There is no native migration path from Freshdesk to Tidio. Freshdesk is a full-featured ticketing platform with robust API coverage, while Tidio is a chat-focused platform with a much narrower API surface that only supports contact and ticket creation. The fundamental gap is API coverage: Tidio has no API endpoints for creating agents, departments, companies, knowledge base content, or canned responses — all of these must be rebuilt manually. Every migration requires creative workarounds, including reconstructing conversation history through Tidio's reply endpoint and populating custom field data via bulk contact updates.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
Duplicate handling
Test what happens when you submit a contact with an email that already exists in Tidio. Depending on the API behavior, the request may fail the entire batch, skip the duplicate, or upsert. Validate this before running the full import.
Preserve original timestamps
Pass the original created_at timestamp from Freshdesk during each creation call. Without this, all records will show the migration date instead of the actual historical date. Freshdesk stores all timestamps in UTC.
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 Freshdesk
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 Tidio can hold your support model
Walk your current workflow through Tidio: 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 Freshdesk → Tidio 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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Define Staff and Structure
Manually create your Operators (Tidio's term for agents) and set up the relevant Departments (Tidio's term for groups/teams). The Tidio API only supports retrieving lists of existing operators and departments — creation must be done in the Tidio administration panel.
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Map Ticket Statuses and Priorities
Freshdesk and Tidio use different internal representations for status and priority values. Map these before import to prevent miscategorization.
Freshdesk → Tidio specifics
- Replicate Custom Data Structures
- Examine the custom fields defined on Freshdesk Contacts. Manually replicate these as custom properties in Tidio. These properties have predefined types: text, email, number, phone, or URL.
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 Freshdesk 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 Freshdesk 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 Freshdesk 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 Freshdesk → Tidio 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 Freshdesk → Tidio 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 Tidio 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 Tidio sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Tidio 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 Tidio'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 Tidio, 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 Freshdesk 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 Tidio'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 Freshdesk 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 Freshdesk 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 Freshdesk and Tidio 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 Tidio, 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 Freshdesk 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 Knowledge Assets
Freshdesk Solutions (Categories, Folders, Articles) must be recreated manually in Tidio's knowledge base tools.
Freshdesk → Tidio specifics
- Replicate Business Logic
- Scenario Automations, SLA Policies, and Business Hours must be redefined and activated within Tidio. These rules govern ticket prioritization, assignment, and escalation — validate them carefully.
- Finalize Internal Structure
- Establish Companies, Canned Responses, Roles, and permissions. Ensure groups adhere to any necessary communication or staffing requirements.
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
| Freshdesk field | Tidio field | Notes |
|---|---|---|
| Contact | Contact (Visitor) | Identified by email, name, or phone |
| Contact Custom Field (schema) | Custom Property | Must create before data import |
| Contact Custom Field (data) | Custom Property (value) | Requires schema to exist first |
| Ticket | Ticket | Must reference Tidio Contact ID |
| Ticket Reply / Note | Ticket Message | Reconstructed per-ticket, chronologically |
| Agent | Operator | API is read-only for operators |
| Group | Department | API is read-only for departments |
| Company | — | No direct equivalent; recreate manually |
| Custom Field (Ticket) | — | Schema and data both manual |
| Canned Response | — | No API migration path |
| Knowledge Base (Category/Folder/Article) | Knowledge Base | Full manual rebuild required |
| SLA Policy | SLA Policy | Historical compliance data lost |
| Business Hours | Business Hours | — |
| Time Entry | — | Archive from Freshdesk |
| Survey (CSAT) | — | Archive from Freshdesk |
| Discussion | — | Archive from Freshdesk |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Contacts | low | Bulk creation supported via Tidio API. |
| Contact Custom Fields | medium | Schema must be created manually; data populated via bulk update workaround. |
| Tickets | medium | Requires careful mapping of status and priority numerical values. |
| Conversations / Replies | high | No direct mapping; reconstructed via reply endpoint with customer/operator distinction. |
| Agents / Operators | high | No API creation support; must be manually set up in Tidio admin panel. |
| Companies | high | No API migration route; must be manually recreated in Tidio. |
| Knowledge Base | high | Entire category, folder, and article structure must be rebuilt manually. |
| Canned Responses | high | No API endpoint; manual recreation required. |
| SLA Policies | high | Must be manually redefined in Tidio with no automated transfer. |
| Time Entries | high | No equivalent in Tidio; archive as export files for reference. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Limited API Surface
Tidio only supports creating contacts and tickets via API. Agents, departments, KB, and canned responses require manual setup.
Conversation Reconstruction
Historical replies must be reconstructed using Tidio's reply-to-ticket endpoint as a workaround, not a direct mapping.
Custom Field Schema
Ticket custom field structures must be manually created in Tidio before any ticket data can reference them.
Status Value Mapping
Freshdesk uses numerical status codes (e.g., 2=Open, 4=Resolved) that must be mapped to Tidio's different internal representations.
What breaks
Known failure modes. Have a recovery plan for each before you cut over.
Freshdesk API Rate Limits:
Freshdesk enforces per-minute rate limits that vary by plan tier. Hitting these limits halts your export. Queue API calls carefully and cache non-volatile data (like agent IDs and group mappings) to reduce unnecessary requests.
Tidio API Rate Limits:
Tidio also enforces rate limits on its API. Check the current limits in their developer documentation before designing your import pipeline — exceeding them will result in 429 responses and dropped records if not handled.
The Contact ID is King:
The linking mechanism between tickets and conversations relies on the Tidio Contact ID. Every ticket must be correctly associated with its Tidio Contact ID. If this mapping breaks, you lose relational integrity between contacts and their ticket history.
Timestamp Handling:
Pass the original created_at timestamp during creation calls to preserve historical context. Freshdesk stores timestamps in UTC. If any Freshdesk records have null or missing created_at values, decide on a fallback strategy (e.g., use updated_at or a sentinel date) before running the import.
Status and Priority Mapping:
Freshdesk uses numerical values for Status (e.g., 2 for Open, 4 for Resolved) and Priority (e.g., 1 for Low, 4 for Urgent). Tidio may use different internal representations. Verify the mapping against Tidio's current API documentation before running the import.
Volume Considerations:
For large Freshdesk instances (tens of thousands of tickets or more), a sequential ticket-by-ticket approach may take hours or days. Consider parallelizing API calls within rate limit bounds, or implementing a job queue to manage throughput.
Partial Migration Rollback:
If the migration fails midway — for example, tickets imported without correct contact associations — you need a rollback plan. Before starting, document how you would identify and delete partially imported records in Tidio to allow a clean re-run.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.