Groove-to-Zendesk migration requires the Ticket Import API for timestamp preservation, user pre-creation, and handling Groove's JSON-only export and Zendesk's plan-based rate limits (200–2,500 req/min).
A Groove to Zendesk migration is a data-model translation problem. Groove is a flat shared inbox — conversations, customers, and tags — designed for small teams. Zendesk is a relational helpdesk with organizations, users, tickets, comments, custom objects, and a deep automation layer. The structural gap is where data silently disappears: Groove has no organization concept, exports data as JSON only, and its REST API is deprecated in favor of an incomplete GraphQL v2 API. Every migration requires the Zendesk Ticket Import API for timestamp preservation, careful private-note handling to prevent data leaks, and a hybrid extraction strategy across Groove's transitioning API landscape.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
Groove has no Organization concept
If your support workflows depend on company-level grouping in Zendesk, you'll need to derive organization membership from email domains or an external source (CRM, billing system) and create Organizations in Zendesk before importing users.
Timezone handling matters
Zendesk expects timestamps in ISO 8601 format with UTC offset (e.g., 2024-01-15T14:30:00Z). Confirm whether your Groove export uses UTC or a local timezone. Mismatched timezone assumptions are a common source of subtle data corruption — timestamps can shift by hours, making conversation chronology appear out of order. Normalize all timestamps to UTC before import.
Zendesk assigns new ticket IDs
You cannot preserve Groove ticket numbers. Store a mapping table (Groove ticket number → Zendesk ticket ID) for cross-referencing during and after migration. Use external_id or a dedicated custom field to store the Groove ticket number for idempotency and audits.
For very large historical imports — around 750,000+ tickets — Zendesk explicitly
For very large historical imports — around 750,000+ tickets — Zendesk explicitly recommends setting archive_immediately: true to keep archive data from impacting active-ticket performance. (developer.zendesk.com)
Groove rate limits are not well-documented
Groove's public API documentation does not publish explicit requests-per-minute or requests-per-second limits for the REST v1 API. In practice, you'll hit rate limiting under sustained load — your extraction script should handle 429 responses with Retry-After headers and start conservatively (e.g., 1–2 requests per second) until you establish what the API tolerates. If you're extracting tens of thousands of tickets with messages, plan for extraction to take significantly longer than the math on page count alone suggests.
If a vendor or internal stakeholder describes this as a "CSV ticket migration," ask
If a vendor or internal stakeholder describes this as a "CSV ticket migration," ask exactly how they are preserving multiple comments, note privacy, timestamps, attachments, and assignee history. Native Zendesk imports are not designed for that. (support.zendesk.com)
Verify what gets migrated
Always run a test migration with a small sample and manually audit the output. Check that multi-message threads import as separate comments, not a single concatenated block. Confirm attachments actually transferred — not just placeholder references.
KB migration has its own field mapping. Key fields to handle
article title, body (HTML), author_id (resolve to Zendesk user), created_at, updated_at, publish status (draft vs. published), and locale. Groove KB categories map to Zendesk Guide sections, and any category hierarchy needs to be flattened or restructured to fit Zendesk's Category → Section → Article model. If your Groove KB uses labels or tags for navigation, map those to Zendesk article labels.
Getting a Zendesk sandbox
Zendesk sandbox environments are available on Professional plans and above. If you're on a Team or Growth plan and don't have access to a sandbox, create a separate Zendesk trial instance for testing — it gives you a clean environment to validate your migration without risking production data. Delete the trial after cutover.
Bulk deletion is not straightforward
If you need to roll back imported tickets from Zendesk, be aware that Zendesk's bulk delete endpoint (DELETE /api/v2/tickets/destroy_many) accepts a maximum of 100 ticket IDs per request and is subject to the same account-wide rate limits. For a large import (10K+ tickets), a full rollback deletion can take hours. Closed and archived tickets may need to be un-archived before deletion. Plan your rollback strategy around these constraints — the groove_migration tag makes it possible to identify records, but removing them at scale requires its own scripted pipeline.
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
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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 Zendesk can hold your support model
Walk your current workflow through Zendesk: 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.
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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 → Zendesk 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
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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.
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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
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Generate the first-pass Groove → Zendesk 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 → Zendesk 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.
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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 Zendesk has no equivalent, decide between a new custom field, a tag, or a note appended to the ticket body — and record which.
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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.
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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.
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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.
Groove → Zendesk specifics
- Big bang
- simplest mentally, highest operational risk
- Incremental / delta
- best when support cannot pause, using Groove webhooks to capture changes during the migration window
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 Zendesk sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Zendesk 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.
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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.
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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 Zendesk'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.
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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 Zendesk, agents working in the new system, and a rollback path that stayed available throughout.
Keep these open
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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 Zendesk'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.
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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.
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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.
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
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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 Zendesk 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.
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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.
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Confirm compliance and produce the audit trail
Re-scan the loaded data for regulated fields, confirm retention and deletion policies are configured in Zendesk, 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.
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Rebuild automations
Groove rules don't transfer. Recreate them as Zendesk triggers, automations, and macros. Audit every Groove rule and map it to Zendesk's trigger conditions.
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Configure Views
Replace Groove Folders with Zendesk Views. Use the same filter logic, adapted to Zendesk's field names.
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Update integrations
Any tool that pointed at Groove's API (Zapier zaps, Slack notifications, CRM syncs) needs to be re-pointed to Zendesk webhooks or API.
Groove → Zendesk specifics
- Train agents
- Zendesk's agent workspace is structurally different from Groove's inbox. Run 2–3 training sessions focused on ticket handling, macros, and the new search interface.
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.
Object Object
| Groove field | Zendesk field | Notes |
|---|---|---|
| Customers | Users (role: end-user) | Map by email address. Groove stores name + email; Zendesk Users support phone, external_id, custom user fields. |
| (no equivalent) | Organizations | Groove has no company/org concept. Derive orgs from email domains or external CRM data. |
| Conversations | Tickets | One Groove conversation = one Zendesk ticket. |
| Messages | Comments (on Tickets) | Each message in a Groove thread becomes a Comment. Preserve public/private flag and author_id. |
| Notes | Comments (private) | Groove internal notes → Zendesk private comments (public: false). |
| Agents | Users (role: agent) | Map by email. Pre-create agents in Zendesk before importing tickets. |
| Tags | Tags | Direct 1:1 mapping. Zendesk tags are lowercase, no spaces — normalize before import. |
| Folders | Views or Tags | Groove Folders are saved filters. Rebuild as Zendesk Views or flatten into tags. |
| Mailboxes | Brands or Groups | Depends on your Zendesk setup. One mailbox per brand, or route via groups. |
| Attachments | Attachments | Must be uploaded to Zendesk first, then referenced by token in the ticket import payload. |
| KB Articles | Help Center Articles | Separate migration via Help Center API. Category → Section mapping required. |
Groove Zendesk
| Groove field | Zendesk field | Notes |
|---|---|---|
| title / subject | subject | Direct mapping |
| created_at | created_at | Requires Ticket Import API (not standard API). Groove exports timestamps in ISO 8601 format — confirm UTC offset handling and ensure your script normalizes to UTC before import if needed. |
| updated_at | updated_at | Requires Ticket Import API |
| state (opened/pending/closed/unread) | status (new/open/pending/solved/closed) | Map: opened→open, pending→pending, closed→closed, unread→new |
| assigned_agent | assignee_id | Resolve agent email → Zendesk user ID |
| assigned_group | group_id | Pre-create groups, build ID lookup map |
| tags | tags | Normalize to lowercase, replace spaces with underscores |
| mailbox | brand_id or group_id | Depends on Zendesk architecture |
| customer_email | requester_id | Resolve email → Zendesk user ID |
| starred | Custom ticket field or tag | No native "starred" in Zendesk |
| message.body | comment.html_body | Preserve HTML. Use html_body not body for formatting. |
| message.author | comment.author_id | Resolve to Zendesk user ID |
| message.created_at | comment.created_at | Only supported via Ticket Import API |
| message.note (boolean) | comment.public | Groove note=true → Zendesk public=false |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Customers / Users | medium | Same person with multiple emails creates duplicate records; merge decision required before import. |
| Organizations | high | No Groove equivalent; must be derived from email domains or external CRM data. |
| Conversations / Tickets | medium | State mapping gaps — Groove's "unread" has no direct Zendesk match. |
| Messages / Comments | medium | HTML body sanitization needed; very long threads may timeout during import. |
| Notes (Private) | high | Incorrect note flag mapping leaks internal context to customers permanently. |
| Attachments | medium | Groove URLs may expire; two-step upload-then-reference workflow required. |
| Tags | low | Direct 1:1 mapping with lowercase and underscore normalization. |
| Folders | medium | No Zendesk equivalent; must be rebuilt as Views with filter conditions. |
| KB Articles | medium | Category-to-section mapping required; separate Help Center API migration. |
| Starred Flag | low | No native Zendesk equivalent; use a custom field or tag as substitute. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
No Organization Concept
Groove has no company or org model. Organizations must be derived from email domains or external CRM data before Zendesk import.
API In Transition
Groove's REST v1 is deprecated and GraphQL v2 is incomplete for inbox data, requiring a hybrid extraction strategy.
Ticket ID Replacement
Zendesk assigns new ticket IDs. Groove ticket numbers cannot be preserved and must be stored in external_id for cross-referencing.
Private Note Leakage
Groove's note boolean must map correctly to Zendesk private comments, or internal context leaks to end users irreversibly.
Closed Ticket Immutability
Zendesk closed tickets cannot be reopened or updated after import. Status mapping must be verified before the import runs.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can I migrate Groove tickets to Zendesk using CSV import?
No. Groove exports conversations as JSON, not CSV. Zendesk's native data importer handles users and organizations, not tickets. To import tickets with conversation threads, timestamps, and attachments, you must use the Zendesk Ticket Import API (POST /api/v2/imports/tickets).
Does the Zendesk Ticket Import API preserve original timestamps?
Yes. The Ticket Import API lets you set created_at, updated_at, and solved_at on tickets, and created_at on individual comments. The standard Tickets API does not — it stamps everything with the current date. This makes the Import API the only viable option for historical migrations.
What are Zendesk's API rate limits for ticket imports?
Rate limits depend on your plan: Team gets 200 req/min, Growth and Professional get 400, Enterprise gets 700, and Enterprise Plus gets 2,500. The Ticket Import create_many endpoint accepts batches of up to 100 tickets. The High Volume API add-on increases limits to 2,500 req/min and is available on Growth plans and above.
What data is lost when migrating from Groove to Zendesk?
Groove Folders (saved filters) don't transfer — rebuild as Zendesk Views. The starred flag has no Zendesk equivalent. Groove ticket numbers are replaced with new Zendesk IDs. SLAs and metrics won't apply to imported tickets. Closed tickets in Zendesk are immutable after import.
When should I hire a managed migration service instead of building it in-house?
When you need full thread fidelity, attachment handling, org and assignee relationships, delta sync during cutover, or you don't have a senior developer with 2–4 weeks of bandwidth for retries, QA, and rollback planning. The hidden cost is not the first script — it's the debugging.