There is no native migration path from Tidio to Freshdesk. Tidio is a real-time chat platform with operators and departments, while Freshdesk is a full-featured helpdesk with agents, groups, and structured ticket workflows. Every migration requires custom API work to pre-create custom field schemas, convert chat messages into ticket replies and notes, and transform viewed page history into Freshdesk Omnichannel Activities.
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 Tidio
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 Freshdesk can hold your support model
Walk your current workflow through Freshdesk: 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 Tidio → Freshdesk timeline -
Name owners and set the go/no-go date
One named owner each for data, configuration, integrations, and agent enablement, plus a decision-maker who can call a rollback. Put the go/no-go meeting in calendars now, 48 hours before the freeze.
Don't move on until
- Record counts confirmed for tickets, contacts, organisations and macros
- Success criteria signed off by the support lead
- Freeze window provisionally booked with the business
02 Data Audit Find out what is actually in the data before you try to move it.
Objective A profiled, cleaned export with every quality defect either fixed at source or explicitly accepted.
Keep these open
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Take a full Tidio 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 Tidio 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 Tidio 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 Tidio → Freshdesk 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 Tidio → Freshdesk 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 Freshdesk 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.
Tidio → Freshdesk specifics
- Custom field verification
- Filter contacts and tickets by custom field values in Freshdesk to confirm data populated correctly. Empty custom fields on records that had values in Tidio indicate a mapping failure.
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 Freshdesk sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Freshdesk 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 Freshdesk'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 Freshdesk, 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 Tidio 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 Freshdesk'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 Tidio 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 Tidio read-only rather than cancelled — cancelling the old contract on day one removes your only fallback.
Tidio → Freshdesk specifics
- Rollback plan
- If validation reveals systemic issues (wrong contact associations, missing ticket batches), you need a path back. Before starting the migration, document which Freshdesk objects were created by the import process so they can be identified and deleted cleanly via the API if a re-run is necessary.
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 Tidio and Freshdesk 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 Freshdesk, 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 Tidio 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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Record counts
Compare the total number of contacts, tickets, and conversations in Tidio against what landed in Freshdesk. Any discrepancy above zero needs investigation.
Tidio → Freshdesk specifics
- Spot-check relational integrity
- Pick a sample of tickets and verify the requester, assignee, and group associations are correct. Broken ID mappings tend to surface here.
- Conversation ordering
- Open several migrated tickets and confirm that replies and notes appear in the correct chronological sequence. Timestamp mishandling shows up as jumbled conversation threads.
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.
Tidio Freshdesk
| Tidio field | Freshdesk field | Notes |
|---|---|---|
| Contact email | Contact email | Primary identifier; must be unique in Freshdesk |
| Contact name | Contact name | Direct mapping |
| Contact phone | Contact phone | Direct mapping |
| Contact custom properties | Contact custom fields | Custom fields must be pre-created in Freshdesk |
| Department name | Group name | Create groups before assigning agents |
| Operator name / email | Agent name / email | Agents must be associated with groups |
| Ticket subject | Ticket subject | Direct mapping |
| Ticket status (Open, Resolved, etc.) | Ticket status (numeric) | Map to Freshdesk status codes: 2=Open, 3=Pending, 4=Resolved, 5=Closed |
| Ticket priority | Ticket priority (numeric) | Map to Freshdesk priority codes: 1=Low, 2=Medium, 3=High, 4=Urgent |
| Ticket assignee (Operator) | Ticket responder_id | Use the ID mapping table to resolve |
| Ticket requester (Contact) | Ticket requester_id | Use the ID mapping table to resolve |
| Conversation messages (public) | Ticket Reply | Created via the Reply endpoint |
| Conversation messages (internal/bot) | Ticket Note (private) | Mark as private when not customer-facing |
| Viewed pages | Omnichannel Activity | Restructure as timeline events on the contact |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Conversations/Messages → Replies/Notes | high | Unstructured chat stream must be classified into public and private types |
| Custom Properties → Custom Fields | medium | Field schemas must exist in Freshdesk before contact data can import |
| Viewed Pages → Omnichannel Activities | medium | Only 30 days of history available, requires special API endpoint |
| Tickets → Tickets | medium | Status and priority values must be mapped to Freshdesk numerical codes |
| Operators → Agents | low | Direct mapping with group association |
| Departments → Groups | low | Direct structural mapping between platforms |
| Contacts → Contacts | low | Standard field mapping with custom property translation |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Custom Field Pre-Creation
Tidio contact property schemas must be replicated as Freshdesk custom contact fields before any data import can begin.
Message Type Conversion
Tidio's unstructured chat messages from contacts, operators, and chatbots must be sorted into Freshdesk public replies or private notes.
Viewed Pages Mapping
Tidio's 30-day page view history must be restructured as Omnichannel Activities on the Freshdesk contact timeline.
ID Mapping Complexity
A master cross-reference table linking Tidio IDs to Freshdesk IDs is essential for accurate ticket and conversation attribution.
What breaks
Known failure modes. Have a recovery plan for each before you cut over.
Duplicate contacts:
Tidio contacts may share an email address, but Freshdesk enforces unique emails per contact. Before importing, deduplicate your contact list. Decide whether to merge conversation history into the surviving record or flag duplicates for manual review.
Contacts without email addresses:
Freshdesk requires either an email or phone number to create a contact. Tidio contacts identified only by browser session or name cannot be imported as-is. You'll need to either skip these records or assign a placeholder identifier.
Tickets with deleted operators:
If a Tidio operator who was assigned to tickets has since been removed, those tickets will fail to import if you try to set a responder_id that doesn't exist. Import these tickets without an assignee, or reassign them to a catch-all agent.
Attachment handling:
File attachments on Tidio messages need to be downloaded and re-uploaded to Freshdesk. Large attachments may hit Freshdesk's file size limits depending on your plan. Check Freshdesk's documentation for current limits and plan accordingly.
Ticket custom fields:
If you use custom fields on tickets in Tidio (not just contacts), those fields must also be pre-created in Freshdesk before ticket import—the same pattern as contact custom properties.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.