Desk365 to Intercom migration requires translating a ticket-centric, M365-integrated model into a contact-centric platform. Extract via API, import contacts first, then tickets with full thread history.
Migrating from Desk365 to Intercom requires translating a ticket-centric, Microsoft 365-integrated helpdesk into a contact-centric, messenger-first platform — no native migration path exists between the two systems. The fundamental data model difference is significant: Desk365 organizes work around tickets linked to Microsoft Azure AD identities, while Intercom requires all tickets and conversations to be anchored to pre-existing Contact records identified by email or external ID. Custom migration scripting is required to extract threaded conversations from Desk365's separate reply and notes endpoints, sanitize Microsoft Teams and HTML-formatted content, flatten multi-level custom fields into Intercom's flat attribute schema, and handle file attachments via intermediary storage. Several Desk365 objects — including Time Entries, Asset Management records, Approval workflows, Change Management items, and SLA policies — have no Intercom equivalent and must be archived externally.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
TL;DR — Desk365 to Intercom Migration
A Desk365 to Intercom migration translates a ticket-centric, Microsoft 365-integrated helpdesk into a contact-centric, messenger-first platform. Realistic timeline: 1–2 weeks for under 20K tickets, 2–4 weeks for larger volumes. The hard parts: extracting threaded conversations from Desk365's API (which returns tickets and replies at separate endpoints), mapping multi-level custom fields to Intercom's flat attributes, handling attachments through intermediary storage, suppressing Intercom automations during import, and working within Intercom's API rate limits. Desk365 objects with no Intercom equivalent include Time Entries, Asset Management records, Approval workflows, Change Management items, and SLA policies. Build in-house if you have a developer, low ticket volume, and simple custom fields. For anything larger, a managed migration pays for itself in rework avoided.
Critical decision
Ticket or Conversation? Every Desk365 ticket must land as either an Intercom Ticket or an Intercom Conversation. This is one of the most important decisions to make before you start scripting. Intercom Tickets have structured states (submitted, in_progress, waiting_on_customer, resolved) and support Ticket Types with custom attributes. Conversations are lighter, thread-based, and better for historical chat-style data. For most migrations from ticket-centric platforms like Desk365, Intercom Tickets are the right choice because they preserve the structured workflow your team is used to — and critically, the Tickets API supports backdating reply timestamps while the Conversations API does not.
Desk365's API documentation is instance-specific — access it at
Desk365's API documentation is instance-specific — access it at https://<yoursubdomain>.desk365.io/apis/api-docs.html. Available fields vary by plan (Standard, Plus, Premium). Confirm which custom field endpoints are available on your plan before writing extraction scripts.
Intercom has no "Closed" ticket state
Desk365 distinguishes between Resolved and Closed — Desk365 has a default rule that automatically closes resolved tickets after 120 hours if no further activity occurs. In Intercom, both map to resolved. If the distinction matters for your reporting or SLA calculations, store the original Desk365 status as a custom attribute (desk365_original_status). Be aware that any Intercom Workflow triggered on ticket resolved will count both Resolved and Closed Desk365 tickets as the same event — audit your Workflows for this before re-enabling them post-migration.
Collection limit
There is a maximum limit of 500 articles per Help Center collection. To manage more articles, split your content across multiple collections or sub-collections.
Intercom test workspace rate limits differ from production
Test workspaces have lower rate limits and data caps. Run your initial sandbox validation with a representative sample (500–1,000 tickets) rather than a full dataset, then extrapolate timing before committing to a full production 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 Desk365
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 Desk365 → 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.
Desk365 → Intercom specifics
- Identity management
- Desk365 relies on Microsoft 365 and Azure Active Directory (Azure AD) for user and agent identity. Intercom uses standard email addresses or an external_id. You need to map Azure AD User Principal Names (UPNs) to Intercom contact profiles — detaching from M365's identity layer is the first hurdle.
- Ticket-centric vs. contact-centric
- Desk365's default ticket statuses are Open, Pending, Resolved, and Closed, with tickets representing the core unit of work. Intercom organizes everything around Contacts — a fundamental data model mismatch we also cover in our Zendesk to Intercom migration guide — meaning every ticket and conversation must be linked to an existing Contact record. Contacts must exist before you can create tickets.
- Conversation thread structure
- Desk365 stores ticket replies and internal notes as part of the ticket record. Intercom uses a Conversation Parts / Ticket Parts model with a hard limit of 500 parts per ticket. If a Desk365 ticket has hundreds of replies plus internal notes, you need to plan for that ceiling.
- Thread formatting
- Desk365 captures Microsoft Teams chat logs and rich HTML emails. Intercom supports a specific subset of HTML in its conversation parts. Complex Teams formatting — nested tables, adaptive cards, Microsoft Word-style inline CSS (e.g., <span style="mso-bidi-font-weight: normal;">) — will break or render poorly if not sanitized before import.
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 Desk365 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 Desk365 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 Desk365 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 -
Build exponential backoff
on a 429 response, retry after 1s, then 2s, 4s, 8s, up to a maximum of 60s.
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Run batches sequentially within a single thread
multi-threaded imports without a centralized rate-limit manager will cause cascading 429s.
Desk365 → Intercom specifics
- Log every 429
- with the full request payload so you can replay only failed requests.
- Do not share your migration app's rate limit with live production traffic
- create a dedicated private app for the migration with its own rate limit allocation.
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 Desk365 → 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 Desk365 → 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.
Desk365 → Intercom specifics
- Custom fields mismatch
- Desk365 supports single-level field types (Dropdown, Text Input, Checkbox, Date, Number) and multi-level fields that dynamically populate child options based on parent selection. Intercom supports flat custom attributes only (string, integer, float, boolean, datetime, list) — there is no native multi-level field support.
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 Desk365 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 Desk365 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 Desk365 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 Desk365 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 Desk365 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.
Object Equivalent
| Desk365 field | Intercom field | Notes |
|---|---|---|
| Ticket | Ticket or Conversation | Decide before scripting — this shapes everything |
| Contact | Contact (User or Lead) | Email is the minimum required field |
| Company | Company | Matched by company_id or name |
| Ticket Reply (agent/customer) | Ticket Part (comment) | Preserves conversation thread |
| Internal Note | Ticket Part (note) | Admin-only visibility |
| Knowledge Base Article | Article | HTML body preserved |
| KB Category/Folder | Collection | Up to 3 levels deep |
| Custom Ticket Field | Ticket Type Attribute or Data Attribute | Flat types only — no multi-level |
| Tag | Tag | Tags applied post-creation |
| SLA Policy | SLA in Intercom | No API import |
| Automation Rule | Workflow | Platform-specific logic |
| Time Entry | No equivalent | Export as CSV/JSON for reference |
| Asset Record | No equivalent | Intercom has no asset management |
| Approval / Change Management | No equivalent | ITSM-specific features |
| Department | No direct equivalent | Map to tags or contact segments |
Status Ticket State
| Desk365 field | Intercom field | Notes |
|---|---|---|
| Open | in_progress | |
| Pending | waiting_on_customer | |
| Resolved | resolved | |
| Closed | resolved | Intercom has no "closed" state |
| Custom statuses | Map to nearest state | Store original as custom attribute |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Tickets | high | Tickets require a pre-migration decision on whether to map to Intercom Tickets or Conversations, must be extracted across multiple endpoints, and are subject to the 500 conversation parts per ticket limit — making them the highest-complexity entity in the migration. |
| Contacts | medium | Contacts require deduplication before import, carry a billing risk if large volumes of inactive historical contacts trigger a plan tier upgrade, and must be created before any ticket association can be established. |
| Companies | low | Companies map cleanly to Intercom's Company object via company_id or name and can be created via standard API calls with minimal transformation required. |
| Custom Fields | high | Desk365's multi-level custom fields have no equivalent in Intercom's flat attribute schema, requiring custom transformation logic to decompose or collapse hierarchical field structures before import. |
| Ticket Replies and Internal Notes | high | Replies and notes are stored at separate Desk365 API endpoints from ticket metadata and must be individually fetched per ticket ID, stitched chronologically, and sanitized of unsupported HTML before being posted as Ticket Parts in Intercom. |
| Attachments | high | Desk365's CSV export contains only attachment references rather than files, and binary files must be routed through intermediary storage before being re-uploaded to Intercom, adding pipeline complexity and potential for data loss. |
| Knowledge Base Articles | medium | Article HTML bodies are generally portable via the Intercom Articles API, but category and folder hierarchy must be mapped to Intercom Collections, which support only up to three levels of nesting. |
| Tags | low | Tags are supported natively in Intercom and can be applied post-creation via API, making them a low-risk entity provided the tagging step is sequenced after ticket and contact creation. |
| SLA Policies | high | Desk365 SLA policies have no API import path in Intercom and must be manually rebuilt in the Intercom admin UI, with no automated migration option available. |
| Time Entries / Asset Records | high | Desk365 Time Entries, Asset Management records, Approval workflows, and Change Management items have no equivalent objects in Intercom and must be archived externally as CSV or JSON, resulting in permanent functional data loss within the platform. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Threaded Conversation Extraction
Desk365 stores ticket replies and internal notes at separate API endpoints from the ticket metadata, requiring a multi-step extraction script that stitches the initial description, replies, and notes into a single chronological record per ticket before import.
Azure AD Identity Detachment
Desk365 uses Microsoft 365 and Azure Active Directory for agent and user identity, meaning User Principal Names (UPNs) must be explicitly mapped to Intercom email-based or external_id contact profiles before any ticket association can be established.
Multi-Level Custom Field Flattening
Desk365 supports dynamically populated multi-level fields where child options depend on parent selections, but Intercom only supports flat custom attributes (string, integer, float, boolean, datetime, list), requiring hierarchical field logic to be decomposed or collapsed before import.
Teams Formatting Sanitization
Desk365 captures Microsoft Teams chat logs and rich HTML emails containing nested tables, adaptive cards, and Microsoft Word-style inline CSS that must be sanitized before import because Intercom only supports a specific subset of HTML in conversation parts.
Conversation Part Volume Limits
Intercom enforces a hard limit of 500 parts per ticket, so Desk365 tickets with large reply and internal note counts must be identified and handled — through truncation, summarization, or splitting — before attempting import.
Contact-First Import Sequencing
Intercom requires all Contact records to exist before tickets can be created, meaning the migration must be strictly sequenced — contacts and companies first, agent mappings second, and tickets last — with deduplication applied to Desk365 contacts before any import begins.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can I export all my data from Desk365?
Desk365 offers CSV exports from the UI for ticket metadata and API v3 for full programmatic access including ticket descriptions in HTML and plain text. The CSV export does not include individual replies, attachments, or internal notes — use the API for complete data extraction.
How long does a Desk365 to Intercom migration take?
For teams with under 5,000 tickets and simple fields, expect 3–5 days. Mid-size migrations (5K–20K tickets) take 1–2 weeks. Larger datasets with attachments and complex custom fields can take 2–6 weeks depending on scope.
Does Intercom have a built-in import tool for Desk365?
No. Intercom does not offer a native Desk365 import. You need to extract data via Desk365's API or CSV export, transform it to match Intercom's data model, and import using Intercom's REST API (Contacts, Tickets, Articles endpoints).
What Desk365 data cannot be migrated to Intercom?
Time entries, asset management records, approval/change management workflows, and SLA policies have no Intercom equivalent. These should be exported as CSV or JSON archives. Automation rules and Microsoft Teams bot configurations must be manually rebuilt.
How do I keep historical timestamps when importing into Intercom?
If you import Desk365 tickets as Intercom Tickets, the reply endpoint supports a created_at parameter for preserving original dates. If you use Conversations instead, the creation timestamp cannot be backdated — store the original date in a custom attribute and prepend it to the message body.