Crisp to Intercom migration is API-driven. Conversations must be extracted via REST API, transformed from Crisp's session model to Intercom's contact-conversation schema, and loaded while managing rate limits on both sides.
There is no native migration path from Crisp to Intercom. Crisp offers CSV export for contact profiles only; conversation data—messages, notes, attachments, and routing context—must be extracted via the Crisp REST API. The data models differ fundamentally: Crisp embeds company data on contact profiles while Intercom treats Companies as standalone first-class objects, and Crisp's session-based conversation model must be mapped to Intercom's Contact-centric Conversations or Tickets with a 500-part limit. A successful migration requires custom API scripting to extract, transform, and load data while managing rate limits on both platforms, with typical engineering timelines of 1–4 weeks depending on volume and complexity.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
Crisp does not have standalone "Company" objects with their own API endpoints
Company data (name, URL, description, employment, geolocation) is stored as a property on contact profiles. During migration, you'll need to deduplicate and create these as standalone Intercom Company objects, then link contacts to them.
Run at least one full test migration on a separate Intercom development workspace before
Run at least one full test migration on a separate Intercom development workspace before touching production. Intercom provides free developer workspaces for this purpose. Disable or exempt automated workflows and outbound campaigns for API-created records during migration. (intercom.com)
With a Website Token's 10,000 requests/day limit, extracting 5,000 conversations (1 call
With a Website Token's 10,000 requests/day limit, extracting 5,000 conversations (1 call to list + 1 call per conversation for messages) would take at minimum a full day, assuming no retries. For datasets exceeding 5,000 conversations, request a Plugin Token with elevated quotas from Crisp — the registration process typically takes 1–3 business days through the Crisp Marketplace.
Save extracted data to local JSON files after each batch
If extraction fails mid-way (rate limit, network error), you can resume from the last saved checkpoint instead of restarting. Use Crisp message fingerprint fields and timestamp values as dedupe keys — they're more reliable than position in thread.
Intercom enforces strict data typing for Custom Attributes (string, integer, float, boolean, date)
If your Crisp data field contains mixed types — for example, passing the string "42" into an integer field, or null into a boolean — the Intercom API will reject the entire contact payload with a 400 error. Validate and cast all values before loading.
Intercom returns a 409 Conflict if you create a contact with an email or external_id that
Intercom returns a 409 Conflict if you create a contact with an email or external_id that already exists. Build a lookup cache of email → intercom_id to avoid redundant creation attempts. Freshly created contacts may not be immediately searchable in Intercom (eventual consistency delay of a few seconds), so cache IDs locally rather than re-searching. (developers.intercom.com)
Intercom limits conversations to 500 parts
If a Crisp conversation exceeds this (possible with long-running chat sessions), you have three options: (1) truncate older messages and log what was dropped, (2) split into multiple Intercom conversations linked by a custom attribute, or (3) archive the full transcript as an attached file or external link and import a summary as the conversation body.
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 Crisp
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 Crisp → 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 custom data attributes
in Intercom for every custom data key you use in Crisp. Use POST /data_attributes with the correct model (contact, company, or conversation) and data_type. Mismatched types will cause silent failures or rejected payloads.
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Create ticket types
if you plan to import certain Crisp conversations as Intercom tickets. (developers.intercom.com)
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Set up tags
that correspond to your Crisp segments, plus a source:crisp-migration tag for all migrated records.
Crisp → Intercom specifics
- Outgrowing Crisp's CRM
- Crisp embeds company data directly on contact profiles rather than maintaining standalone company objects. Teams managing B2B accounts with multi-contact relationships hit this ceiling fast — there's no way to query "all contacts at Company X" without scanning every profile.
- Advanced ticketing
- Crisp's ticketing system (available only on the Unlimited plan at €295/month per workspace) is relatively basic. Intercom's tickets have first-class API support, typed attributes, configurable state machines, and SLA tracking.
- Marketing automation
- Intercom's Series (outbound campaigns), Product Tours, and Fin AI agent offer depth that Crisp's campaign tools don't match.
- Integration ecosystem
- Intercom's marketplace and deep integrations with tools like Salesforce, HubSpot, and Stripe provide more flexibility at scale.
- B2B account management
- Intercom's Company object enables support teams to manage SLAs and routing based on account tier — something Crisp cannot do natively.
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 Crisp 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 Crisp 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 Crisp 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
Crisp → Intercom specifics
- Contacts
- Total count, percentage with email addresses (contacts without email cannot be reliably matched or merged in Intercom)
- Conversations
- Total count, date range, average messages per conversation (calculate expected API calls: conversations × 2 minimum)
- Segments
- List all segments applied to contacts and conversations
- Custom data keys
- Enumerate all contact-level and session-level custom data keys in use — note the data type of each (string, integer, boolean)
- Company data
- How many contacts have company information set? How many unique company names exist after normalization?
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 Crisp → 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 Crisp → 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.
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Map Crisp contacts to Intercom contacts
set role as user (if they have an external_id or email) or lead (if anonymous/visitor-only)
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Deduplicate and create Company objects
extract unique company names from contact profiles, normalize casing and whitespace, prepare standalone Intercom Company records
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Convert Crisp custom data keys
to Intercom-compatible attribute names (lowercase, underscores instead of spaces, no special characters)
Data Format Converter Reshape the export into the format Intercom's importer expects
Crisp → Intercom specifics
- Enforce data type consistency
- cast values to match the Intercom attribute type you pre-created (e.g., ensure integer fields don't contain strings)
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 Crisp 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 Crisp 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 Crisp read-only rather than cancelled — cancelling the old contract on day one removes your only fallback.
Crisp → Intercom specifics
- Field-level spot checks
- Sample 50 contacts — verify email, name, custom attributes, company association, and tags match Crisp source data.
- Conversation integrity
- Sample 20 conversations (including edge cases) — verify message order, timestamps, sender attribution (admin vs. user), and attachment accessibility (click every attachment URL).
- Segment-to-tag mapping
- Confirm every Crisp segment appears as an Intercom tag with the correct number of associations.
- Company linkage
- Verify contacts are linked to the correct Intercom Company objects. Spot-check 10 companies with multiple contacts.
- Timestamp accuracy
- Confirm created_at on migrated conversations matches original Crisp timestamps (not the migration run date).
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 Crisp 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 Crisp 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.
Concept Equivalent
| Crisp field | Intercom field | Notes |
|---|---|---|
| Contact (People) | Contact (User or Lead) | Intercom splits contacts into user (identified, has external_id or email) and lead (anonymous/visitor) roles |
| Company (embedded on contact) | Company (standalone object) | Crisp stores company as a property on contact; Intercom treats Company as a first-class entity with its own API and relationships |
| Conversation | Conversation or Ticket | Routing decision needed: which Crisp conversations become Intercom conversations vs. tickets? |
| Messages (in conversation) | Conversation Parts | Intercom limits to 500 parts per conversation |
| Segments | Tags | Crisp segments are tag-like labels; map directly to Intercom tags |
| Custom Data (contact-level) | Custom Data Attributes | Intercom requires attributes to be pre-defined via API before population |
| Custom Data (session-level) | Conversation custom attributes or note | No direct equivalent for per-session key-value pairs in standard conversations; ticket custom attributes available if importing as tickets |
| Notes (private) | Notes (on contact) or admin notes in conversation | Crisp private notes → Intercom conversation parts with type note |
| Events (page views, custom) | Events | Intercom supports custom events via POST /events but does not support backdating — events are recorded at submission time only |
| Knowledge Base articles | Articles (Help Center) | Intercom has an Articles API; Crisp articles need re-creation |
| Chatbot / Workflows | Custom Bots / Workflows | Must be rebuilt manually in Intercom's visual builder — no API import |
Crisp Intercom
| Crisp field | Intercom field | Notes |
|---|---|---|
| people.email | contacts.email | Direct — primary identifier for matching |
| people.person.nickname | contacts.name | Direct (split into first/last if needed via space delimiter) |
| people.person.avatar | contacts.avatar | Direct |
| people.person.phone | contacts.phone | Normalize to E.164 format with country code |
| people.person.geolocation.country | contacts.custom_attributes.country | Custom attribute — must be pre-created |
| people.person.geolocation.city | contacts.custom_attributes.city | Custom attribute — must be pre-created |
| people.company.name | companies.name | Deduplicate, normalize casing → create Company object |
| people.company.url | companies.website | Direct |
| people.segments [] | tags [] | Create tag if not exists, apply to contact |
| people.data.* | contacts.custom_attributes.* | Pre-create attribute with correct type; sanitize key names |
| conversation.session_id | N/A (internal) | Use as idempotency key in migration log |
| conversation.meta.subject | conversations.body (first line) | Prepend to first message body |
| conversation.state | conversations.state | Map: resolved→closed, pending→open, unresolved→open |
| message.content | conversation_parts.body | Strip HTML for first message (plain text required); HTML allowed in replies |
| message.from | conversation_parts.author | operator→admin, user→user |
| message.timestamp | conversation_parts.created_at | Divide by 1000 if >10 digits (ms→seconds) |
| message.type: file | Attachment URL | Re-host — Crisp CDN URLs (storage.crisp.chat) expire after subscription cancellation |
| message.type: note | Conversation part (type: note) | Create as admin note with message_type: "note" |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Contacts | low | Crisp supports CSV export for up to 200,000 contacts and API extraction for larger sets; contacts with email addresses map reliably to Intercom Users or Leads. |
| Companies | high | Crisp has no standalone company objects, requiring deduplication of embedded company properties across all contacts and creation of new Intercom Company records via API. |
| Conversations | medium | Conversations must be extracted via API and a routing decision made for each (Conversation vs. Ticket), with Intercom's 500-part limit potentially truncating lengthy threads. |
| Messages & Conversation Parts | medium | Replaying every historical message can increase API volume by 10–100×, leading many teams to compress legacy transcripts into admin notes instead of individual parts. |
| Attachments | medium | Attachments must be individually downloaded from Crisp and re-uploaded to Intercom with no bulk transfer mechanism, and large files may fail silently. |
| Custom Data Attributes | medium | All Intercom custom attributes must be pre-defined via API before population, and Crisp's session-level custom data has no direct equivalent in standard Intercom conversations. |
| Segments / Tags | low | Crisp segments function as tag-like labels and map directly to Intercom tags, making this a straightforward field-level mapping. |
| Events | high | Intercom does not support backdating events, so all historical Crisp events (page views, custom events) will lose their original timestamps upon import. |
| Knowledge Base Articles | low | Intercom has a dedicated Articles API, but Crisp articles must be individually re-created with content reformatting rather than bulk-transferred. |
| Chatbots & Workflows | high | Crisp chatbot flows and automation workflows cannot be exported or imported via API and must be completely rebuilt manually in Intercom's visual builder. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
No Native Conversation Export
Crisp does not support CSV export for conversation data, requiring full extraction via the REST API for messages, notes, attachments, and routing context.
Company Object Reconstruction
Crisp embeds company data as properties on contact profiles, so teams must deduplicate, create standalone Intercom Company objects via API, and link contacts to them.
Conversation-to-Ticket Routing Decisions
Each Crisp conversation must be evaluated and mapped to either an Intercom Conversation or a Ticket, requiring pre-created ticket types and custom routing logic.
Rate Limit Management
Neither Crisp nor Intercom offers bulk endpoints, meaning every record requires an individual API call with careful rate-limit throttling on both sides.
Event Backdating Not Supported
Intercom does not support backdating events, so historical page views and custom events from Crisp will be recorded at submission time rather than their original timestamps.
Custom Data Pre-Definition Requirement
Intercom requires all custom data attributes to be explicitly defined via API before they can be populated, unlike Crisp's flexible key-value custom data on contacts and sessions.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can I export conversation history from Crisp as CSV?
No. Crisp only supports CSV export for contact profiles (limited to 200,000 contacts). Conversation data, including messages and attachments, can only be extracted through the Crisp REST API.
Does Intercom support importing historical conversation timestamps?
Yes. Intercom's Create Conversation API endpoint accepts a created_at field as a UTC Unix timestamp, specifically designed for migrating past conversations from another source.
Should Crisp chats become Intercom conversations or tickets?
Use Intercom conversations when you want Messenger-style historical context. Use tickets when you need structured states, typed attributes, and queue-based routing. Create ticket types in Intercom before importing.
How long does a Crisp to Intercom migration take?
It depends on data volume. A small migration (under 5,000 conversations) can be completed in 2–5 days. Larger datasets (50K+ conversations) typically take 1–3 weeks including test runs, transformation, and validation. With a managed service, the data cutover itself takes hours to a few days.
What Crisp data cannot be migrated to Intercom?
Chatbot flows and workflows must be rebuilt manually in Intercom. CSAT/satisfaction survey responses, SLA policy history, and saved replies (macros) cannot be imported via API. Agent performance metrics are also not transferable.