions and confirm export logs are retained.

Note: Some clients change CRMs mid-year. Build flexible migration paths that allow re-mapping of stable IDs and re-running matching rules without data loss.

direct-mail automation, data-driven decisions, auditable AI logs, CSV/JSON exports, deduplication, deterministic matching, canonical ID, provenance, single source of truth, stable IDs, ETL/ELT pipelines, address normalization, property-type and pest-risk segmentation, campaign attribution, response tracking, ROI visibility, mail-ready lists, service history aware segmentation, audit trails, export controls, role-based access, versioned data models, end-to-end testing, rollback plans, governance dashboards, reproducible processes, automation tooling