Cut Manual Work with Zendesk Custom Objects

A company with high ticket volume aggregates incident information from multiple sources — third-party providers, collection agencies, legal partners, payment services (e.g., PayPal), and its own backend. Before the project, relevant details were copied into tickets manually. Together with Leafworks, the team introduced Zendesk Custom Objects and rule-based detection to automatically identify key details — such as sender, organization type, and case reference number — and store them as structured ticket fields. The result: significantly less manual effort, faster initial assessment, and more reliable reporting.
 

Starting point & challenge

Many inputs, many sources — one case, sometimes several tickets:

• Senders and content arrive from providers, collection agencies, legal partners, payment services (e.g., PayPal), and the internal backend.

• Critical details like reference numbers, identifiers, or sender information are buried in email bodies, attachments, and reply threads.

• Manual review and transfer into ticket fields was time-consuming and error-prone.

Target state

• Consistent, structured data per case directly in the ticket.

• Automated detection and assignment of sender, organization type, and reference number.

• Far less manual effort in first-level support and faster prioritization.

Solution: custom objects & rule-based extraction

1) Knowledge base with Zendesk custom objects
A maintainable knowledge base inside Zendesk:

• Organizations and their types are maintained (e.g., provider, collection agency, legal partner, payment service).

• Detection rules per organization define what to look for (e.g., “reference number: 9 digits,” allowed prefixes such as “Ref.:”).

2) Extraction from multiple sources
An extraction layer analyzes emails, attachments, and ticket text. It identifies:

• Sender/organization and the corresponding type (provider, collection agency, legal partner, payment service).

• Reference numbers, including format variations and prefixes.

3) Automatic ticket enrichment

Detected values are written back as structured fields (e.g., organization, type, reference number).

This creates a reliable data foundation for triage, automation, and reporting.

Results

• Less manual data entry in day-to-day operations.

• Faster triage thanks to reliable, structured ticket fields.

• Lower error rates in reference numbers and sender assignment.

• Better analytics through consistent data (e.g., volumes by type, handling times by organization).

Interested?

Get in touch if you face similar challenges — we’ll show you live how to enrich Zendesk tickets automatically and speed up your processes.