Enchant to Deskpro migration requires API-to-API scripting — Deskpro's CSV importer only handles first messages. Map objects in strict dependency order and validate in a sandbox before cutover.
Migrating from Enchant to Deskpro requires bridging a significant structural gap between Enchant's minimal, inbox-centric data model and Deskpro's strict relational architecture built around Departments, People, and Organizations. No native migration path exists that preserves full conversation history—Deskpro's CSV importer only captures ticket properties and the first message, omitting threads, attachments, and internal notes. Custom API scripting or a managed ETL pipeline is required for any production-grade migration that maintains conversation integrity, attachment fidelity, and relational dependencies between customers, organizations, and tickets.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
If your Enchant workspace shows account, order, or opportunity data in the sidebar, audit
If your Enchant workspace shows account, order, or opportunity data in the sidebar, audit the source system. Enchant's docs describe sidebar apps as data pulled from internal systems at view time — that data lives in your CRM, not in Enchant. Do not try to recreate it as fake objects in Deskpro. (help.enchant.com)
Enchant stores tickets in states including open, hold, closed, snoozed, and archived
If your compliance requirements don't mandate full history, skip archived and trashed tickets to reduce migration time and keep your Deskpro instance clean.
Superuser API keys in Deskpro are not tied to a single agent
They can impersonate any agent via the X-DeskPRO-Agent-ID header. This is required during migration to preserve the original agent assignment on tickets and messages.
Timezone handling
Enchant returns timestamps in UTC (ISO 8601 with Z suffix). Deskpro accepts ISO 8601 timestamps with timezone offset (e.g., 2024-01-15T10:30:00+0000). Always normalize timestamps to UTC before loading. If your Deskpro instance is configured for a different timezone, Deskpro will display the converted local time — but the stored value should be UTC to avoid shifted ticket chronology.
For open tickets, consider checking the last message direction
If the last public message was from the agent, awaiting_user may be more accurate than awaiting_agent. Decide this based on your operational model.
Never pipe data directly from API to API
Network failures will cause data loss or duplication. A staging database lets you resume exactly where you left off.
Embedding costs extra credits
Each embedded resource type (messages, customer, labels) adds one credit to the request. A ticket request with embed=messages,customer,labels costs 4 credits total. At 100 credits/minute, that is only 25 requests per minute — plan your extraction time accordingly.
Verify historical date support against your Deskpro build
Deskpro release notes show date_created support on ticket POSTs and ticket message POSTs, plus date_resolved on ticket POSTs. Older or customized deployments may differ. Test this in your sandbox before depending on it in production. (support.deskpro.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 Enchant
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 Enchant → 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.
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Record-count comparison
Verify customer count, organization count, ticket count, and total message count match between source and target.
Enchant → Deskpro specifics
- Complex routing
- with skill-based and department-layered ticket assignment
- Multi-brand support
- managing different customer bases from a single instance
- Contacts
- (email, phone, Twitter) → Deskpro Contact Data
- Organizations
- (derived from customer email domains or CRM data) → Deskpro Organizations
- Messages
- (replies and notes) → Deskpro Messages/Notes
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 Enchant 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 Enchant 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 Enchant 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
Enchant → Deskpro specifics
- When to use it
- Low volume, low fidelity requirements, and you can accept partial history.
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 Enchant → 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 Enchant → 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.
Enchant → Deskpro specifics
- Dry run on a test Deskpro instance
- Create a sandbox and migrate a representative subset (100–200 tickets across all inboxes, including tickets with attachments, notes, 10+ replies, and multiple channel types).
- Field-level spot checks
- Sample 20–50 tickets and verify subject, status, assignment, labels, timestamps, custom field values, and full message threads.
- Thread integrity
- Check tickets with 10+ replies. Ensure chronological order is preserved and that agent replies vs. customer replies are attributed correctly.
- UAT with agents
- Have 2–3 agents review migrated tickets for their own assigned history. They will catch mapping errors that scripts will not.
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 Enchant 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 Enchant 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 Enchant read-only rather than cancelled — cancelling the old contract on day one removes your only fallback.
Enchant → Deskpro specifics
- Rollback plan
- For Deskpro Cloud sandboxes, you can request a reset through Deskpro support. For on-premise instances, snapshot the database before migration and restore from snapshot to roll back. Document the exact rollback procedure and test it once before the production migration.
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 Enchant 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 Enchant 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 automations
Deskpro triggers and escalations must be configured from scratch. Enchant's automation rules do not export. See our automation migration guide.
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Set up email routing
Update your DNS and forwarding rules to point incoming support email at Deskpro instead of Enchant. Ensure you have disconnected them from Enchant to prevent loop conditions or missed tickets.
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Configure SLA policies
Recreate any SLA rules in Deskpro's admin panel.
Enchant → Deskpro specifics
- Attachment verification
- Confirm attachments are downloadable from Deskpro and match the originals in file size and content type.
- Inline image verification
- Open 5–10 tickets that contained inline images in Enchant. Confirm images render in the Deskpro agent UI, not as broken <img> tags.
- Train your team
- Deskpro's interface and workflow model differs significantly from Enchant's shared inbox. Plan training sessions before cutover, focusing on department navigation and Snippets (macros).
Don't move on until
- Full reconciliation report attached to the project record
- Reporting baselines match pre-migration figures within agreed tolerance
- Formal acceptance signed and archive retention scheduled
Field mapping reference
The field-by-field mapping for each object. Use this as the starting point for your mapping spec.
Object Equivalent
| Enchant field | Deskpro field | Notes |
|---|---|---|
| Inbox | Department | 1:1 mapping by name or ID |
| User (Agent) | Agent | Must pre-exist in Deskpro |
| Customer | Person (User) | Deskpro requires a valid email |
| Contact | Person Email/Phone | Nested under person record |
| Customer email domain | Organization | Derived; group People by company domain |
| Ticket | Ticket | Map states, types, and assignments |
| Message (reply) | Ticket Message/Reply | Preserve direction (in/out) |
| Message (note) | Ticket Note | Internal notes via is_note=true |
| Attachment | Blob Attachment | Upload to temp blob first, then link to message |
| Label | Label | Tags on tickets |
| Customer Summary | Custom User Field | No direct equivalent; use a custom text field |
State Status
| Enchant field | Deskpro field | Notes |
|---|---|---|
| open | awaiting_agent | Active, needs agent attention |
| hold | awaiting_user | Waiting for customer reply |
| closed | resolved | Resolved but visible |
| archived | archived | Long-term storage |
| snoozed | awaiting_user (with follow-up date) | Deskpro has no native snooze — use a follow-up date |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Tickets | medium | Ticket metadata migrates cleanly via API, but status mapping between Enchant's states (open, hold, closed, snoozed, archived) and Deskpro's granular statuses (awaiting_agent, awaiting_user, resolved, archived) requires careful translation logic. |
| Conversation Threads | high | Full message history including replies and internal notes cannot be imported via CSV and requires sequential API calls per ticket, making it the most engineering-intensive and error-prone part of the migration. |
| Attachments | high | Attachments must be individually downloaded from Enchant and re-uploaded as blob attachments to Deskpro via API, with no native import support and significant risk of orphaned or missing files. |
| Contacts (People) | low | Enchant customers map relatively cleanly to Deskpro People, though multi-channel contact data (email, phone, Twitter) must be correctly structured into Deskpro's contact data format. |
| Organizations | medium | Organizations do not exist natively in Enchant and must be derived from email domains or external CRM data, introducing risk of incorrect groupings or duplicate organization records. |
| Labels | low | Enchant labels can be mapped directly to Deskpro labels with minimal transformation, though teams should consider whether some labels are better represented as custom field values in Deskpro. |
| Custom Fields | low | Since Enchant does not support custom fields on tickets, there is no structured data at risk of loss—but teams must pre-create any desired Deskpro custom fields and populate them from Enchant metadata like labels or customer summaries. |
| Agents | low | Agent accounts must be manually recreated in Deskpro before migration, and accurate user ID-to-agent ID mapping is essential for preserving ticket assignment history. |
| Knowledge Base Articles | high | Enchant provides no bulk export API for knowledge base content, requiring manual recreation of all articles in Deskpro with high risk of content loss or formatting inconsistencies. |
| Automations and Workflows | medium | Triggers, canned responses, SLA policies, and routing rules cannot be migrated and must be manually rebuilt in Deskpro, with risk of operational gaps if not fully documented from Enchant beforehand. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Relational Dependency Enforcement
Deskpro enforces strict relational dependencies between People, Organizations, and Tickets, requiring a precise creation order that Enchant's flat data model does not naturally support.
No Full-History Native Import
Deskpro's built-in CSV importer only loads ticket properties and the first message, making it impossible to migrate complete conversation threads, attachments, or internal notes without using the API.
Inbox-to-Department Mapping
Enchant's inbox-centric organization must be manually mapped to Deskpro's department-centric structure, with departments pre-created and their IDs recorded before any ticket import can begin.
Missing Custom Field Parity
Enchant lacks custom fields on tickets entirely, relying on labels and free-text summaries, so teams must decide how to restructure this metadata into Deskpro's typed custom field system before migration.
Organization Derivation from Scratch
Enchant does not have a native organization object, so company-level groupings must be derived from customer email domains or external CRM data and created in Deskpro prior to People import.
Knowledge Base Export Gap
Enchant's knowledge base has no bulk export API endpoint, meaning KB articles must be manually recreated in Deskpro rather than programmatically migrated.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can I use Deskpro's CSV importer to migrate from Enchant?
Only partially. Deskpro's CSV importer supports ticket properties and the first message only — it cannot import conversation threads, attachments, or agent notes. For a full migration with history, you need the Deskpro REST API v2. ([support.deskpro.com](https://support.deskpro.com/en-US/kb/articles/can-i-import-data-from-other-systems-or-helpdesks-1))
Can I migrate attachments from Enchant to Deskpro?
Yes, but it requires a multi-step process: download the file from Enchant's API, upload it to Deskpro's /blobs/temp endpoint to get a blob auth token, then attach that token to the Deskpro ticket message payload.
How do I handle duplicate customer emails during migration?
Deskpro enforces unique email addresses for the Person object. Enchant allows loose customer creation with potential duplicates. Your transform layer must deduplicate on email address and merge records before pushing to Deskpro to avoid API rejections.
What is the Enchant API rate limit for data extraction?
Enchant's API is rate limited to 100 credits per minute per account, with a secondary burst limit of 6 requests per second. Embedding related resources (messages, customer, labels) costs additional credits per embed type, which reduces effective throughput.
Will I lose my ticket creation dates when migrating to Deskpro?
Not necessarily. Deskpro's API supports a date_created parameter on ticket POSTs, but you should verify this works on your specific Deskpro build before depending on it. Older or customized deployments may behave differently. ([support.deskpro.com](https://support.deskpro.com/en-US/news/posts/pdf/deskpro-2018-2-release))