Kustomer's customer-centric timeline must be translated into JSM's issue-centric structure. The biggest risk is leaking internal notes as public comments. API-only migration; plan 2–4 weeks.
Migrating from Kustomer to Jira Service Management (JSM) is a data-model translation problem with no native migration path. Kustomer organizes support data around a CRM-style customer timeline, while JSM is issue-centric, treating every support interaction as a Jira issue with fields, comments, and a workflow state machine. Key structural mismatches include Kustomer's KObjects (custom object schemas) having no direct JSM equivalent, requiring design decisions around custom fields, linked issues, or JSM Assets. Workflows, business rules, SLA policies, and routing configurations cannot be exported from Kustomer and must be manually rebuilt in JSM Automation.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
Quick Answer
Migrating from Kustomer to Jira Service Management (JSM) is a data-model translation — Kustomer organizes everything around a customer timeline, while JSM organizes everything around issues. There is no native migration path. The single biggest risk is exposing internal notes as public comments if your migration script uses the standard Jira comment API (/rest/api/3/issue/{key}/comment) instead of the Service Desk API (/rest/servicedeskapi/request/{key}/comment) with "public": false. Kustomer's KObjects have no 1:1 JSM equivalent and must be handled as custom fields, linked issues, or JSM Assets. Workflows, business rules, and SLA policies cannot be exported from Kustomer and must be rebuilt manually. Realistic timeline: 2–4 weeks for most environments. Teams with fewer than 5,000 conversations and no KObjects can attempt a DIY API script. Everyone else should use a managed migration service.
KObjects are the hardest mapping problem
Kustomer lets you define custom object schemas (Klasses) — orders, subscriptions, shipments — tied to customer timelines. JSM has no native equivalent. Your options: flatten KObject data into custom fields on the issue, create linked Jira issues in a separate project, use JSM Assets (formerly Insight, requires Premium plan), or store references as external links. Each approach has trade-offs in searchability, reporting, and agent experience.
Extract to an intermediate format
Don't try to write directly to JSM during extraction. Dump everything to local JSON files first, then transform and load in a separate phase. This decouples the two APIs, makes debugging easier, and lets you re-run the load phase without re-extracting.
You cannot backdate issue creation in JSM Cloud
The created field is system-generated and immutable. If preserving original timestamps matters for reporting or compliance, store the Kustomer createdAt in a dedicated DateTime custom field (e.g., "Original Created Date") and train your team to filter on it. Comment creation dates are also set to the time the API call is made, not the original Kustomer timestamp. This limitation affects SLA reporting on migrated tickets — any time-based SLA metric will be calculated from migration date, not original creation date.
Simplify transitions during migration
Temporarily modify the JSM workflow to allow "Any Status to Any Status" transitions. This lets your script set the final state directly without triggering required fields, validation rules, or approval gates. Revert the workflow once the migration finishes.
Comment ordering matters
JSM displays comments in chronological order based on creation timestamp. If you load comments out of order (e.g., parallelizing API calls per conversation), the conversation thread will be scrambled. Load comments sequentially within each conversation, sorted by Kustomer's sentAt timestamp.
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 Jira Service Management can hold your support model
Walk your current workflow through Jira Service Management: 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 → Jira Service Management 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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Define an Object Schema
in Jira Assets (Assets → Object Schemas → Create Schema).
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Create Object Types
that mirror your Kustomer KObject classes (e.g., Order, Subscription). For each Object Type, define attributes matching the KObject Klass fields — use Assets attribute types: Text, Integer, Float, Boolean, Date, DateTime, URL, Select, Object (for references).
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Prepare the Assets CSV import file
The required CSV schema for Assets import is:
Kustomer → Jira Service Management specifics
- Atlassian ecosystem consolidation
- Engineering teams already on Jira Software and Confluence want a single pane of glass for IT and customer support. JSM's native links to Jira issues and Confluence knowledge bases reduce tool sprawl.
- ITSM maturity
- Kustomer is built for omnichannel B2C support — messaging, sentiment analysis, CRM timelines. Teams that need structured ITSM workflows (change management, incident management, problem management) find JSM's ITIL alignment a better fit.
- Cost restructuring
- Kustomer's pricing is per-agent with usage-based tiers. JSM's free tier supports up to 3 agents, and its Standard plan can be more cost-effective for teams that don't need Kustomer's AI features.
- Platform stability concerns
- Meta acquired Kustomer for a reported $1 billion in 2020, then divested it in 2023. Kustomer subsequently raised a $30 million Series B as an independent company oriented toward an AI-centric roadmap. Some teams prefer Atlassian's long-term ecosystem predictability.
- Alternatively, use the Assets REST API
- (POST /rest/assets/1.0/object/create) with a JSON body specifying objectTypeId and attributes as an array of {objectTypeAttributeId, objectAttributeValues} pairs. Retrieve objectTypeAttributeId values via GET /rest/assets/1.0/objecttype/{id}/attributes.
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 -
Export KObjects
from Kustomer via GET /v1/customers/{id}/klasses/{klassName} for each customer.
Kustomer → Jira Service Management specifics
- List all customers
- via GET /v1/customers with cursor-based pagination.
- For each customer
- , fetch conversations via GET /v1/customers/{id}/conversations.
- For each conversation
- , fetch messages via GET /v1/conversations/{id}/messages and notes via GET /v1/conversations/{id}/notes.
- For each message with attachments
- , download attachment binaries from Kustomer's AWS S3 buckets.
- Fetch KObjects
- per customer via GET /v1/customers/{id}/klasses/{klassName}.
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 → Jira Service Management 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 → Jira Service Management 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 Jira Service Management 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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Create an Assets custom field
on your JSM Request Types and link the Jira issue to the specific Asset object using the field's object picker.
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 Jira Service Management sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Jira Service Management 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 Jira Service Management'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 Jira Service Management, 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 Jira Service Management'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 Jira Service Management 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 Jira Service Management, 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.
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.
Kus mer vs
| Kustomer field | Jira Service Management field | Notes |
|---|---|---|
| Customer | Customer (portal user) | JSM customers are portal users, not CRM records. No timeline concept. |
| Conversation | Issue (Service Request) | One conversation = one issue. Channel metadata is lost. |
| Message (outbound/inbound) | Public comment | Map to comments via the Service Desk API. |
| Note | Internal comment | Must use "public": false on the Service Desk comment API. |
| KObject (custom object) | Custom field, linked issue, or JSM Asset | No native equivalent. Requires design decisions. |
| Tag | Label | JSM labels are flat strings — no nesting. |
| Team | Group / Queue | JSM uses agent groups for assignment. |
| Snippet (canned response) | Canned response (JSM template) | Manual recreation required. |
| Workflow / Business Rule | Automation rule | Cannot be exported; must be rebuilt. |
| Knowledge Base Article | Confluence page | Requires a separate migration to Confluence. |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Conversations | medium | Conversations map 1:1 to JSM issues but require post-creation workflow transitions to replicate status, and channel metadata (e.g., chat, email source) is lost in translation. |
| Internal Notes | high | Using the wrong JSM comment API endpoint will make internal agent notes publicly visible to customers, representing a serious data exposure risk that requires careful API selection. |
| KObjects (Custom Objects) | high | Kustomer KObjects have no native JSM equivalent and require upfront architectural decisions; the wrong mapping strategy can result in non-searchable data, broken reporting, or loss of relational context. |
| Attachments | medium | Attachments must be downloaded from Kustomer's S3 buckets and re-uploaded to JSM, where Standard-plan tenants face a 250 GB total storage limit and a 1 GB per-file cap. |
| Custom Fields | medium | Kustomer supports custom attributes on every object type, requiring each field to be individually mapped to a JSM custom field schema or explicitly excluded before migration begins. |
| Tags / Labels | low | Kustomer tags map to JSM labels, but JSM labels are flat strings with no nesting and no spaces allowed, requiring sanitization of tag values during transformation. |
| Customer Records | high | Kustomer's CRM customer records with full timeline history cannot be replicated in JSM, which only supports portal users without relational history, resulting in structural data loss. |
| Agent and User Accounts | medium | Every migrated agent requires a paid JSM agent license, and user attribution for historical comments depends on matching Kustomer agent identities to existing or newly provisioned Atlassian accounts. |
| Workflows and Business Rules | high | Kustomer automations, routing rules, and SLA configurations cannot be exported in any machine-readable format and must be manually documented and rebuilt entirely in JSM before the platform switch. |
| Knowledge Base Articles | medium | Kustomer knowledge base articles require a separate migration to Confluence and are not part of the core JSM issue migration, adding scope and complexity to the overall project. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Internal Note Visibility Risk
Kustomer notes must be migrated using the JSM Service Desk comment API with 'public': false; using the standard Jira comment API will expose internal agent notes to customers.
KObject Schema Translation
Kustomer's custom object schemas (KObjects/Klasses) have no native JSM equivalent and must be re-architected as custom fields, linked issues, or JSM Assets (Premium-only), each with distinct trade-offs in searchability and reporting.
Archive Data Retrieval
The standard Kustomer Search API only surfaces records updated within the past two years, requiring use of the Archive Search endpoint to prevent silent data loss for older conversations.
API Rate Limit Constraints
Kustomer's rate limits (300–2,000 rpm by plan tier) apply across all platform integrations simultaneously, making large migrations of 50,000+ conversations require 12–16 hours of sustained extraction with retry logic.
Workflow and Automation Rebuild
Business rules, routing queues, SLA policies, and conversational assistant configurations cannot be exported from Kustomer in any format and must be fully reconstructed as JSM Automation rules before go-live.
Customer Data Model Mismatch
Kustomer's CRM-style customer records with full interaction timelines have no equivalent in JSM, where customers are simply portal users without relational history or timeline attributes.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can I migrate from Kustomer to Jira Service Management using CSV?
Not for a full migration. Kustomer's Export Buddy and reporting exports only cover users, teams, snippets, tags, and flattened conversation data. They don't preserve the customer→conversation→message hierarchy, attachments, or internal notes. You need the Kustomer REST API for a complete extraction. JSM also lacks a bulk CSV import for issues with threaded comments.
How do I preserve internal notes when migrating from Kustomer to JSM?
Use the JSM Service Desk API endpoint POST /rest/servicedeskapi/request/{issueKey}/comment with "public": false for Kustomer notes. Do not use the standard Jira comment API — its visibility parameter restricts by role/group but does not hide comments from JSM portal customers.
What happens to Kustomer KObjects in Jira Service Management?
JSM has no native equivalent to KObjects. Your main options are: flatten KObject attributes into custom fields on the JSM issue, create linked issues in a separate Jira project, use JSM Assets (requires Premium plan) as CMDB objects, or store KObject data externally and link via URL fields. Most teams flatten the most-used attributes into custom fields.
How long does a Kustomer to JSM migration take?
Typically 2–4 weeks for most environments. Small teams (under 5K conversations, no KObjects) can finish in 10–12 days. Complex migrations with 100K+ conversations, KObjects, and extensive custom fields should budget 4–6 weeks including validation and manual workflow rebuilds.
Can I preserve original timestamps when migrating to JSM?
No. JSM Cloud's created field is system-generated and immutable via the API. Store Kustomer's original createdAt timestamp in a custom DateTime field. Comment creation dates are also set to the time the API call is made, not the original Kustomer timestamp.