There is no native migration path from Freshdesk to Intercom. Freshdesk organizes support around flat tickets and contacts, while Intercom uses a conversational relationship model that unifies tickets, chat, and automation under contacts and companies. The fundamental gap is structural: Freshdesk tickets must be evaluated individually to determine whether they map to an Intercom Ticket or an Intercom Conversation, and Intercom enforces strict uniqueness on emails and company names that Freshdesk does not. Every migration requires custom API work to handle attachment re-hosting, HTML sanitization, timestamp workarounds, and careful data attribute pre-creation across both platforms.
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 Intercom can hold your support model
Walk your current workflow through Intercom: 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 → Intercom 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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Create data attributes
for contacts, companies, tickets, and conversations to mirror your Freshdesk custom fields.
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Set up ticket types and attributes
for common categories (bug, refund, feature request).
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Rebuild your Help Center structure
create Collections and Folders before importing Articles.
Freshdesk → Intercom specifics
- Invite all agents
- and assign them to Teams matching Freshdesk groups.
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
Freshdesk → Intercom specifics
- Tag taxonomy cleanup
- standardize tags to avoid duplicates during import.
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 → Intercom 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 → Intercom 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 Intercom 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.
Freshdesk → Intercom specifics
- Intercom Conversation → HubSpot Ticket
- Map Intercom state (open, closed) to HubSpot hs_pipeline_stage.
- Conversation Parts → HubSpot Engagements
- Map admin replies to HubSpot Emails or Notes associated with the Ticket.
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 Intercom sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Intercom 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 Intercom'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 Intercom, 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 Intercom'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 Intercom 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 Intercom, 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.
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.
Freshdesk Intercom
| Freshdesk field | Intercom field | Notes |
|---|---|---|
| id | company_id | Use as external_id for idempotent imports |
| name | name | Must be unique |
| domains [] | domains [] | Enables automatic linking |
| custom_fields | custom_attributes | Pre-create attributes |
| created_at | Custom “original_created_at” field | Intercom can’t backdate timestamps |
Freshdesk Intercom
| Freshdesk field | Intercom field | Notes |
|---|---|---|
| id | external_id | Required for deduplication |
| name | name | Combine first/last names if stored separately |
| Required for mapping replies | ||
| phone | phone | Normalize to E.164 |
| company_id | Company relationship | Link post-import |
| tags [] | tags [] | 1:1 mapping |
| custom_fields{} | custom_attributes{} | Create in advance |
Tickets Intercom Tickets
Must decide between Intercom Tickets vs Conversations per record.
| Freshdesk field | Intercom field | Notes |
|---|---|---|
| id | external_id | Maintain idempotency |
| subject | title | Default to “No Subject” if missing |
| description | description | Clean HTML and upload inline images |
| priority | priority | Map Low→Low, High→High |
| status | state | Open, Pending, Resolved, Closed |
| group_id | team_assignee_id | Map to team in Intercom |
| agent_id | admin_assignee_id | Map to admin |
| tags [] | tags [] | Direct import |
| custom_fields{} | ticket_attributes{} | Schema must pre-exist |
Freshdesk Intercom
| Freshdesk field | Intercom field | Notes |
|---|---|---|
| ticket_id | conversation_id | For one-to-one mapping |
| subject | source.subject | Optional; derive from first message |
| body_html | source.body | Sanitize and re-upload inline images |
| attachments [] | attachments [] | Upload via multipart API |
| status | state | Map Open, Pending, Closed |
| requester | contact_ids | Must exist as contact |
| agent_id | teammate_ids | Must exist as admin |
| tags [] | tag_ids | Maintain parity |
| created_at | created_at | If not backdatable, store in note text |
Solutions Help Center
| Freshdesk field | Intercom field | Notes |
|---|---|---|
| category | collection | Parent-level grouping |
| folder | section | Sub-grouping |
| article | article | Retain formatting and attachments |
| tags [] | labels [] | Preserve topic tags |
| translations [] | translations [] | Optional multilingual support |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Companies | low | Direct mapping with domain-based auto-linking. |
| Contacts | medium | Strict email uniqueness requires deduplication before import. |
| Tickets | high | Must decide between Intercom Tickets vs Conversations per record. |
| Conversations & Replies | high | Multi-step attachment handling and inline image re-hosting required. |
| Tags | low | Direct 1:1 mapping across contacts, conversations, and tickets. |
| Custom Fields | medium | Data attributes must be pre-created in Intercom with correct types. |
| CSAT Ratings | medium | Intercom conversation_rating is read-only; historical data stored as notes. |
| Help Center Articles | medium | Requires attachment re-upload and internal link revalidation. |
| SLAs & Automations | high | No API migration path; full manual rebuild in Intercom's workflow builder. |
| Time Entries | high | No Intercom equivalent; must be exported to CSV for offline reference. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Ticket Model Mismatch
Freshdesk tickets must be evaluated to decide whether they map to Intercom Tickets (asynchronous workflows) or Conversations (continuous dialogue).
Email Deduplication
Intercom enforces strict email uniqueness. Freshdesk may allow duplicate contacts across channels that must be merged before import.
Attachment Re-hosting
Freshdesk attachment URLs expire quickly. Files must be downloaded, re-uploaded via multipart API, and inline image references rewritten.
Timestamp Backdating
Intercom cannot backdate created_at timestamps. Original dates must be stored in custom fields or appended as notes.
Help Center Restructuring
Freshdesk's category/folder/article hierarchy must be mapped to Intercom's flatter Collections and Articles structure.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.