Gladly to Zendesk migration is high-complexity due to the person-centric vs. ticket-centric data model clash. Budget 10–17 business days and handle conversation splitting and API truncation.
There is no native migration path from Gladly to Zendesk; the migration is fundamentally a data-model translation from Gladly's person-centric architecture (where all interactions live on a single, lifelong Customer timeline) to Zendesk's ticket-centric model (where each support interaction is a discrete Ticket with its own lifecycle). Custom API scripting is required to extract data via Gladly's REST and Export APIs, split continuous conversation timelines into individual Zendesk tickets, and load them using Zendesk's Ticket Import API to preserve original timestamps. Key custom work includes building conversation-splitting logic, normalizing custom field formats, handling attachment extraction (not included in standard exports), and manually rebuilding automation rules and Help Center content.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
TL;DR — Gladly to Zendesk Migration
Migrating from Gladly to Zendesk is a data-model translation problem, not a CSV job. Gladly's architecture centers on the Customer — a single, continuous conversation timeline per person. Zendesk centers on the Ticket — a discrete record with its own lifecycle. The biggest risk is silent truncation: Gladly's core history endpoints cap at 100 conversations per customer and 1,000 items per conversation with no standard pagination. You must use Zendesk's Ticket Import API (/api/v2/imports/tickets) to preserve historical timestamps. Realistic timeline: 10–17 business days depending on volume, attachment handling, and custom field complexity. Teams with fewer than 5,000 conversations and no attachment requirements can self-serve with API scripting. For anything larger — or if you need zero downtime and full attachment fidelity — a managed migration service is the safer path.
What has no clean Zendesk equivalent
Gladly's lifelong customer timeline view, native voice AI transcripts (in structured form), Sidekick AI resolution metadata, satisfaction survey responses (must be exported separately and stored outside Zendesk or appended as tags/notes), and routing/queue history cannot be directly replicated in Zendesk. Plan to archive this data separately or append it as internal notes on the customer's Zendesk user profile.
[For engineering] As of mid-2026, new migration code should use Zendesk OAuth instead of
[For engineering] As of mid-2026, new migration code should use Zendesk OAuth instead of long-lived API tokens. Zendesk starts auto-deactivating inactive API tokens on July 28, 2026, blocks new token creation on October 27, 2026, and permanently deactivates remaining tokens on April 30, 2027. The client credentials flow is the documented server-to-server option for background jobs and data pipelines. (developer.zendesk.com)
If you only validate record counts, you can still miss the important loss
a 1,001st conversation item or a missing recording. Validate by history depth and binary presence, not only by totals. (developer.gladly.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 Gladly
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 Gladly → 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.
Gladly → Zendesk specifics
- Integration ecosystem
- Zendesk's marketplace lists 1,500+ pre-built integrations. Gladly lists roughly 40 native integrations. Teams that need deep connections to CRMs (Salesforce, HubSpot), BI tools (Looker, Tableau), or ITSM workflows (Jira, ServiceNow) outgrow Gladly's ecosystem.
- Multi-brand and enterprise scale
- Zendesk supports multi-brand instances, complex SLA policies, custom ticket statuses, and a mature RBAC model with up to 5 custom roles on Enterprise and unlimited on Enterprise Plus. Gladly targets mid-market consumer brands and lacks equivalent enterprise governance.
- Reporting and analytics
- Zendesk Explore offers SQL-like custom reporting, scheduled dashboards, and cross-object analytics. Gladly's reporting is improving but remains less flexible for operations teams that need granular, ad-hoc data slicing.
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 Gladly 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 Gladly 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 Gladly 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
Gladly → Zendesk specifics
- Edge-case audit
- Check customers who hit the 100-conversation or 1,000-item Gladly API cap.
- Partial import cleanup
- If migration fails mid-load, use Zendesk's bulk delete API (DELETE /api/v2/tickets/destroy_many?ids=1,2,3) to remove imported tickets. The API accepts up to 100 ticket IDs per request. Tag all migrated tickets with migrated_from_gladly so you can identify and delete them in bulk via the Search API.
- User cleanup
- Imported end-users can be deleted if they have no non-migrated tickets. Agents cannot be deleted if they are assigned to tickets — reassign or delete their tickets first.
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 Gladly → 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 Gladly → 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.
Gladly → Zendesk specifics
- By Topic
- Each distinct Topic in a Conversation becomes a separate Zendesk ticket. Best when Gladly Topics are well-governed and consistently applied.
- By Time Gap
- If there's a gap exceeding a defined threshold between items, start a new ticket. Choose a threshold that matches your support patterns — common values range from 72 hours to 14 days. A 7-day gap works well for most consumer brands.
- By Session
- Use Gladly's sessionId on conversation items to group messages into logical tickets. This is the most structurally faithful approach since sessions represent natural interaction boundaries in Gladly.
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.
Gladly → Zendesk specifics
- Sandbox first
- Run the full migration in a Zendesk Sandbox environment before production. Sandbox limitations: sandbox environments share the same rate limits as your production plan, sandbox data resets on refresh (which deletes all imported data), and some Enterprise features may not be available in sandbox. Plan sandbox testing before any scheduled refresh.
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 Gladly 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 Gladly 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 Gladly read-only rather than cancelled — cancelling the old contract on day one removes your only fallback.
Gladly → Zendesk specifics
- Full rollback
- If the import must be completely reversed, delete all tickets tagged migrated_from_gladly, then remove imported organizations and users. This is operationally expensive on large imports — another reason to validate thoroughly in sandbox first.
- Gladly continuity
- Keep Gladly active and receiving traffic until Zendesk is fully validated. Do not cancel your Gladly contract until go-live is confirmed and agents have operated in Zendesk for at least one full business week.
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 Gladly 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 Gladly 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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Record-count reconciliation
Total Gladly conversations vs. total Zendesk tickets created (accounting for splits). If you split by session, total Zendesk tickets will exceed total Gladly conversations — document the expected multiplier.
Gladly → Zendesk specifics
- Field-level sampling
- Spot-check 2–5% of records across high-volume customers. Confirm comments are in order, custom fields populated, and attachments present.
- Timestamp verification
- Confirm created_at on Zendesk tickets matches Gladly conversation start times.
- Search checks
- Find migrated customers by email and original external ID.
- Attachment presence check
- For sampled tickets, verify that all attachment links resolve and files open correctly in Zendesk.
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.
How Does the Gladly Zendesk Object Mapping Work
| Gladly field | Zendesk field | Notes |
|---|---|---|
| Customer | User (end-user) | Map emails, phones, and custom attributes. Preserve customerId as external_id or a custom user field. Merged customers return 301 errors in Gladly's API — follow the redirect to the canonical customer ID. |
| Customer → Company | Organization | Gladly's company association maps to Zendesk organization membership. If B2B, roll up by email domain. |
| Conversation | Ticket(s) | One Gladly Conversation may produce multiple Zendesk tickets. Define a splitting strategy (by topic, by time gap, or by session). |
| Conversation Item | Comment | Each message, note, or reply becomes a Zendesk comment with public: true/false. Preserve author, timestamp, and channel. A Conversation Item is a single atomic message within a Conversation — it has a type (e.g., CHAT_MESSAGE, EMAIL, NOTE), an author, a timestamp, and optional attachments. |
| Topic | Tag or Custom Field | Gladly Topics are categorical labels. Map to a custom dropdown field for reporting, with tags as fallback. |
| Inbox | Group | Route tickets by mapping Gladly Inboxes to Zendesk Groups. Add ticket forms if one inbox represented a distinct workflow. |
| Agent | Agent (User) | Match by email. Agents must exist in Zendesk before ticket import to preserve historical assignment. |
| Team | Group | Gladly Teams map to Zendesk Groups. Multiple Teams may consolidate into fewer Groups. |
| Task | Ticket (type: task) | Gladly Tasks become Zendesk tickets with type: task. Do not bury tasks inside unrelated comments. |
| Answers (agent-facing) | Macro | Gladly Answers used as quick replies map to Zendesk Macros. Variables (e.g., {{customer.name}}) must be translated to Zendesk Liquid markup ({{ticket.requester.name}}). |
| Answers (public-facing) | Help Center Article | Public-facing Answers map to Zendesk Guide articles. Neither mapping is automated — both require manual creation or scripted import via the Help Center API. |
| Conversation Attachments | Comment Attachments | Not included in standard Gladly exports. Requires custom API extraction per conversation item. |
| Voice Transcript | Internal Comment | Zendesk Support has no standalone transcript object. Append as internal notes — structured format (speaker labels, timestamps) is lost. |
| Rules | Trigger or Automation | Cannot be migrated programmatically. Must be rebuilt in Zendesk after data load. Budget 2–5 days for a complex rule set (20+ rules). |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Customers / Users | low | Customer-to-User mapping is straightforward via email and phone matching, though merged customers in Gladly return 301 redirects that must be followed to the canonical ID. |
| Conversations / Tickets | high | One Gladly conversation may need to produce multiple Zendesk tickets, and the splitting logic is complex; additionally, the 100-conversation-per-customer API cap risks silent data loss for high-volume customers. |
| Conversation Items / Comments | medium | Individual messages map cleanly to comments, but the 1,000-item-per-conversation API limit can silently truncate long threads, and channel-specific metadata (social reactions, voice structure) is lost. |
| Attachments | high | Attachments are not included in standard Gladly exports and require custom per-item API extraction, making them the highest-risk data entity if not explicitly handled in the migration plan. |
| Topics / Tags & Custom Fields | low | Gladly Topics map to Zendesk tags or a custom dropdown field with minimal transformation, though teams should avoid field sprawl by staying within Zendesk's recommended ~400 ticket field limit. |
| Custom Attributes / Custom Fields | medium | Gladly's free-form text attributes must be normalized to Zendesk's strict field types, and inconsistent data formats (especially dates) will cause import failures if not pre-processed. |
| Agents & Teams / Groups | low | Agents are matched by email and teams map to Zendesk Groups, but all agents must exist in Zendesk before ticket import to preserve historical assignment records. |
| Voice Transcripts | medium | Zendesk has no standalone transcript object, so structured voice data (speaker labels, timestamps) must be appended as internal notes, losing its original structured format. |
| Answers / Macros & Help Center Articles | medium | Agent-facing Answers require manual variable syntax translation from Gladly placeholders to Zendesk Liquid markup, and public-facing Answers must be manually created or scripted via the Help Center API. |
| Rules / Triggers & Automations | high | Gladly routing rules and workflow logic cannot be exported or migrated programmatically and must be entirely rebuilt in Zendesk, with complex rule sets (20+ rules) requiring 2–5 days of manual effort. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Person-to-Ticket Model Translation
Gladly's single lifelong customer timeline must be decomposed into discrete Zendesk tickets using custom splitting logic based on topic, time gap, or session boundaries — and incorrect splitting renders reporting and AI triage tools ineffective.
Silent Data Truncation Limits
Gladly's API caps responses at 100 conversations per customer and 1,000 items per conversation with no standard pagination, causing silent data loss flagged only by a response header.
Attachment Extraction Complexity
Conversation attachments are not included in Gladly's standard exports and must be individually extracted via custom API calls per conversation item, significantly increasing migration time and API usage.
Timestamp and Status Preservation
Zendesk's standard ticket creation endpoint overwrites all dates with the current server time, requiring use of the Ticket Import API (/api/v2/imports/tickets) and careful status mapping from Gladly's binary Open/Closed to Zendesk's six-status lifecycle.
Custom Field Format Normalization
Gladly's free-form custom attributes must be cast to Zendesk's strict field types (dropdown, date, numeric, regex), requiring data normalization — particularly for inconsistent date formats that must conform to ISO 8601.
Automation and Rules Rebuild
Gladly's routing rules, answer variables, and workflow logic cannot be migrated programmatically and must be manually rebuilt as Zendesk triggers, automations, and macros with translated Liquid markup syntax.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
How long does a Gladly to Zendesk migration take?
A typical Gladly to Zendesk migration takes 10–17 business days end to end. The largest variable is data volume — extracting 50,000 customers at Gladly's 10 req/sec API limit takes roughly 36 hours for extraction alone. Zendesk's Ticket Import API can load approximately 40,000 tickets per hour on a Professional plan.
Can Gladly conversation history be preserved in Zendesk?
Yes, but you must use Zendesk's Ticket Import API (/api/v2/imports/tickets) to preserve original timestamps. The standard ticket creation endpoint overwrites created_at with the current server time. Each Gladly Conversation is split into one or more Zendesk tickets with comments in chronological order.
What data cannot be migrated from Gladly to Zendesk?
Gladly Sidekick AI metadata, voice AI transcripts in structured form, routing queue history, Liveboard metrics, and satisfaction survey responses have no direct Zendesk equivalent. SLAs are not calculated on imported tickets. Rules and automations must be manually rebuilt in Zendesk.
Will attachments migrate from Gladly to Zendesk?
Not through standard exports — Gladly's CSV and bulk exports are text-only. To preserve attachments, you must extract them via Gladly's REST API per conversation item, upload each file to Zendesk's Attachments API, and link the upload token to the corresponding comment during import.
Does the Gladly API have pagination for conversations?
No. Gladly's conversation listing endpoint returns at most 100 conversations per customer and is not paginated. If a customer exceeds this limit, the API sets a Gladly-Limited-Data response header but provides no standard mechanism to retrieve additional records. Contact Gladly support for bulk extraction in these cases.