Zendesk to LiveChat migration requires splitting data into two targets, extracting via Zendesk's Incremental API, and loading via HelpDesk's /v1/importedTickets endpoint — the only path that preserves timestamps.
Migrating from Zendesk to LiveChat has no native, single-step migration path and requires splitting historical data across two separate target platforms: LiveChat (for historical live chat conversations) and HelpDesk (for asynchronous email and web form tickets). Zendesk's unified Ticket object — which treats all channels including live chat as tickets with a via.channel attribute — must be decomposed and routed based on channel type, then transformed to match fundamentally different data hierarchies: LiveChat's Chats → Threads → Events model and HelpDesk's ticket → event model. Zendesk's comment-per-ticket structure does not map directly to either target, requiring custom transformation logic to convert comments into typed event payloads with explicit authors, timestamps, and visibility flags. Additionally, Zendesk chat transcripts are frequently exported as monolithic plain-text blocks, requiring regex-based parsing to reconstruct individual message events before they can be loaded into LiveChat's Agent Chat API v3.5.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
What you lose in the move
Zendesk's mature trigger/automation engine, SLA policies with business-hour calculations, multi-brand support, side conversations, satisfaction prediction, and Explore analytics do not have direct equivalents in LiveChat/HelpDesk. Evaluate these gaps before committing.
Transaction IDs expire after 24 hours
If your migration script pauses or crashes, you cannot reuse a stale transaction. All uploaded attachments associated with an unused transaction are purged automatically. Generate a new transaction ID at the start of each ticket's processing, not once at the start of the migration run.
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 LiveChat can hold your support model
Walk your current workflow through LiveChat: 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 → LiveChat 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.
Zendesk → LiveChat specifics
- Chat-first support model
- The team is pivoting from email/ticket-based support to real-time chat as the primary channel — common in e-commerce and SaaS with high-volume, low-complexity queries.
- Cost reduction
- Zendesk's per-agent pricing scales steeply. A team on Suite Professional ($115/agent/month) can often cover the same use case with LiveChat ($49/agent/month) + HelpDesk Team ($29/agent/month) for roughly half the cost.
- Platform consolidation
- Teams standardizing on the Text ecosystem (LiveChat + HelpDesk + ChatBot + KnowledgeBase) want all customer interactions under one vendor.
- Agent experience
- LiveChat's interface is purpose-built for real-time conversations. Teams that spend most of their time in chat find Zendesk's agent workspace heavier than needed.
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 → LiveChat specifics
- Rate limit
- 10 requests per minute to incremental export endpoints (30 with the High Volume API add-on). This is a separate, lower limit from the account-wide limit.
- Pagination
- Each page returns up to 1,000 tickets. Read end_of_stream: true in the response to detect completion.
- Comments are not included
- in incremental ticket exports. Fetch them separately per ticket:
- Attachments are URLs, not binary files
- Each comment attachment includes a content_url pointing to an AWS S3-hosted file behind an expiring signed URL. You must download the binary during extraction and store it locally — not as a deferred step. URLs expire and cannot be reused during the load phase.
- Ticket count
- by status (new, open, pending, hold, solved, closed) and by channel (email, chat, web, API)
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 → LiveChat 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 → LiveChat 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 LiveChat 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.
Zendesk → LiveChat specifics
- Custom fields
- list types, names, and which ones are in active use
- Tags and custom fields
- populated correctly, no truncation on 120-char single-line fields
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 LiveChat sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a LiveChat 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 LiveChat'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 LiveChat, 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 LiveChat'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.
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 LiveChat 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 LiveChat, 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.
Zendesk → LiveChat specifics
- Ticket counts
- match expected totals by status and channel
- Event counts
- per ticket — spot-check 50–100 tickets across different sizes and verify comment counts match
- Timestamps
- verify createdAt values match Zendesk originals, not the import date
- Agent assignments
- resolve to the correct HelpDesk agents (not "Legacy Agent" where unintended)
- Attachments
- sample 100 random tickets, download attachment URLs, verify files are accessible and non-zero in size
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.
Concept HelpDesk Equivalent
| Zendesk field | LiveChat field | Notes |
|---|---|---|
| Ticket | Ticket | 1:1 mapping. Use POST /v1/importedTickets for timestamp preservation. |
| Comment (public) | Event (type: message, isPrivate: false) | Must include date for each event in chronological order. |
| Comment (internal note) | Event (type: message, isPrivate: true) | Set agentID and agentName on agent-issued events. |
| User (requester) | Requester (email + name) | HelpDesk has no standalone contacts/users table. |
| User (agent) | Agent | Must pre-create agents via POST /v1/agents before referencing in tickets. |
| Organization | — | No equivalent. Store in a custom field if needed. |
| Group | Team | Create teams first via POST /v1/teams. |
| Custom Field | Custom Field (singleLine, multiLine, date, url) | HelpDesk supports only 4 types. Dropdowns, checkboxes, numeric, and regex fields must be flattened to singleLine. 120-char limit on single-line fields; 1,000-char limit on multi-line. |
| Tag | Tag | Tags are team-scoped in HelpDesk. Create via POST /v1/tags with a teamID. |
| Priority (low, normal, high, urgent) | Priority (-10, 0, 10, 20) | Direct numeric mapping: low→-10, normal→0, high→10, urgent→20. |
| Status (new, open, pending, hold, solved, closed) | Status (open, pending, onhold, solved, closed) | Zendesk new has no equivalent — map to open. |
| Macro | Macro | Rebuild manually. HelpDesk limits macros to 20 shared + 20 per user. |
| Trigger / Automation | Rule | Rebuild manually via POST /v1/rules. HelpDesk Rules use a condition/action model but lack Zendesk's time-based trigger scheduling and business-hour arithmetic. |
| SLA Policy | — | No native SLA engine in HelpDesk. |
| Attachment | Attachment (via Transaction) | 3-step process: create transaction → upload file → reference in ticket. |
| Satisfaction Rating | Rating (good, neutral, bad) | Zendesk has 2 ratings (good/bad). HelpDesk has 3 (good/neutral/bad). Map Zendesk good→good, bad→bad; no source value maps to neutral. |
| Webhook | — | Zendesk webhooks break at cutover. Rebuild downstream integrations against HelpDesk webhook events before go-live. |
Concept Equivalent
| Zendesk field | LiveChat field | Notes |
|---|---|---|
| Ticket (chat) | Chat | Top-level container. |
| Ticket thread / comments | Thread | A Chat contains one or more Threads. |
| Comment | Event (type: message) | Each agent or customer message becomes a discrete Event with author_type and timestamp. |
| Internal note | Event (type: system_message) | No direct equivalent for private agent notes in archived chats. |
| Attachment on comment | Event (type: file) | Separate event type; requires file upload to LiveChat CDN before referencing. |
| Agent (comment author) | Agent (author_type: "agent") | Agent must exist in LiveChat before the Chat is created. |
| End-user (requester) | Customer (author_type: "customer") | Customer identified by email in the Chat payload. |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Tickets | medium | Tickets map 1:1 to HelpDesk tickets using the <code>POST /v1/importedTickets</code> endpoint for timestamp preservation, but routing logic must correctly separate chat tickets from async tickets before load. |
| Comments / Events | high | Zendesk comments must be individually transformed into typed HelpDesk event payloads or LiveChat message events with explicit author, visibility, and timestamp fields; direct field mapping is not possible and errors silently produce broken conversation histories. |
| Live Chat Transcripts | high | Chat transcripts exported from Zendesk are often monolithic plain-text blocks with no per-message structure, requiring custom parsing logic that may fail on non-standard formatting or multi-language content. |
| Attachments | high | Attachment content URLs are time-limited AWS S3 signed URLs that expire before the load phase if not downloaded immediately during extraction, and re-upload requires a three-step process in HelpDesk or a CDN upload in LiveChat. |
| Custom Fields | high | HelpDesk's four supported field types and 120-character single-line limit require lossy flattening of Zendesk dropdown, checkbox, numeric, and regex field types, risking data truncation and loss of structured values. |
| Organizations | high | HelpDesk has no native organization or company object equivalent, so Zendesk organization data must be stored in a custom text field, losing relational grouping and any organization-level reporting. |
| Tags | medium | Tags are team-scoped in HelpDesk and must be pre-created via <code>POST /v1/tags</code> with an explicit <code>teamID</code> before being referenced in migrated tickets, requiring a pre-migration seeding step. |
| Satisfaction Ratings | low | Zendesk's two-value rating scale (good/bad) maps cleanly to HelpDesk's <code>good</code> and <code>bad</code> values, though HelpDesk's third <code>neutral</code> value will remain unpopulated from migrated data. |
| Triggers, Automations, and SLAs | high | Zendesk triggers, automations, and SLA policies have no direct equivalents in HelpDesk or LiveChat and must be fully rebuilt manually as HelpDesk Rules, with the loss of time-based scheduling, business-hour arithmetic, and any native SLA enforcement. |
| Macros | medium | Macros must be rebuilt manually in HelpDesk and are subject to a hard limit of 20 shared macros plus 20 per-user macros, which may require consolidation for teams with large macro libraries. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Dual-Target Data Routing
Every Zendesk ticket must be inspected for its <code>via.channel</code> value and routed to either LiveChat (chat tickets) or HelpDesk (email, web form, and API tickets), requiring a bifurcated ETL pipeline rather than a single load process.
Comment-to-Event Model Transformation
Zendesk's comment objects must be transformed into typed HelpDesk event payloads with explicit <code>isPrivate</code> flags and chronological <code>date</code> fields, as a direct field-copy will produce broken or malformed ticket records.
Chat Transcript Parsing
Zendesk frequently exports live chat transcripts as a single monolithic plain-text block, requiring regex or structured-splitting logic to reconstruct individual per-message events with correct authors and timestamps before posting to LiveChat's Agent Chat API v3.5.
Attachment Multi-Step Re-Upload
Zendesk attachment URLs are expiring AWS S3 signed URLs that must be downloaded as binaries during extraction and then re-uploaded to HelpDesk via a three-step transaction process or to LiveChat's CDN before they can be referenced in migrated records.
Custom Field Type Flattening
HelpDesk supports only four custom field types (<code>singleLine</code>, <code>multiLine</code>, <code>date</code>, <code>url</code>), so Zendesk dropdown, checkbox, numeric, and regex fields must be converted to <code>singleLine</code> text with a hard 120-character limit.
Agent and Team Pre-Creation Dependency
HelpDesk and LiveChat require agents and teams to be created via their respective APIs before any ticket or chat records reference them, making pre-migration entity seeding a blocking prerequisite for the load phase.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Does LiveChat still have a built-in ticketing system?
No. LiveChat's legacy ticketing was sunset in January 2025. Ticketing now runs through HelpDesk (helpdesk.com), a separate product by the same parent company, Text. HelpDesk integrates directly into the LiveChat agent app and shares the same authentication system.
Can I import Zendesk tickets into HelpDesk with original timestamps?
Yes, but only through the HelpDesk POST /v1/importedTickets endpoint. The first message event's date becomes the ticket's createdAt timestamp. The standard POST /v1/tickets endpoint ignores client-supplied dates and stamps the server's current time.
What are the API rate limits for Zendesk and HelpDesk during migration?
Zendesk Support API limits range from 200 req/min (Team) to 2,500 req/min (Enterprise Plus). Incremental exports are capped at 10 req/min. HelpDesk enforces 1,000 requests per 10-minute window per license, shared across all integrations.
Will migrating historical tickets trigger email notifications to customers?
Yes, unless you explicitly suppress them. Disable all automated workflows and auto-responders in HelpDesk before the migration, ensure your API token bypasses outbound email triggers, and run a test batch to verify no SMTP traffic is generated.
How long does a Zendesk to LiveChat migration take?
A small migration (under 5,000 tickets) takes 1–2 weeks. Medium (5,000–50,000) takes 2–5 weeks. Large migrations (50,000+) can take 4–12 weeks. Attachment volume and custom field complexity are the biggest variables.