Migrating Front to Zendesk requires the Ticket Import API to preserve timestamps and threading. CSV exports won't work for history. Rules, drafts, and analytics must be rebuilt. Expect 4–8 weeks DIY or 1–3 weeks managed.
There is no native migration path from Front to Zendesk. The core challenge is translating Front's conversation-centric shared inbox model into Zendesk's ticket-lifecycle system with structured statuses, SLAs, and group assignments. CSV exports from Front do not include full conversation threads, attachments, or tags, making them unsuitable for historical data migration. A successful migration requires API-based extraction from Front and loading via Zendesk's Ticket Import API, along with manual rebuilding of rules, analytics, integrations, and canned responses.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
Use the Ticket Import API, not the standard Create Ticket API
The standard POST /api/v2/tickets endpoint stamps every ticket with the current timestamp and fires triggers. The Import API (POST /api/v2/imports/tickets) preserves created_at, updated_at, and solved_at values and bypasses triggers. Using the wrong endpoint is the single most common migration error — it makes every historical ticket look like it was created today.
Throughput math
At 50 req/min on Front, you get roughly 3,000 requests per hour. If your extractor averages three API calls per conversation (listing pass, messages pass, comments pass), you can extract about 1,000 conversations per hour before retries and attachment downloads. For 100K conversations with an average of 8 messages each, you need roughly 900K API calls — approximately 12.5 days of continuous extraction. Purchasing a rate limit add-on or scheduling the migration during off-peak hours is not optional — it is a prerequisite. (dev.frontapp.com)
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 Front
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 Front → 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.
Front → Zendesk specifics
- Formal SLA management
- Zendesk's SLA policies track first-reply and resolution times natively with escalation paths. Front handles SLAs through rules, but they are inbox-scoped and less granular.
- Ticket lifecycle control
- Zendesk tickets move through defined statuses (new → open → pending → solved → closed) with triggers at each transition. Front conversations are either open or archived — there is no native pending or solved state.
- Reporting depth
- Zendesk Explore provides cross-team reporting with custom metrics and drill-downs. Front's analytics are more limited for enterprise reporting needs.
- Enterprise compliance
- Zendesk offers FedRAMP authorization and advanced data privacy add-ons. Teams in regulated industries often need these certifications. (zendesk.com)
- Marketplace ecosystem
- Zendesk's app marketplace has 1,500+ integrations versus Front's smaller ecosystem.
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 Front 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 Front 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 Front 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 Front → 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 Front → 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.
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 Front 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 Front 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 Front 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 Front 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 Front 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.
Front Zendesk Object
| Front field | Zendesk field | Notes |
|---|---|---|
| Conversations (email, chat, SMS, social) | Tickets | 1:1 mapping; status translation required |
| Messages (within conversations) | Ticket Comments (public/private) | Must preserve author_id and created_at |
| Conversation Comments (internal) | Internal Notes | Lose threaded context |
| Inboxes (shared inboxes) | Groups + Email Addresses | One inbox may map to one group + one support address; in multi-brand Zendesk setups, each inbox may also map to a specific brand |
| Teammates | Agents | Map by email; create in Zendesk before importing tickets |
| Teams | Groups | Front teams → Zendesk groups |
| Contacts | End-Users | Zendesk enforces unique emails per identity |
| Accounts | Organizations | Map Front account → Zendesk organization |
| Tags | Tags | Zendesk tags are lowercase, no spaces — transform required |
| Custom Fields (contacts) | User Fields | Data type mapping required |
| Custom Fields (conversations) | Ticket Fields | Must be created in Zendesk before import |
| Canned Responses / Message Templates | Macros | Partial mapping; dynamic variable syntax differs |
| Rules (inbox-scoped) | Triggers + Automations | Cannot be migrated. Must be rebuilt manually. |
| Signatures | Agent Signatures | Manual recreation |
| Analytics / Reports | Explore | Must be rebuilt from scratch. |
| Integrations (CRM, Slack) | Marketplace Apps | Must be reconfigured. |
| Attachments | Ticket Attachments | Upload via Zendesk Uploads API; attach token to comment |
| Drafts / Shared Drafts | No equivalent | No migration path. Finalize or discard before cutover. |
| Conversation Links | No native equivalent | Side Conversations is partial; not a 1:1 replacement |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Tickets (Conversations) | high | Conversation-to-ticket mapping requires status translation, correct API endpoint selection (Import API vs. standard), and proper timestamp preservation to avoid data loss. |
| Conversation Messages | high | Each message must retain its original author_id, created_at timestamp, and public/private visibility flag, requiring careful API extraction and payload construction. |
| Internal Comments | medium | Front's threaded internal comments flatten into linear internal notes in Zendesk, preserving content but losing conversational threading context. |
| Contacts (End-Users) | medium | Zendesk enforces unique emails per identity, and all contacts must be loaded before tickets to prevent permanent requester misattribution. |
| Organizations (Accounts) | low | Front accounts map cleanly to Zendesk organizations via bulk API creation with minimal transformation required. |
| Tags | low | Tags migrate straightforwardly but require transformation to lowercase with no spaces to comply with Zendesk's tag formatting constraints. |
| Custom Fields | medium | Data type differences between Front and Zendesk (e.g., yes/no vs. checkbox, dropdown value vs. label) require explicit type mapping, and fields must be pre-created in Zendesk before import. |
| Attachments | medium | Attachments are excluded from Front's CSV export and must be individually uploaded via Zendesk's Uploads API, with the returned token attached to the corresponding comment payload. |
| Rules and Automations | high | Front's inbox-scoped rules have no migration path and must be entirely rebuilt as Zendesk triggers, automations, and SLA policies with different scoping logic. |
| Shared Drafts | high | Zendesk has no equivalent to Front's shared drafts, so all in-progress drafts must be finalized or discarded before cutover with no migration path available. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Data Model Translation Gap
Front's conversation-centric model (open/archived states, inbox-scoped routing) must be mapped to Zendesk's ticket-lifecycle model (new/open/pending/solved/closed statuses, group assignments, SLA context), requiring explicit status and structural translation at every level.
Timestamp and Attribution Preservation
Using Zendesk's standard Create Ticket API instead of the Ticket Import API overwrites all historical timestamps with the current date and fires triggers, destroying chronological accuracy and author attribution across the entire dataset.
Threaded Collaboration Context Loss
Front's shared drafts and threaded internal comments are first-class collaboration objects that flatten into a linear sequence of public and private comments in Zendesk, losing the original threading and collaborative context.
Rules and Automation Rebuild
Front's inbox-scoped rules cannot be migrated and must be manually reconstructed as Zendesk global triggers, automations, and SLA policies, which follow a fundamentally different scoping and execution model.
Requester Load Order Dependency
Zendesk tickets require a valid requester_id, so all Front contacts must be created as Zendesk end-users before ticket import, otherwise attribution permanently defaults to the API admin's user ID.
CSV Export Inadequacy
Front's account data export must be requested through support, excludes attachments, full message bodies, tags, and contact data, and only provides a 200-character message extract, making it unsuitable for conversation history migration.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
How long does a Front to Zendesk migration take?
For a team with 50 agents and 100K conversations, expect 4–8 weeks end-to-end if building in-house, or 1–3 weeks with a managed migration service. The primary variable is Front's API rate limits (starting at 50 requests per minute), which bottleneck extraction speed.
Can I keep my full Front conversation history in Zendesk?
Yes, but only if you use Zendesk's Ticket Import API (POST /api/v2/imports/tickets), which preserves original created_at, updated_at, and solved_at timestamps. Using the standard Create Ticket API will stamp every ticket with the current date. Each Front message becomes a Zendesk comment with the correct author and timestamp.
What data is lost when migrating from Front to Zendesk?
Rules and automations, analytics dashboards, shared drafts, conversation links, integration configurations, and SLA timer states cannot be migrated programmatically and must be rebuilt manually in Zendesk. Front's support-led account export also excludes tags and message templates.
Do I need to shut down Front during migration?
No. Historical data can be extracted from Front while it remains active. Most teams run a final delta sync immediately before the cutover to capture new conversations created during the migration window. Only the cutover itself requires coordination — typically done over a weekend or low-traffic window.
Is there a free tool to migrate from Front to Zendesk?
No free tool performs a full migration with conversation history, threading, and timestamps. Automated tools like Help Desk Migration charge per-record fees. For complete history preservation, you need either custom API scripts or a managed migration service.