Migrating Kustomer to Zendesk requires translating a timeline-based model to tickets, mapping KObjects to Custom Objects, and navigating strict API rate limits on both platforms.
There is no native migration path between Kustomer and Zendesk — no import wizard, no built-in connector. The fundamental challenge is translating Kustomer's timeline-centric data model, where all customer interactions exist as a continuous chronological stream, into Zendesk's ticket-centric model of discrete conversations with independent lifecycles. Kustomer's CSV export is a reporting feature capped at 30-day windows and cannot export ticket history, while Zendesk's native importer only handles users, organizations, and custom object records. A production-grade migration requires custom ETL work using Kustomer's REST API for extraction and Zendesk's Ticket Import API for loading, with significant transformation logic to map conversations to tickets, handle multi-assignee conflicts, flatten customer identity arrays, and translate Custom Klasses (KObjects) into Zendesk Custom Objects within their structural limits.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
Kustomer conversations can have multiple assignees
Zendesk tickets support only one assignee at a time. You need a strategy: pick the last assignee, or log additional assignees as tags or custom fields.
Zendesk's legacy Custom Objects API is being fully removed in June 2026
Any migration must target the new Custom Objects API (/api/v2/custom_objects). The two APIs are not compatible.
When using Fivetran or similar ETL connectors for Kustomer, the default API usage cap is
When using Fivetran or similar ETL connectors for Kustomer, the default API usage cap is set to 90% of your plan's rate limit. For a migration running alongside production traffic, cap at 80% to leave headroom. (fivetran.com)
Do not build extraction around the standard Search endpoint alone
Standard Search only returns records updated within the last 2 years and caps each query at 100 pages. For older data, use Archive Search (/v1/customers/archive/search). Missing this silently drops historical data.
Tag every imported ticket with a consistent marker (e.g., kustomer-import) and exclude
Tag every imported ticket with a consistent marker (e.g., kustomer-import) and exclude them from SLA reports. Zendesk explicitly warns that metrics and SLAs are not accurate for imported tickets. (developer.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 Kustomer
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 Zendesk can hold your support model
Walk your current workflow through Zendesk: 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 Kustomer → Zendesk 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 organizations, users, tickets, and comments in Zendesk must match Kustomer source counts.
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Define Custom Objects
Use Zendesk Custom Objects to map over Kustomer Klasses, allowing you to store complex data that doesn't fit into standard fields.
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Establish User Identities
Prepare your user profiles to include multiple identifiers like email, phone numbers, and social handles.
Kustomer → Zendesk specifics
- Ecosystem breadth
- Zendesk's marketplace has 1,500+ integrations. Kustomer's is significantly smaller. Teams that need tight connections to Jira, Salesforce, or Shopify often find Zendesk's native integrations more mature.
- Ticketing scale and reporting
- Zendesk's ticketing model is purpose-built for high-volume, multi-channel support with mature SLA enforcement, CSAT surveys, and Explore analytics.
- AI capabilities
- Zendesk's investment in AI (Answer Bot, Intelligent Triage, AI Agents) is tightly coupled to its ticketing model and Help Center content.
- Cost predictability
- Kustomer's per-conversation pricing can spike unpredictably during peak volume. Zendesk's per-agent model is more predictable for budget planning.
- Historical migration + ongoing sync
- Managed service for historical data, then Zapier/Make for incremental sync
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 Kustomer 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 Kustomer 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 Kustomer 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
Kustomer → Zendesk specifics
- When to use
- Small datasets (<5,000 records), simple object structures, no custom Klasses.
- Ticket Import
- Uses the account-wide rate limit (no separate cap)
- Customers
- → GET /v1/customers (includes relationships.org)
- Conversations
- → GET /v1/customers/{id}/conversations (per customer)
- Messages
- → GET /v1/conversations/{id}/messages (per conversation)
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 Kustomer → Zendesk 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 Kustomer → Zendesk 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 Zendesk 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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Rebuild Custom Fields
Recreate all Kustomer Custom Attributes as Ticket, User, or Organization fields in Zendesk to ensure data points have a place to land.
Kustomer → Zendesk specifics
- Status mapping
- Kustomer's open, snoozed, done → Zendesk's new, open, pending, solved, closed
- Multi-email flattening
- Pick the primary email, create additional identities via Zendesk's User Identities API
- Assignee consolidation
- Multiple Kustomer assignees → single Zendesk assignee + tags for others
- Message → Comment conversion
- Set author_id based on message direction, map created_at timestamps, set public based on note vs. message
- Inline image URL rewriting
- Download from Kustomer CDN, re-upload to Zendesk via Uploads API, replace URLs in HTML body
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 Zendesk sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Zendesk 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 Zendesk'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 Zendesk, 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 Kustomer 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 Zendesk'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 Kustomer 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 Kustomer 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 Kustomer and Zendesk 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 Zendesk, 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 Kustomer 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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Rebuild triggers and automations
in Zendesk to replicate Kustomer business rules
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Configure macros
based on Kustomer shortcuts and snippets
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Set up routing
Kustomer queues → Zendesk routing rules and views
Kustomer → Zendesk specifics
- Field-level spot checks
- Sample 50–100 records across old and new date ranges, open and closed states. Verify names, emails, custom field values, timestamps, and public/private state.
- Conversation thread integrity
- Open 20+ migrated tickets and verify the comment sequence matches the original Kustomer timeline — correct order, correct authors, correct timestamps.
- Attachment verification
- Confirm embedded images render and file attachments are downloadable.
- Custom object records
- Verify Klass data appears correctly in Zendesk Custom Object records with proper lookup relationships.
- Train agents
- on Zendesk's ticket model vs. Kustomer's timeline model
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.
Kustomer Zendesk
| Kustomer field | Zendesk field | Notes |
|---|---|---|
| customer.emails [].email | user.email (primary) + User Identities | Flatten array → primary + identities |
| customer.phones [].phone | user.phone | First phone → primary |
| customer.displayName | user.name | Direct |
| customer.externalId | user.external_id | Direct |
| conversation.status (open/snoozed/done) | ticket.status (new/open/pending/solved/closed) | Status mapping logic required |
| conversation.priority | ticket.priority (low/normal/high/urgent) | Value normalization |
| conversation.assignedUsers | ticket.assignee_id | Kustomer allows multiple assignees; Zendesk allows one |
| conversation.tags [] | ticket.tags [] | Direct |
| conversation.channel | ticket.via | Map Kustomer channels to Zendesk via types |
| message.body / message.htmlBody | comment.body / comment.html_body | Direct, but inline image URLs need re-hosting |
| message.direction (in/out) | comment.public + comment.author_id | Inbound = requester, Outbound = agent |
| company.name | organization.name | Direct |
| company.externalId | organization.external_id | Direct |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Tickets (Conversations) | high | Translating Kustomer's continuous timeline conversations into Zendesk's discrete ticket model requires complex transformation logic, and the Ticket Import API is the only endpoint that supports historical timestamps with multiple comments per request. |
| Ticket Comments (Messages) | medium | Messages map to comments with direction-based author assignment, but inline image URLs embedded in HTML bodies must be re-hosted to remain accessible in Zendesk. |
| Custom Objects (KObjects/Klasses) | high | Kustomer Klasses with complex schemas may exceed Zendesk's 100-field limit, 32 KB record size cap, or 50 million total record ceiling, requiring schema redesign before migration. |
| Contacts (Customers) | medium | Kustomer's native multi-email and multi-phone arrays require flattening to a single primary identity plus additional identities via the User Identities API, with risk of duplicate detection failures. |
| Organizations (Companies) | low | Companies map nearly 1:1 to Zendesk Organizations, though custom attributes need field-level translation and source IDs should be preserved in external_id for traceability. |
| Agents (Users) | low | Agents must be provisioned in Zendesk before ticket migration to preserve assignment history, but the mapping itself is straightforward. |
| Groups (Teams) | medium | Teams map directly to Groups, but all associated assignment rules, queue routing logic, and round-robin configurations must be manually rebuilt in Zendesk. |
| Tags | low | Tags transfer directly as flat strings, but any hierarchical tag structures in Kustomer will be flattened since Zendesk does not support tag hierarchy. |
| Attachments | medium | File attachments must be downloaded from Kustomer and re-uploaded to Zendesk, and inline images referenced by URL in message HTML bodies need to be re-hosted to avoid broken links. |
| Custom Fields | medium | Status mappings (3-state to 5-state), priority normalization, picklist value differences, and multi-assignee fields all require custom transformation logic with no assumed 1:1 equivalence. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Timeline-to-Ticket Model Translation
Kustomer stores all interactions as a single continuous timeline per customer, while Zendesk requires each conversation to be mapped to a discrete ticket with its own comment thread, status, and lifecycle.
Multi-Assignee to Single-Assignee
Kustomer conversations support multiple simultaneous assignees, but Zendesk tickets allow only one assignee, requiring a strategy to select the primary assignee and preserve others as metadata.
Custom Klass Schema Limits
Kustomer Klasses (KObjects) with more than 100 custom attributes must be split or flattened to fit Zendesk Custom Objects' 100-field maximum, 32 KB record size cap, and lookup relationship field limits.
Customer Identity Array Flattening
Kustomer stores multiple emails and phone numbers as native arrays on customer records, while Zendesk requires designating one primary email and creating additional identities via the User Identities API.
No Native Export Path
Kustomer's CSV export is limited to 30-day reporting windows and 50,000-row saved search caps covering only two years, making API-based extraction the only viable method for full historical data.
Status and Priority Normalization
Kustomer's three conversation statuses (open, snoozed, done) and priority values must be mapped to Zendesk's five-state ticket lifecycle (new, open, pending, solved, closed) and four priority levels through custom transformation logic.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can Zendesk natively import Kustomer conversations?
No. Kustomer's native CSV export is reporting-oriented (capped at 30-day windows), and Zendesk's data importer handles users, organizations, and custom object records — not full ticket history. Full-fidelity migration requires Kustomer API extraction plus Zendesk's Ticket Import API.
Can I preserve original timestamps when migrating from Kustomer to Zendesk?
Yes, but only via Zendesk's Ticket Import API (/api/v2/imports/tickets). This is the only endpoint that lets you set created_at on both tickets and individual comments. The standard Tickets API does not support backdating.
What are the API rate limits for a Kustomer to Zendesk migration?
Kustomer enforces plan-based rate limits: 300 req/min (Professional) to 2,000 req/min (Ultimate), shared org-wide. Zendesk ranges from 200 req/min (Team) to 2,500 req/min (Enterprise Plus or High Volume add-on). The Kustomer Search API also has a hard cap of 100 pages and 10,000 records per query.
How do I migrate Kustomer custom Klasses (KObjects) to Zendesk?
Map Kustomer Klasses to Zendesk Custom Objects via the new Custom Objects API (/api/v2/custom_objects). Key constraints: Zendesk limits each custom object to 100 fields, 32 KB per record, and 50 million records total. If your Klass has more than 100 attributes, split or flatten the schema. The legacy Custom Objects API is being removed in June 2026.
What data is lost in a Kustomer to Zendesk migration?
Kustomer's unified customer timeline view has no Zendesk equivalent. Business rules, workflows, and automations cannot be exported and must be rebuilt manually. SLA metrics do not carry over — Zendesk warns that metrics are inaccurate for imported tickets. Multi-assignee history is also lost since Zendesk supports only one assignee per ticket.