Dixa to Crisp migration is moderate complexity. Custom attribute translation, 31-day API export windows, and no native Dixa adapter in Crisp's import tool are the main challenges. Budget 2–4 weeks.
There is no native migration path from Dixa to Crisp — no built-in importer, no official adapter, and no one-click transfer tool exists between the two platforms. The fundamental data model differences are significant: Dixa uses schema-defined custom attributes, offer-based push routing, and linked-conversation relationships (parent/child, merged), while Crisp centers on freeform key-value data objects, inbox-based workflows, and sequential sessions with no native linked-conversation concept. Custom ETL work is required to extract data via Dixa's Exports API (constrained to 31-day query windows), transform conversation structures, flatten linked-conversation relationships into notes or custom data fields, and load into Crisp's REST API — with separate handling needed for contacts, conversations, attachments, and internal notes.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
Data loss risk
Dixa's Exports API message endpoint does not return internal notes. If you only use the Exports API for messages, you'll lose all internal notes. You must also extract conversation_wrapup_notes from the conversation export endpoint or use the Dixa API v1 conversation endpoint.
Critical step
Before starting any conversation import, contact Crisp support and ask them to block outbound emails for your workspace. Imported conversations can trigger email notifications to customers if this isn't disabled. Re-enable after the import is complete and validated.
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 Dixa
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 Crisp can hold your support model
Walk your current workflow through Crisp: 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 Dixa → Crisp 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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Record count comparison
Total conversations in Dixa export vs. total sessions in Crisp. Accept <1% variance for filtered spam/bot conversations.
Dixa → Crisp specifics
- Cost structure
- Dixa starts at €89/user/month (Growth plan) with a minimum of 7 seats, and API access is only available on Ultimate (€139) or Prime (€179). Crisp uses flat-rate workspace pricing — the Plus plan at $295/month includes unlimited conversations and up to 20 agents. Worked example: a 15-agent team on Dixa Ultimate pays 15 × €139 = €2,085/month (≈$2,290). The same team on Crisp Plus pays $295/month — an 87% reduction in platform cost.
- Simplicity
- Crisp is a lighter platform: live chat, shared inbox, knowledge base, CRM contacts, campaigns, and an AI agent in a single workspace. Teams that don't need Dixa's native telephony, IVR, and advanced routing often find Crisp's simpler model faster to operate.
- Marketing automation
- Crisp includes campaign features (email, chat, in-app) natively, with targeting based on segments, events, and custom data. Dixa has no native outbound marketing layer.
- Agency and multi-brand use cases
- Crisp's per-workspace pricing makes it practical to run multiple brand workspaces without per-seat multiplication.
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 Dixa 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 Dixa 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 Dixa 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 Dixa → Crisp 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 Dixa → Crisp 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 Crisp has no equivalent, decide between a new custom field, a tag, or a note appended to the ticket body — and record which.
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Resolve identity and threading
Decide how source IDs are preserved — most platforms will not let you set the primary key, so keep the original ID in a custom field. Without it, reconciliation becomes fuzzy matching and every future support question about an old ticket is unanswerable.
Losing the original ticket ID makes reconciliation and rollback effectively impossible.
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Plan attachments, inline images and threading order
Confirm size limits, allowed MIME types, and whether inline images survive as attachments or need rehosting. Decide the comment ordering and author attribution rules: comments loaded out of order, or all attributed to the API user, destroy the conversation history agents rely on.
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Freeze and sign off the mapping spec
Version the spec, walk the support lead through it row by row, and get explicit sign-off. Any change after this point goes through change control — mid-flight mapping edits are how partial loads happen.
Don't move on until
- Every source field is mapped, deliberately dropped, or parked in a custom field
- Status, priority and channel value maps agreed with the support lead
- Mapping spec version-controlled and signed off
04 Test Migration Prove the pipeline on a small, representative slice.
Objective A pilot load into a Crisp sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Crisp 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 Crisp'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.
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Test migration
Run the full pipeline against a subset (e.g., last 30 days of data) into a Crisp test workspace. Always validate in a sandbox before touching production.
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Use a test workspace
for the first full import. Only import into production after validation passes.
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 Crisp, 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 Dixa 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 Crisp'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 Dixa 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 Dixa read-only rather than cancelled — cancelling the old contract on day one removes your only fallback.
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Track imported session IDs
in your persistent ID map so you can programmatically delete them via DELETE /v1/website/{website_id}/conversation/{session_id} if needed. For 100K conversations at Crisp's daily API quota, a full rollback would take approximately 2–5 days depending on your quota tier.
Dixa → Crisp specifics
- Back up everything
- from Dixa before starting. Keep the raw JSON exports in versioned cloud storage (S3, GCS) with at least 90-day retention.
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 Dixa and Crisp 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 Crisp, 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 Dixa 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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Contact count
Dixa end users vs. Crisp people profiles. Account for deduplication reducing the Crisp count.
Dixa → Crisp specifics
- Message count per conversation
- Spot-check 50–100 conversations for message count parity.
- Segment coverage
- Verify that all Dixa tags appear as Crisp segments by querying GET /v1/website/{website_id}/conversations/list and checking segment values.
- Custom data integrity
- Sample 20 contacts and compare custom data values side-by-side between your Dixa export JSON and Crisp's people data endpoint.
- Timestamp integrity
- Verify that message timestamps in Crisp match the original Dixa timestamps, not the import time.
- Pre-migration snapshot
- Export full record counts from Dixa — total conversations, messages, end users, tags. Store these counts in a validation spreadsheet.
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
| Dixa field | Crisp field | Notes |
|---|---|---|
| End User | People Profile | Match on email. Crisp enforces email uniqueness. |
| Conversation | Conversation (Session) | 1:1 mapping. Both use conversation as the core unit. |
| Message | Message | Crisp supports types: text, note, file, audio, event. |
| Internal Note / Wrap-up Note | Message (type: note) | Dixa stores wrap-up notes separately; Crisp treats all notes as message type. Both types must be extracted and imported. |
| Tag | Segment | Dixa tags are conversation-level; Crisp segments apply to both conversations and contacts. |
| Custom Attribute (End User) | People Custom Data | Dixa: typed schema (UUID-keyed). Crisp: freeform key-value JSON in data object. |
| Custom Attribute (Conversation) | Session Data | Same transformation challenge. |
| Queue | Inbox | Rebuild manually; routing logic differs fundamentally. |
| Flow | Workflow | No migration path; rebuild in Crisp's visual builder. |
| CSAT Rating | No direct structural equivalent | Store as conversation metadata/custom data or re-implement using Crisp's native satisfaction survey. Historical ratings import as data.csat_score and data.csat_comment. |
| Agent | Operator | Recreate manually in Crisp. |
| Linked Conversations | No equivalent | Dixa's link_type (merged, parent/child) has no Crisp analog. Preserve as data.dixa_linked_to and data.dixa_link_type custom fields on the session. |
| Webhooks | Plugin webhooks or Crisp hooks | Must be rebuilt; endpoint configurations and event types differ. |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Conversations | high | Conversations require custom ETL with 31-day windowed extraction from Dixa's Exports API, data transformation, and careful loading via Crisp's API with rate-limit handling and quota negotiation. |
| Messages | high | Message bodies must be extracted, ordered, and loaded with preserved original timestamps, and Dixa's message export excludes internal notes which require separate extraction. |
| Contacts | low | Contacts can be migrated via straightforward CSV export from Dixa and CSV import into Crisp, which supports email, name, company, segments, and custom data mapping. |
| Custom Attributes | high | Dixa's typed, schema-defined attributes (dropdown, integer, text) identified by UUID must be translated into Crisp's freeform key-value data objects with non-trivial mapping logic for each attribute type. |
| Tags / Segments | medium | Dixa tags (string labels on conversations) must be mapped to Crisp segments (labels on conversations and contacts), requiring a mapping layer but no complex structural transformation. |
| Internal Notes | high | Internal notes are excluded from Dixa's message_export endpoint and must be separately extracted from conversation_wrapup_notes, making them easy to miss entirely during migration. |
| Attachments | high | Dixa attachment URLs may expire or require authentication, necessitating download and re-upload to Crisp with individual file-level error handling. |
| CSAT Ratings | medium | CSAT ratings can be extracted from Dixa's exports but must be stored as custom data fields on Crisp conversations since Crisp's native satisfaction model may not map one-to-one. |
| Linked Conversations | high | Dixa's parent/child and merged conversation relationships have no structural equivalent in Crisp and must be flattened into notes or custom data fields, losing the relational link. |
| Knowledge Base Articles | medium | Both platforms support categories and articles but use different content formats — Crisp is markdown-based — requiring content reformatting during migration. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
No Native Dixa Adapter
Crisp's open-source import tool ships with adapters for Zendesk, Gorgias, Help Scout, and others, but no Dixa adapter exists, requiring teams to write a custom adapter (~200–400 lines of Node.js) to transform Dixa's export format into Crisp's expected schema.
31-Day Export Window Constraint
Dixa's Exports API limits queries to 31-day date ranges, meaning extracting multi-year conversation history requires dozens of sequential API calls with individual error handling and resumption logic.
Linked Conversation Flattening
Dixa's parent/child and merged conversation relationships via link_type and linked_to fields have no structural equivalent in Crisp, requiring these relational links to be flattened into internal notes or custom data fields to preserve the audit trail.
Custom Attribute Schema Translation
Dixa uses typed, schema-defined custom attributes (dropdown, integer, text) identified by UUID, which must be translated into Crisp's freeform key-value data objects with non-trivial mapping logic.
Internal Notes Extraction Gap
Dixa's message_export endpoint does not include internal notes, requiring separate extraction of conversation_wrapup_notes from the conversation export or the Dixa API conversation endpoint to avoid losing all internal annotations.
Email Suppression During Import
Crisp must be configured to block outbound emails before importing historical conversations, requiring coordination with Crisp support to prevent imported conversations from triggering unexpected customer-facing messages.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can I migrate conversation history from Dixa to Crisp?
Yes, but not through CSV import alone. Crisp's native import is contact-focused. Historical conversations require Crisp's crisp-import-conversations tool with a custom Dixa adapter, or direct API calls using the message endpoint with original timestamps. Internal notes require a separate extraction step from Dixa's conversation export — the message export endpoint does not include them.
Does Crisp have a built-in Dixa migration tool?
Not directly. Crisp's open-source crisp-import-conversations tool supports adapters for Zendesk, Gorgias, Help Scout, Tidio, GrooveHQ, and WHMCS — but no Dixa adapter is included. You need to write a custom adapter or use the raw Crisp API for data loading.
What Dixa plan do I need for API-based migration?
Dixa's API access is only available on the Ultimate and Prime plans (€139–€179/user/month). The Growth plan does not include API access. Without API access, you're limited to CSV exports, which don't include message bodies or threaded conversation history.
How long does a Dixa to Crisp migration take?
For 50,000–150,000 conversations, expect 2–4 weeks including data extraction, transformation script development, test migration, validation, and cutover. Smaller datasets (under 10K conversations) can be completed in 1–2 weeks. The 31-day API export window constraint and Crisp's daily plugin quotas are the main time factors.
Will my customers receive emails during the Crisp migration?
They can if you don't take precautions. Contact Crisp support before importing and ask them to block outbound emails for your workspace. Imported conversations can trigger email notifications to customers if this isn't disabled. Re-enable notifications after the import is complete and validated.