Zendesk to Front migration requires API-level extraction (CSV won't work), careful field mapping from tickets to conversations, and rate limit orchestration across both platforms to avoid silent data loss.
Migrating from Zendesk to Front is a data-model translation problem. Zendesk is ticket-centric with structured records, statuses, and SLAs. Front is conversation-centric — everything lives as threaded messages in shared inboxes with collaboration via comments. The mapping between these two models is not one-to-one. Front has no native knowledge base, no priority field, no ticket type, and a simpler status model. Every structural gap is where data silently disappears without planning. CSV exports cannot be used for ticket history migration. The only viable path is API-level extraction from Zendesk with rate-limit orchestration across both platforms — Zendesk's 10 req/min export cap and Front's 50–500 req/min company-wide limit.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
Front's native Zendesk importer is not actively maintained
Front's own documentation states: "we recommend using one of our migration partners" and notes the built-in importer is available "at no additional cost, but it is not actively maintained." Plan accordingly. (help.front.com)
Practical throughput
For a 100K-ticket account with an average of 8 comments per ticket, expect the full extraction (tickets + all comments + attachments) to take 6–12 hours when properly throttled against Zendesk's rate limits. Rushing it triggers 429 errors and can get your API token temporarily blocked.
Negotiate a temporary rate limit increase
Front offers API rate limit add-ons on Scale plans and above. If you're migrating more than 20K tickets, request a temporary increase before starting. It can cut migration time by 50–75%.
2026 clarification
Older migration guides sometimes mention ticket caps for Front's native importer. Front's current Zendesk importer article says there is no hard limit, but also says the importer is not actively maintained. That combination means you should test for fidelity on a real subset, not assume either limitlessness or maturity. (help.front.com)
Do not rely on CSVs for historical migrations
Zendesk CSV exports strip out the actual conversation thread. You get metadata only, leaving agents blind to the actual customer interaction.
Idempotency is non-negotiable
Use the external_id field on every imported message. A good pattern is zd-{ticket_id}-c-{comment_id}. Front rejects duplicate external IDs, preventing duplicate messages during retries. Store the Zendesk ticket ID in a conversation custom field for post-migration reconciliation.
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 Zendesk
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 Front can hold your support model
Walk your current workflow through Front: 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 Zendesk → Front 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.
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Disable any rules or automations
that could fire on imported conversations — imported messages can trigger auto-replies, assignments, and tagging rules if left active
Zendesk → Front specifics
- Cross-functional collaboration
- Teams that involve sales, ops, logistics, or finance in customer conversations find Zendesk's ticket model too rigid. Front's shared inboxes let multiple people work a thread natively.
- Cost consolidation
- Mid-market teams paying for Zendesk Suite Professional look at Front's Growth plan and see immediate savings — especially when they don't need Zendesk's ITSM or marketplace depth.
- Simplicity
- Front's UX resembles a modern email client. Teams that over-invested in Zendesk customization often want to reset to something lighter.
- Industry-specific workflows
- Logistics, financial services, and agency teams that rely on email-heavy, relationship-driven communication find Front's threading model more natural than Zendesk's ticket abstraction.
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 Zendesk 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 Zendesk 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 Zendesk 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
Zendesk → Front specifics
- Ticket comments and descriptions
- you get metadata only (ID, subject, status, requester, timestamps)
- 1 MB per-ticket limit
- tickets exceeding this have their comments stripped with a MaximumCommentsSizeExceeded error
- Six-minute exclusion window
- items updated within six minutes of the export are skipped
- Shared Inboxes
- Inbox configurations and team member access.
- Conversations & Messages
- The full thread of emails, SMS, or chat messages, including internal comments.
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 Zendesk → Front 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 Zendesk → Front 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 Front 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.
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 Front sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Front 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 Front'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 Front, 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 Zendesk 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 Front'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 Zendesk 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 Zendesk read-only rather than cancelled — cancelling the old contract on day one removes your only fallback.
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Keep Zendesk active
until you've validated Front. Don't cancel your Zendesk contract until you're confident.
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Document the point of no return
typically when agents start replying from Front, as those replies won't exist in Zendesk.
Zendesk → Front specifics
- Tag all imported conversations
- with a migration-specific tag (e.g., migrated_from_zendesk) so you can identify and bulk-archive if needed.
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 Zendesk and Front 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 Front, 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 Zendesk 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.
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 Equivalent
| Zendesk field | Front field | Notes |
|---|---|---|
| Ticket | Conversation | One ticket = one conversation |
| Ticket Comment (public) | Message (imported) | Direction inferred from comment author; mapped chronologically via imported_messages |
| Ticket Comment (private/internal note) | Comment | Internal-only, not visible to contacts |
| User (end-user) | Contact | Matched by email address |
| Organization | Account | Map company-level data; domain-based auto-association available in Front |
| Agent | Teammate | Matched by email |
| Group | Inbox | One group maps to one shared inbox |
| Tag | Tag | Direct mapping; tags must be pre-created in Front |
| Custom ticket field | Custom conversation field | Types must match; picklists need recreation; 50-field-per-category cap; text fields limited to 2,000 characters |
| Attachment | Attachment | 25 MB per message limit |
| Macro | — | Must be rebuilt as Front message templates |
| Trigger/Automation | — | Must be rebuilt as Front Rules |
| SLA Policy | — | Must be rebuilt in Front's SLA settings |
| Zendesk Guide article | — | Front has no native KB; use third-party |
Zendesk Front
Front has no native KB. Articles must be migrated to a third-party platform.
| Zendesk field | Front field | Notes |
|---|---|---|
| ticket.id | Conversation custom field + external_id pattern | Store as reference; use zd-{ticket_id}-c-{comment_id} for message external IDs |
| ticket.subject | conversation.subject | Direct map |
| ticket.status | conversation.status + custom field | New/Open → open; Pending/Hold → open + tag/snooze; Solved/Closed → archived. Store original in custom field |
| ticket.priority | Custom conversation field or tag | Front has no native priority field |
| ticket.type | Custom conversation field or tag | Front has no ticket type |
| ticket.tags | conversation.tags | Must be pre-created in Front; native importer skips tags |
| ticket.group_id | inbox_id | Map each Zendesk group to a Front inbox |
| ticket.assignee_id | conversation.assignee_id | Match agent email → teammate ID |
| ticket.requester_id | Contact (sender of first message) | Lookup by email |
| ticket.created_at | First message created_at | Unix timestamp |
| comment.body | message.body | HTML body; strip Zendesk-specific markup (survey widgets, notification footers) |
| comment.public | Message (if true) / Comment (if false) | Determines import endpoint |
| comment.author_id | message.sender or comment author | Resolve to email handle |
| comment.attachments | message.attachments | Multipart upload; 25 MB cap |
| ticket.custom_fields [] | Custom conversation fields | Must pre-create fields in Front via API; mind the 50-field cap and 2,000-char text limit |
| organization.name | account.name | Create accounts before contacts |
| organization.domain_names | account.domains | Normalize to lowercase; enables auto-association by domain |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Ticket Comments (public) | low | Map to Front imported messages with direction inferred from comment author. |
| Ticket Comments (private) | low | Map to Front internal comments on conversations. |
| Custom Ticket Fields | medium | 50-field cap per category and 2,000-char text limit. Must pre-create in Front. |
| Tags | medium | Must be pre-created in Front. Native importer skips tags entirely. |
| Attachments | medium | 25 MB per message limit. Attachments over this must be split or hosted externally. |
| Zendesk Guide Articles | high | Front has no native KB. Articles must be migrated to a third-party platform. |
| Priority and Type Fields | medium | Front has no native priority or type field. Must use custom fields or tags. |
| SLA Policies | high | Must be rebuilt from scratch in Front's simpler SLA engine. |
| Triggers and Automations | high | Must be rebuilt as Front Rules. No automated conversion path exists. |
| Side Conversations | high | Separate API objects not included in standard ticket comments export. Require custom handling. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Data Model Translation
Every Zendesk ticket becomes a Front conversation. Comments become messages or internal comments. Threading logic failures create orphan messages and fragmented history.
Rate Limit Orchestration
Front's API limit is per-company, not per-token. Migration scripts compete with live integrations for the same quota, risking degraded workflows.
Inline Image Expiration
Zendesk inline image URLs are authenticated and expire. Images must be downloaded, re-hosted, and HTML src tags rewritten before importing to Front.
Custom Field Limits
Front caps custom fields at 50 per category with a 2,000-character text limit. Complex Zendesk custom field configurations must be consolidated.
No Native KB Migration
Front has no knowledge base. Zendesk Guide articles must be migrated to a third-party tool or archived separately.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can I migrate Zendesk tickets to Front using CSV files?
No, not for full history. Zendesk CSV exports exclude ticket comments, descriptions, multi-line text fields, deleted tickets, and attachments. Front's CSV imports are for contacts and accounts only, not conversation history. You need the Incremental Export API for the actual ticket data.
Does Front have a native Zendesk importer?
Yes. As of Front's help article edited December 2, 2025, the importer has no hard ticket limit, but Front states it is 'not actively maintained' and recommends migration partners instead. It does not import custom fields, tags, or rules, and edge case handling is limited.
How long does a Zendesk to Front migration take?
It depends on volume and Front plan. On a Growth plan (100 API requests/min), expect roughly 10–12 complete tickets per minute import throughput. A 50K-ticket account can take 60–80 hours of continuous import. Temporary rate limit increases from Front or a managed service can cut this significantly.
What data cannot be migrated from Zendesk to Front?
Zendesk Guide articles (Front has no native KB), SLA metrics, trigger/automation logic, CSAT survey responses, and custom object relationships cannot be directly migrated. These must be rebuilt, archived separately, or moved to third-party tools.
What is the biggest API bottleneck in a Zendesk to Front migration?
Both sides are constrained. Zendesk's Incremental Export API is capped at 10 requests per minute. Front's write limits are 50–500 requests per minute depending on plan, enforced per-company across all integrations and scripts. Your migration script competes with live workflows for Front's quota.