Zendesk to Dixa is moderate complexity for data (API-based, timestamps preserved) but high complexity for workflows — triggers, views, and SLAs must be rebuilt as Dixa Flows manually.
There is no native migration path from Zendesk to Dixa; all data movement requires API-based extraction and loading or CSV intermediaries. The fundamental architectural difference is Zendesk's ticket-centric, pull-based model versus Dixa's conversation-centric, push-based routing system, which means ticket lifecycle statuses, views, triggers, and automations must be redesigned rather than directly mapped. Custom work is required to rebuild Zendesk triggers and automations into Dixa's visual Flows, remap custom field types that lack Dixa equivalents, and accept data loss for objects like Custom Objects, Explore dashboards, and multi-action macros that have no Dixa destination.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
TL;DR Ticket history, contacts, tags, and custom fields extract cleanly from Zendesk and
TL;DR Ticket history, contacts, tags, and custom fields extract cleanly from Zendesk and load into Dixa via API with timestamp preservation. The routing redesign (Zendesk triggers and views → Dixa Flows and Queues) is the most time-consuming task. Custom Objects (Enterprise) have no Dixa destination. Realistic timeline for ~40 agents and 200K tickets: 4–8 weeks including planning, Flow design, test migration, and validation. Simple Zendesk configurations can be migrated in-house; teams with complex trigger stacks, heavy Explore usage, or many marketplace app dependencies should use a managed service.
If you use Zendesk Full JSON export, test your largest tickets first
The export is NDJSON format and is recommended for accounts over 200,000 tickets. However, tickets over 1 MB can be exported without comments, and a single ticket can contain up to 5,000 comments. The export also omits records updated within six minutes of the export start. (support.zendesk.com)
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 Zendesk
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 Dixa can hold your support model
Walk your current workflow through Dixa: 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 Zendesk → Dixa 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.
Zendesk → Dixa specifics
- Unified agent workspace
- Zendesk spreads work across Support, Chat, Talk, and Guide as separate products. Dixa puts voice, email, chat, and social in one workspace with full customer history, order data, and AI suggestions visible instantly.
- Push-based routing
- In a ticketing system, messages sit in a shared inbox and agents pick what they work on. In Dixa, every conversation is automatically routed to the best-available agent based on skills, language, and customer priority. This eliminates cherry-picking and typically improves first-response time.
- Native telephony
- Zendesk Talk is a separate add-on. Dixa offers browser-based cloud telephony with IVR built in the same visual flow builder, callback instead of hold, voicemail in the same queue, call recording, transcription and number transfer across 60+ countries.
- Configuration simplification
- Teams with 50+ Zendesk triggers and years of accumulated configuration debt use Dixa's visual Flow builder to consolidate routing logic onto a single canvas per channel. This is often as much a motivation as any feature gap.
- AI cost positioning
- Dixa markets lower per-resolution AI costs compared to Zendesk. Treat vendor cost comparisons as positioning until you price your exact seat mix, AI usage, and telephony footprint. (dixa.com)
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 Zendesk 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 Zendesk 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 Zendesk 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 Zendesk → Dixa 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 Zendesk → Dixa 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 Dixa 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 Dixa sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Dixa 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 Dixa'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 Dixa, 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 Zendesk 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 Dixa'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 Zendesk 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 Zendesk 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 Zendesk and Dixa 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 Dixa, 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 Zendesk 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
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Tickets/Conversations | low | Dixa's conversation import endpoint accepts historical conversations with original timestamps and message arrays, enabling clean ticket-to-conversation migration with full history preservation. |
| Contacts/End Users | low | Core contact fields (name, email, phone) map directly and Dixa supports bulk creation via API, though Zendesk custom user fields require remapping to Dixa custom attributes. |
| Tags | low | Both platforms support free-form tags, though Zendesk's lowercase-with-underscore convention should be validated against Dixa's character restrictions before import. |
| Custom Fields | medium | Text and dropdown types map cleanly, but Zendesk field types like regex, lookup relationship, numeric, date, multi-select, and credit card have no Dixa equivalent and must be degraded to text or dropped. |
| Organizations | medium | While conceptually equivalent, Dixa's public API does not clearly expose a bulk organization import endpoint as of mid-2025, potentially requiring workarounds via custom cards or external CRM references. |
| Triggers and Automations | high | Zendesk's rule-based trigger and automation stacks must be entirely redesigned as Dixa visual Flows, which is a manual process representing the most time-consuming task in the migration. |
| Macros | medium | Dixa canned responses support text insertion only, so multi-action macros that set fields, change statuses, or add tags must be decomposed into Flow logic or manual agent steps. |
| Custom Objects | high | Zendesk Enterprise Custom Objects have no Dixa destination whatsoever, requiring lossy flattening into custom attributes, migration to an external system, or complete data loss. |
| SLA Policies | high | SLA rules must be reconfigured within Dixa's Flow or queue configuration with no 1:1 mapping, and historical SLA compliance data does not transfer. |
| Attachments | low | Attachments can be uploaded via Dixa's API, but supported file types are limited to common formats (.jpg, .png, .pdf, .doc, .docx, .pptx, .xlsx), requiring preflight validation of unusual legacy file types. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Trigger-to-Flow Redesign
Zendesk's trigger and automation stacks (often 30–50 rules) must be manually consolidated into 3–8 Dixa visual Flows, which cannot be created via API and require hands-on rebuilding in Dixa's editor.
Smaller Integration Ecosystem
Zendesk's 1,500+ marketplace apps must be audited against Dixa's significantly smaller integration ecosystem, and any trigger-based webhooks to external systems must be rebuilt using Dixa's different event-based webhook model.
Custom Field Type Incompatibilities
Zendesk custom field types such as regex, lookup relationship, numeric, date, multi-select, and credit card have no Dixa equivalent and must be flattened to text or dropped entirely.
Ticket Status Model Mismatch
Zendesk's five-state ticket lifecycle (new/open/pending/solved/closed) must be compressed into Dixa's three-state conversation model (open/pending/closed), losing the distinction between solved and closed.
API Rate Limit Constraints
Dixa's rate limit of 10 requests per second with an 864,000 daily quota becomes the bottleneck for large migrations, requiring careful payload bundling to avoid 4–6x request multiplication across conversations, messages, notes, and tags.
No Custom Objects Destination
Zendesk Enterprise Custom Objects have no Dixa equivalent, forcing teams to either flatten relational data into custom attributes with loss of cardinality, migrate it to an external CRM, or discard it.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
How long does a Zendesk to Dixa migration take?
Most teams complete the migration in 2–4 weeks. Complex instances with heavy trigger debt, custom objects, Zendesk Talk number porting, or large Guide knowledge bases should plan for 4–8 weeks including planning, Flow design, data migration, and validation.
Can Zendesk ticket history be preserved in Dixa?
Yes. Dixa's conversation import API (POST /v1/conversations/import) accepts historical conversations with original createdAt timestamps and full message threading, preserving author attribution and chronological order.
What data can't be migrated from Zendesk to Dixa?
Triggers, automations, views, custom objects, Explore dashboards, Guide community forums, macro field-setting actions, Light Agent roles, and suspended tickets do not transfer. They must be rebuilt, redesigned, or discarded.
Can Zendesk triggers and automations be migrated to Dixa Flows?
Not directly. Zendesk triggers and automations must be consolidated into Dixa Flows manually using the visual Flow editor. A Zendesk instance with 40 triggers and 10 automations typically maps to 4–8 Dixa Flows.
Does Dixa replace Zendesk Talk for phone support?
Yes. Dixa includes native built-in telephony with IVR, call routing, voicemail, callback, and call recording across 60+ countries. Phone numbers must be ported from Zendesk Talk's carrier, which takes 1–3 weeks.