Groove-to-Zammad migration requires API-based extraction (JSON export or GraphQL), a 2-step ticket+article creation process, and careful handling of attachments, timestamps, inline images, and state mapping.
Migrating from Groove to Zammad is a fully custom API-to-API data translation job — there is no native migration wizard, no vendor-provided connector, and no built-in import path between the two platforms. Groove's flat, shared-inbox data model (conversations, messages, customers) must be remapped to Zammad's stricter relational model, where agents and customers share a single User entity, Organizations have no Groove equivalent, and every communication becomes a typed Article within a Ticket. Groove's built-in JSON export and its v1 REST and v2 GraphQL APIs serve as extraction sources, while Zammad's REST API is the sole ingestion target. A full migration requires a staged extract-transform-load workflow with import mode enabled in Zammad, plus a delta-sync pass at cutover to capture tickets modified during the migration window.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
Key distinction
Groove Folders are organizational containers that hold conversations. Zammad Overviews are dynamic, filter-based views — they don't "contain" tickets. If your team uses Groove Folders as a core workflow mechanism, convert them to Tags or a custom field in Zammad to preserve that grouping logic. The folder name becomes the tag value; apply it to every ticket that was in that folder during extraction.
For the most reliable extraction, use the built-in JSON export for bulk
For the most reliable extraction, use the built-in JSON export for bulk ticket/message/customer data, and supplement with API calls for entities not included in the export (folders, groups, mailboxes, KB articles). This hybrid approach minimizes API calls and avoids rate-limit headaches.
Enable import mode before your first ticket POST
Disabling it partway through a migration does not retroactively suppress notifications for tickets already created. For Zammad SaaS (hosted), contact Zammad support to confirm whether import mode is accessible and how to enable it on your instance.
Preserve source IDs explicitly
Store Groove conversation IDs, contact IDs, company IDs, and mailbox names in Zammad custom Object Attributes from the first import batch. Do not rely on subject lines or ticket numbers to find records later.
Timestamp preservation
To maintain original creation dates, authenticate against the Zammad API using an Admin token. This lets you pass created_at and updated_at timestamps. Without Admin privileges, Zammad stamps all imported records with the current date. Prove this works in a sandbox on your exact Zammad version before starting the production import.
Never test historical article replay against a live outbound mailbox
Zammad's docs warn that internal: true does not make an email-type article silent — if you create an article with type: email, that email can still be sent. Do test imports with outbound mail disabled or use type: note during history replay to avoid generating duplicate customer mail. Import mode (described above) suppresses this, but verify it is active before proceeding.
Do not skip inline image processing
It is computationally expensive and slows down the migration script, but failing to rewrite inline image URLs results in permanent data loss once the source system is decommissioned.
If you have fewer than ~50 KB articles, manual copy-paste into Zammad's KB editor is
If you have fewer than ~50 KB articles, manual copy-paste into Zammad's KB editor is often faster than scripting it. For larger KB volumes, script the extraction and creation, but treat KB migration as its own workstream with its own validation pass — do not bundle it into the ticket migration script.
We strongly advise against direct database imports
Zammad's internal ID relationships, caching layers, and Elasticsearch indexing depend on records being created through the application layer. Direct DB writes produce orphaned records in the search index, break ticket counters maintained in Redis, and corrupt the histories table that powers Zammad's timeline view. The API is the supported path and the only one that produces a consistent system state.
The runbook
Work top to bottom. Tick steps as you go — your progress is saved in this browser.
01 Discovery Establish why you are moving, what "done" means, and who signs off.
Objective A written scope with agreed success criteria, a named owner per workstream, and a budget approved by finance.
Keep these open
-
Pull the real numbers out of Groove
Export counts for tickets (open and closed separately), contacts, organisations, attachments, macros, triggers, automations, views and SLA policies. Note the oldest ticket date — history depth drives the whole timeline. Estimating from memory is the single most common cause of a blown migration window.
Data Profiler Get real record counts instead of estimating from memory -
Decide what history actually moves
Agree a cut-off with the support lead: all history, last 24 months, or open tickets plus a read-only archive. Every extra year of closed tickets adds API time and cost without adding much agent value. Get this in writing — it is the decision people relitigate mid-cutover.
A "move everything" default is what turns a two-week migration into a two-month one.
COI & ROI Calculator Build the 36-month business case you will need for sign-off -
Confirm Zammad can hold your support model
Walk your current workflow through Zammad: multi-brand, business hours, SLA targets, CSAT, side conversations, public vs internal notes, and any channel you depend on (voice, chat, WhatsApp, social). List anything with no native equivalent — those are project risks, not configuration details.
-
Build the business case
Model licence delta, migration effort, agent retraining, and the cost of staying put (Cost of Inaction). Executives approve a number, not a plan, and you will be asked for it again at the go/no-go.
Helpdesk Migration Planner Turn ticket volume into a dated Groove → Zammad timeline -
Name owners and set the go/no-go date
One named owner each for data, configuration, integrations, and agent enablement, plus a decision-maker who can call a rollback. Put the go/no-go meeting in calendars now, 48 hours before the freeze.
Don't move on until
- Record counts confirmed for tickets, contacts, organisations and macros
- Success criteria signed off by the support lead
- Freeze window provisionally booked with the business
02 Data Audit Find out what is actually in the data before you try to move it.
Objective A profiled, cleaned export with every quality defect either fixed at source or explicitly accepted.
Keep these open
-
Take a full Groove export and profile it
Export to CSV or JSON and profile every file: row counts, null rates per column, distinct values, and type consistency. Compare row counts against the API totals from Discovery — a gap here means your export is silently truncated, usually by pagination.
Data Profiler Profile the Groove export for nulls, outliers and type drift -
Validate file structure before anyone writes a transform
Check delimiters, quoting, encoding (expect UTF-8, watch for BOMs and Latin-1), duplicate headers, and embedded newlines in ticket bodies. Ticket descriptions with raw newlines and commas break naive CSV parsers and silently shift columns.
A single unescaped quote in one ticket body can shift every subsequent column without any error.
CSV Validator Catch broken headers and ragged rows in the raw export -
Inventory PII and set retention
Scan for emails, phone numbers, payment card fragments, national IDs and anything else regulated in ticket bodies and custom fields — support tickets are where customers paste things they should not. Decide what gets migrated, masked, or dropped, and record the legal basis.
Ticket bodies and attachments routinely contain card and ID data that never appears in a structured field.
PII & Compliance Scanner Find regulated fields before they land in a new system -
Quantify duplicates, orphans and dead references
Count duplicate contacts (same email, different casing), tickets whose requester no longer exists, organisations with no members, and attachments whose parent ticket is gone. Fix these in Groove where you can — migrating them just moves the mess.
Data Cleaner Strip empty rows, stray whitespace and dead columns -
Clean and normalise the export
Trim whitespace, drop empty rows and columns, normalise casing on emails and tags, and standardise every timestamp to UTC ISO 8601. Timezone drift is invisible at load time and shows up weeks later as SLA reports nobody can reconcile.
-
Produce a masked copy for sandbox work
Generate a realistic but fake version of the export for testing and for any vendor who needs sample data. Loading real customer PII into a sandbox is a breach in most jurisdictions, and sandboxes are rarely covered by your DPA.
PII Masker Generate a safe copy for sandbox and vendor testing
Don't move on until
- Export parses cleanly with no ragged rows or encoding errors
- PII inventory complete and retention decisions recorded
- Duplicate and orphan records quantified and triaged
03 Field Mapping Turn two schemas into one signed-off mapping spec.
Objective A reviewed field-level mapping covering every object, with an explicit decision for every field that has no target.
Keep these open
-
Generate the first-pass Groove → Zammad field map
Start from an automated match on both schemas, then review every row by hand. Automated matching gets the obvious 70% right and is confidently wrong on the rest — especially anything named "type", "status" or "custom_field_1".
Schema Mapper Opens pre-loaded with the Groove → Zammad field pair -
Map status, priority and channel values, not just field names
Enumerate every value in each picklist on both sides and map them explicitly. Value-level mismatches are the defect class that survives all the way to production because the field itself mapped fine — a ticket that should be "Pending" arriving as "Open" reopens SLA clocks.
Statuses with no target equivalent (on-hold, pending-customer) need a policy decision, not a best guess.
-
Decide how custom fields land
Create the target custom fields first, matching type exactly (a dropdown mapped to free text can never be mapped back). Where Zammad has no equivalent, decide between a new custom field, a tag, or a note appended to the ticket body — and record which.
-
Resolve identity and threading
Decide how source IDs are preserved — most platforms will not let you set the primary key, so keep the original ID in a custom field. Without it, reconciliation becomes fuzzy matching and every future support question about an old ticket is unanswerable.
Losing the original ticket ID makes reconciliation and rollback effectively impossible.
-
Plan attachments, inline images and threading order
Confirm size limits, allowed MIME types, and whether inline images survive as attachments or need rehosting. Decide the comment ordering and author attribution rules: comments loaded out of order, or all attributed to the API user, destroy the conversation history agents rely on.
-
Freeze and sign off the mapping spec
Version the spec, walk the support lead through it row by row, and get explicit sign-off. Any change after this point goes through change control — mid-flight mapping edits are how partial loads happen.
Don't move on until
- Every source field is mapped, deliberately dropped, or parked in a custom field
- Status, priority and channel value maps agreed with the support lead
- Mapping spec version-controlled and signed off
04 Test Migration Prove the pipeline on a small, representative slice.
Objective A pilot load into a Zammad sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
-
Stand up a Zammad sandbox that matches production config
Create the custom fields, groups, brands, business hours and SLA policies first. A pilot into a default sandbox tests nothing, because the failures you care about are all configuration mismatches.
-
Pick a deliberately nasty pilot sample
Take 500-1000 records chosen for difficulty, not convenience: the longest ticket threads, tickets with the most attachments, non-Latin character sets, merged and split tickets, deleted requesters, and every status value. A clean random sample proves only that easy records are easy.
-
Run the load with masked data and instrument everything
Log every API request and response with its source record ID. When 40 records fail out of 10,000 you need to know exactly which ones and why, without re-running the whole batch.
PII Masker Never load real customer PII into a sandbox -
Measure real throughput against the rate limit
Record achieved records-per-hour under Zammad's actual rate limits, including retries and backoff. Extrapolate to the full volume: if the maths says the full load exceeds your freeze window, you fix that now, not on cutover night.
Published rate limits are ceilings, not throughput. Assume real-world rates are meaningfully lower once retries and backoff are counted.
-
Reconcile the pilot and triage every failure
Diff source against target on record counts and field-level values. Every discrepancy gets a root cause and a fix — "probably fine" at pilot scale becomes thousands of broken records at full scale.
Migration Validation Tool Diff the pilot batch against source before scaling up -
Put real agents in front of the pilot data
Have two or three agents work sample tickets end to end in the sandbox. They find the things reconciliation cannot see: unreadable threading, missing context, macros that no longer make sense. Fix the mapping, then re-run.
Don't move on until
- Pilot batch reconciles to 100% on record counts
- Agents have reviewed sample tickets and confirmed they are workable
- Measured throughput extrapolates to a viable full-load window
05 Cutover Execute the switch inside a controlled, reversible window.
Objective All in-scope data live in Zammad, agents working in the new system, and a rollback path that stayed available throughout.
Keep these open
-
Pre-load history before the freeze
Load closed tickets and contacts days or weeks ahead while Groove stays live. Only open tickets and the final delta need to move inside the freeze — this is the single biggest lever on window length.
Helpdesk Migration Planner Size the freeze window from Zammad's real API limits -
Publish the runbook with times, owners and abort criteria
A timed sequence: freeze start, final export, delta load, channel switch, smoke test, go/no-go, agent switch. Name who does each step and the explicit condition that triggers a rollback. Decide the abort criteria before the night, when nobody wants to be the one to call it.
-
Freeze Groove and take the final delta
Stop new ticket creation, let agents finish in-flight replies, then export everything changed since the pre-load. Announce the freeze to the whole business, not just support — someone always tries to raise a ticket during it.
Tickets created during an unenforced freeze land in the old system and are the most common source of permanently lost data.
-
Load the delta and open tickets
Run the delta load, then reconcile counts before touching any channel. Do not repoint email until the delta has verified — an inbound ticket arriving mid-load is far harder to untangle than a few extra minutes of freeze.
Migration Validation Tool Confirm the final delta landed before you reopen -
Repoint channels and verify with live traffic
Switch email forwarding and MX or connector settings, update chat widgets and web forms, and re-authorise integrations. Then send real test tickets through every channel and confirm each lands, routes and triggers the right automation.
Email forwarding changes can take up to a full DNS TTL to propagate — check the TTL days in advance and lower it if needed.
Cron Expression Builder Schedule the delta syncs that run through the freeze -
Run the go/no-go and switch the agents
Walk the exit criteria with the decision-maker, call it explicitly, then move agents over with a named person on hand for the first few hours. Keep Groove read-only rather than cancelled — cancelling the old contract on day one removes your only fallback.
-
Freeze configuration
Stop making changes to Groups, Tags, and Users in both systems.
-
Enable live outbound
Only after validation should you enable live outbound email behavior on Zammad and disable import mode.
Groove → Zammad specifics
- Primary sync
- Run the migration script to move your historical data. This can take days depending on attachment volume. Your team continues working in Groove during this phase.
- DNS and email routing
- Lower the TTL on your DNS records (to 60–300 seconds) at least 48 hours before cutover. Update email forwarding rules to route incoming mail to Zammad.
- Delta sync
- Run a final script using Groove's GraphQL API to fetch only conversations created or updated since the primary sync began. This catches tickets handled during the primary migration window.
- Elasticsearch reindex
- Trigger a full reindex after migration completes and import mode is off: ``bash bundle exec rails searchkick:reindex CLASS=Ticket bundle exec rails searchkick:reindex CLASS=TicketArticle ``
Don't move on until
- Full historical load complete and counts matched
- Inbound channels repointed and verified with live test tickets
- Rollback decision point passed explicitly, not by default
06 Validation Prove the migration is complete, then close it out.
Objective Documented evidence that data, workflow and reporting all survived, and a signed acceptance.
Keep these open
-
Run the full reconciliation
Compare source and target on every object: total counts, counts by status, counts by group, attachment counts, and field-level spot checks on a random sample. Produce one report you can hand to an auditor.
Migration Validation Tool Reconcile Groove and Zammad record-for-record -
Verify field completeness, not just record counts
Re-profile the loaded data and compare null rates per field against the source profile. Matching record counts with a field that silently arrived empty is the failure mode counts alone will never catch.
Data Profiler Prove field completeness held up through the load -
Rebuild reporting and compare against baselines
Recreate your core dashboards — volume, first response time, resolution time, CSAT — and compare to pre-migration figures for the same period. Explain every variance; a changed SLA calculation is a real finding, not a rounding error.
SLA and first-response metrics are usually recalculated from the loaded timestamps, so they will differ if any timestamp mapping was approximate.
-
Test the workflow layer end to end
Fire every trigger, automation, SLA escalation, macro and notification with a live ticket. Workflow does not migrate — it gets rebuilt — so it is untested until someone has actually watched it run.
-
Confirm compliance and produce the audit trail
Re-scan the loaded data for regulated fields, confirm retention and deletion policies are configured in Zammad, and file the evidence with your PII decisions from the audit phase.
PII & Compliance Scanner Produce the compliance evidence your auditor will ask for -
Sign off, then decommission on a schedule
Get written acceptance against the Discovery success criteria. Keep Groove read-only for an agreed period (30-90 days is typical), take a final archive export, and only then cancel. Diarise the decommission date so it does not quietly renew.
Groove → Zammad specifics
- Validation
- Spot-check complex tickets. Verify private notes remained internal (type: note, internal: true), attachments open correctly, and timestamps reflect the original interactions. Compare ticket and article counts between source and target using the APIs, not just the UI.
Don't move on until
- Full reconciliation report attached to the project record
- Reporting baselines match pre-migration figures within agreed tolerance
- Formal acceptance signed and archive retention scheduled
Field mapping reference
The field-by-field mapping for each object. Use this as the starting point for your mapping spec.
Entity Equivalent
| Groove field | Zammad field | Notes |
|---|---|---|
| Conversation | Ticket | 1:1 mapping. Preserve Groove conversation ID in a custom field. |
| Message | Article | Messages become Articles; type must be set explicitly |
| Customer | User (role: Customer) | Zammad merges agents and customers into a single User model |
| Agent | User (role: Agent) | Same User model, different role |
| Mailbox | Group | No direct 1:1; map to Groups for routing and permissions |
| Folder | Overview or Tag | Zammad Overviews are dynamic filter-based views, not data containers |
| Group (Groove) | Group membership | Different semantics — Groove Groups are agent collections, Zammad Groups carry queue behavior |
| Tag | Tag | Direct mapping |
| Attachment | Attachment | Must be base64-encoded for Zammad's API |
| Company | Organization | Groove has no strong org entity; you may need to create these fresh |
| Merged conversation | Import note on parent ticket | Do not create as standalone ticket |
| KB Article | KB Answer | Locale-bound in Zammad; separate migration required |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Tickets (Conversations) | medium | Groove conversations map 1:1 to Zammad Tickets at the structural level, but merged conversations require special handling — they must not be created as standalone tickets and should instead be represented as import notes on the parent ticket. |
| Messages (Articles) | medium | Each Groove message must be created as a Zammad Article with an explicitly set type (email, note, phone, web), and the author must already exist as a User in Zammad before the article can be written. |
| Customers (Users) | medium | Groove Customer records must be created as Zammad Users with the Customer role, and since Zammad enforces a unified User model, duplicate email addresses across agents and customers will cause conflicts that must be resolved before import. |
| Agents (Users) | low | Groove Agents map cleanly to Zammad Users with the Agent role, but agent permissions and group memberships must be reconfigured manually in Zammad after user records are created. |
| Attachments | high | Attachments must be individually fetched from Groove, base64-encoded, and posted to Zammad's API for each article, making this the most operationally expensive step and the most likely to fail at scale due to API timeouts or rate limiting. |
| Organizations (Companies) | high | Groove has no strong organization entity, so company groupings must be inferred from customer data and created fresh in Zammad, with no guaranteed source of truth for org-to-customer relationships. |
| Tags | low | Tags have a direct mapping between Groove and Zammad and can be applied to Tickets via the API with minimal transformation logic. |
| Mailboxes (Groups) | medium | Groove Mailboxes have no direct 1:1 equivalent in Zammad and must be mapped to Zammad Groups, which carry routing and permission semantics that require deliberate configuration decisions before migration begins. |
| Folders (Overviews or Tags) | medium | Groove Folders are data containers, while Zammad Overviews are dynamic filter-based views that do not hold tickets, so folder membership logic must be converted to Tags or custom fields and applied to every affected ticket during extraction. |
| Custom Fields | medium | Groove custom fields on conversations and customers must be mapped to equivalent custom fields pre-created in Zammad's admin interface before ticket import, and any type mismatches between platforms will cause data loss or import errors. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
No Native Migration Path
Zammad's documented migration options cover Freshdesk, Kayako, OTRS, and Zendesk — not Groove — so all transformation logic must be written from scratch as custom ETL scripts.
Flat-to-Relational Data Model Gap
Groove's flat conversation-and-message structure must be transformed into Zammad's relational model where every entity (agent, customer, ticket, article) must be explicitly linked to a pre-existing User or Organization record.
User and Organization Bootstrapping
Zammad merges agents and customers into a single User model differentiated by role, and introduces an Organization entity that has no native equivalent in Groove, requiring these records to be created and mapped before any ticket import begins.
Attachment Encoding Requirements
All file attachments must be base64-encoded for ingestion via Zammad's REST API, requiring a dedicated extraction and re-encoding step for every attachment on every Groove message.
Groove REST API Pagination Ceiling
Groove's REST API silently caps results at page 50 with a maximum per_page of 50, meaning accounts with more than 2,500 conversations will have records silently missed if REST-only extraction is used without supplementing via JSON export or the GraphQL API.
Notification Suppression Requirement
Zammad's import mode must be enabled before the first ticket POST to prevent the system from sending outbound notification emails to all customers and agents during bulk historical ticket creation.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Is there a native Groove to Zammad migrator?
No. Zammad's official migration sources are Freshdesk, Kayako, OTRS, and Zendesk. Groove projects require a custom migration script using Groove's JSON export or API and Zammad's REST API, or an engineer-led migration service.
Does Groove export data as CSV or JSON?
Groove exports conversation data as JSON only, matching its v1 Tickets API format. There is no CSV export option. The export includes ticket metadata, messages, and customer information but does not include knowledge base articles.
How do I preserve timestamps when importing tickets into Zammad?
Pass the original created_at value from Groove in ISO 8601 format when creating tickets and articles via Zammad's REST API. An admin-level API token is required to set custom timestamps. Omitting this field causes Zammad to stamp records with the current server time.
How long does a Groove to Zammad migration take?
For accounts under 5,000 tickets, expect about one week including script development and testing. For 50,000+ tickets, plan for 2–4 weeks. Attachment-heavy accounts take longer due to base64 encoding overhead in Zammad's API.
Can I migrate Groove knowledge base articles to Zammad?
Yes, but there is no bulk export or import path. Extract articles from Groove via API, create the category structure in Zammad's knowledge base, and push articles through Zammad's KB API endpoints. For fewer than 50 articles, manual copy-paste is often faster than scripting.