Kustomer-to-Deskpro migration requires API-based extraction (CSV exports lose message bodies), KObject flattening into custom fields, and 2–5 weeks of ETL work.
There is no native migration path between Kustomer and Deskpro; the process requires translating Kustomer's customer-centric timeline architecture — where Conversations, Messages, Notes, and KObjects all hang off a unified Customer record — into Deskpro's ticket-centric model of People, Organizations, Tickets, and Messages. KObjects (custom data objects) have no direct equivalent in Deskpro and must be flattened into custom fields on Tickets, People, or Organizations. CSV exports from Kustomer only include message previews rather than full message bodies, making API-based extraction essential for preserving conversation history. Additionally, Kustomer's Business Rules, Workflows, and Saved Searches cannot be exported and must be manually rebuilt as Deskpro Triggers, Automations, and Escalations.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
TL;DR — Kustomer to Deskpro Migration
A Kustomer to Deskpro migration is a data-model translation project. Kustomer's customer-centric timeline architecture — where Conversations, Messages, Notes, and KObjects all hang off a single Customer record — must be decomposed into Deskpro's ticket-centric model of People, Organizations, Tickets, and Messages. The realistic timeline is 2–3 weeks for under 50K conversations (roughly 1 week extraction, 3–4 days transformation, 1 week loading and validation), and 3–5 weeks for larger datasets with complex KObject schemas. The single biggest risk is losing full conversation transcript history: Kustomer's CSV exports only include message previews, not full message bodies, so you must extract via the REST API. KObjects have no direct equivalent in Deskpro — they must be flattened into custom fields on Tickets, People, or Organizations. Kustomer's Business Rules, Workflows, and Saved Searches cannot be exported and must be rebuilt as Deskpro Triggers and Automations manually.
CSV is not a migration method for conversation history
Both platforms' CSV tooling drops message bodies. Use it only for bootstrapping a contact list. (help.kustomer.com)
AWS Glue's Kustomer connector has been reported to have a hard limit of 10,000 records
AWS Glue's Kustomer connector has been reported to have a hard limit of 10,000 records per filtered request — data beyond that limit may be silently dropped unless you implement date-range partitioning. Verify this against current AWS documentation before relying on it for full extraction.
No read-only access after cancellation
Kustomer does not provide a read-only access period after your subscription ends. Export all data via the API before your subscription terminates — you won't be able to retrieve it afterward. (help.kustomer.com)
Before mapping KObjects, audit which ones agents actually use day-to-day
In our experience, teams often have 5+ Klasses defined but only 1–2 that are actively referenced during support interactions.
On-premise Deskpro
If loading into a self-hosted Deskpro instance, you may need VPN access or IP whitelisting for your migration servers. On-premise instances may also run different Deskpro versions with API behavior differences — verify API compatibility against your installed version before starting. Plan this with your infrastructure team.
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 Deskpro can hold your support model
Walk your current workflow through Deskpro: 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 → Deskpro 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.
Kustomer → Deskpro specifics
- Cost structure
- Kustomer's per-seat pricing at the Enterprise/Ultimate tier can exceed Deskpro's per-agent cost significantly. Deskpro publishes public cloud pricing starting at $39, $59, and $99 per agent/month for Team, Professional, and Enterprise tiers. (deskpro.com)
- Simplified data model
- Not every team needs KObjects, Klasses, and a full CRM data layer. Teams that primarily need ticket management and a knowledge base find Deskpro's simpler schema easier to maintain.
- Ownership uncertainty
- Kustomer was acquired by Meta (acquisition closed February 2022) and subsequently divested. Teams evaluating long-term platform stability sometimes prefer Deskpro's independent trajectory.
- Attachment re-hosting
- downloading from Kustomer, uploading to Deskpro's /api/v2/blobs/tmp endpoint, then linking blob_auth codes to tickets
- Relationship reconstruction
- Deskpro requires People and Organizations to exist before Tickets can reference them, so load order matters
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 -
Archive Search
Use Archive Search for data older than 2 years, as standard searches only return records updated within that window. (help.kustomer.com)
Kustomer → Deskpro specifics
- Rate limit management
- across two APIs with different throttling models (Kustomer's org-wide 1,000 RPM ceiling vs. Deskpro's per-key configurable limits)
- Base URL
- https://{orgname}.api.kustomerapp.com/v1/
- Pagination
- Cursor-based using page [size] (max 100 per page) and page [after]
- Rate limits
- 1,000 RPM org-wide for standard API keys (all keys share this ceiling). Machine users: 100 RPM independent limit.
- Pagination ceiling
- 100 pages max per search query. For datasets exceeding 10,000 records per query, paginate using updated_at sort and windowed date-range queries.
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 → Deskpro 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 → Deskpro 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 Deskpro 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 Deskpro sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Deskpro 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 Deskpro'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 Deskpro, 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 Deskpro'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.
Kustomer → Deskpro specifics
- Deployment flexibility
- Deskpro offers on-premise and private cloud options. Teams in regulated industries (finance, healthcare, government) with data-sovereignty requirements need infrastructure control that Kustomer's SaaS-only model can't provide.
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 Deskpro 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 Deskpro, 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.
Object Object
KObjects have no equivalent in Deskpro and must be flattened into custom fields, which can lose relational structure and complex nested data from the original Klass schemas.
| Kustomer field | Deskpro field | Notes |
|---|---|---|
| Customer | Person (User) | Primary email is the match key. Deskpro deduplicates on email. |
| Company | Organization | Map company name + domain. |
| Conversation | Ticket | One Conversation = one Ticket. Channel metadata maps to Ticket channel. |
| Message | Ticket Message/Reply | Preserve direction (inbound/outbound), timestamp, and channel. |
| Note (internal) | Ticket Note (internal) | Agent-only visibility preserved. |
| KObject | Custom Fields (on Ticket, Person, or Org) | No 1:1 equivalent. Flatten key attributes into custom fields. |
| Tag | Label | Direct mapping. |
| User (Agent) | Agent | Map roles and team assignments. |
| Team | Agent Team | Recreate team structure. |
| Queue | Department / Custom Queue | Routing logic must be rebuilt. |
| KB Article | KB Article | Map categories, re-host inline images. |
Kustomer Deskpro
| Kustomer field | Deskpro field | Notes |
|---|---|---|
| customer.name | person.name | Direct |
| customer.emails [0].email | person.primary_email | Take first verified email |
| customer.phones [0].phone | person.phone | Format with country code |
| customer.custom.* | person.custom_fields.* | Create matching custom fields in Deskpro first |
| company.name | organization.name | Direct |
| conversation.name | ticket.subject | Fallback to "No Subject" if null |
| conversation.status | ticket.status | Map: open→awaiting_agent, snoozed→awaiting_agent, done→resolved |
| conversation.priority | ticket.priority | Map Kustomer priority values to Deskpro's 1–10 scale |
| conversation.assignedUsers | ticket.agent | Map by agent email |
| conversation.tags | ticket.labels | Direct |
| conversation.createdAt | ticket.date_created | Preserve original timestamp |
| message.preview | — | Do not use. Extract full body via API. |
| message.body (via API) | ticket_message.message | Set format: html. Rewrite inline image URLs. |
| message.direction | Message type | in → user reply, out → agent reply |
| message.attachments | ticket_message.attachments | Re-host via blob upload |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Contacts / People | low | Kustomer Customers map relatively cleanly to Deskpro People, though duplicate email addresses and encoding issues may require deduplication passes. |
| Companies / Organizations | low | Kustomer Companies map directly to Deskpro Organizations with minimal structural transformation required. |
| Conversations / Tickets | medium | Kustomer's customer-centric conversation model must be decomposed into Deskpro's ticket-centric structure, and multi-channel threads may lose their unified timeline context. |
| Messages | high | Full message bodies are not available via CSV export and must be extracted through the REST API; without API extraction, only message previews are preserved, resulting in permanent transcript data loss. |
| KObjects / Custom Data Objects | high | KObjects have no equivalent in Deskpro and must be flattened into custom fields, which can lose relational structure and complex nested data from the original Klass schemas. |
| Attachments | high | Attachments must be downloaded from Kustomer, re-hosted to Deskpro's blob storage via a multi-step upload process, and linked to tickets, adding significant time and storage complexity. |
| Tags / Labels | low | Kustomer Tags map to Deskpro Labels with straightforward string-based mapping and minimal transformation. |
| Agents / Users | low | Kustomer Users map to Deskpro Agents directly, though role and permission configurations must be manually reconfigured. |
| Automations / Workflows | high | Kustomer Business Rules and Workflows cannot be exported and must be manually audited and rebuilt as Deskpro Triggers, Automations, and Escalations from scratch. |
| Knowledge Base Articles | medium | KB articles can be migrated but require re-mapping into Deskpro's category structure, and embedded media or internal links may break during transfer. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
KObject Schema Flattening
Kustomer's custom data objects (KObjects/Klasses) have no equivalent in Deskpro and must be decomposed and mapped into custom fields on Tickets, People, or Organizations, requiring careful schema translation.
Full Message Body Extraction
Kustomer's CSV exports only include message previews, not full message bodies, so preserving complete conversation transcript history requires extraction via the REST API.
Automation and Workflow Rebuild
Kustomer's Business Rules, Workflows, and Saved Searches cannot be exported and must be manually recreated as Deskpro Triggers, Automations, and Escalations.
Entity Load Order Dependencies
Deskpro requires People and Organizations to exist before Tickets can reference them, so the migration must enforce strict entity creation sequencing to preserve relationships.
Attachment Re-Hosting Complexity
Migrating attachments requires downloading blobs from Kustomer, uploading them to Deskpro's temporary blob storage endpoint, and linking blob authorization codes to the corresponding tickets.
Dual API Rate Limit Management
Kustomer enforces an org-wide 1,000 RPM rate limit ceiling while Deskpro uses per-key configurable limits, requiring coordinated throttling logic across both platforms during extraction and loading.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can I migrate full conversation history from Kustomer to Deskpro?
Yes, but only via the Kustomer REST API. CSV exports only include message previews, not full message bodies. You must use the GET /v1/conversations/{id}/messages endpoint to extract complete message content, then load it into Deskpro via the Tickets and Messages API.
How do I handle Kustomer KObjects in Deskpro?
Deskpro has no custom object system equivalent to KObjects. Your options are: flatten key KObject attributes into custom fields on Tickets, People, or Organizations; serialize the full KObject as JSON into a text field or pinned note; store KObject data in an external database; or archive and exclude it from migration.
What are the API rate limits for a Kustomer to Deskpro migration?
Kustomer enforces 1,000 requests per minute org-wide (shared across all API keys) with a 100-page maximum per search query. Machine users have an independent 100 RPM limit. Deskpro's rate limits are configurable per API key with hourly and daily thresholds. Plan extraction and loading as separate, independently throttled processes.
How long does a Kustomer to Deskpro migration take?
Expect 2–3 weeks for under 50K conversations with no KObjects, and 3–5 weeks for larger datasets with complex KObject schemas and attachments. A managed migration service can typically compress this to 5–10 business days.
Does Deskpro have a native Kustomer import tool?
No. Deskpro offers a CSV importer for Users, Organizations, and Tickets, but ticket CSV imports only support ticket properties and the first message — not full conversation threads or attachments. For a complete migration, you need the API or a third-party migration tool.