Zendesk to Gladly is a high-complexity schema translation — tickets become conversation items on person-centric timelines. 2–4 week timeline. Biggest risk: attachments and CSAT cannot be imported.
There is no native migration path from Zendesk to Gladly; the transfer requires a full schema translation from Zendesk's ticket-centric architecture into Gladly's person-centric timeline, where multiple discrete tickets are collapsed into conversation items on a single, lifelong customer profile. Gladly's historical import is text-only and does not support attachments, images, recordings, or CSAT data, creating significant silent data-loss risk. Core workflow objects — Macros, Triggers, Automations, SLAs, and Views — have no programmatic migration path and must be manually rebuilt as Gladly Answers, Rules, and Inboxes. Organizations, ticket statuses beyond Open/Closed, Side Conversations, and Ticket Forms have no direct Gladly equivalent, requiring custom attribute mapping or external Lookup Adapters.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
TL;DR — Zendesk to Gladly Migration
Migrating from Zendesk to Gladly is a high-complexity data-model translation, not a simple field-mapping exercise. Zendesk's ticket-centric architecture must be restructured into Gladly's person-centric timeline, where discrete tickets become conversation items on a lifelong customer record. Realistic timeline: 2–4 weeks end to end for a mid-size instance (20,000–100,000 tickets). The single biggest risk is silent data loss — Gladly's historical import is text-based only and does not support attachments, images, recordings, metrics, or routing assignments. (help.gladly.com) Zendesk Macros, Triggers, Automations, and SLAs cannot be migrated programmatically and must be rebuilt manually in Gladly. Teams with fewer than 10,000 tickets and no attachment-heavy threads can work through Gladly's native JSON import with their implementation team. Anything above that — or with complex custom fields, duplicate requesters, and zero-downtime requirements — should use a managed migration service.
What has no clean equivalent in Gladly
Zendesk Organizations, SLA Policies, Side Conversations, Ticket Forms, Ticket Statuses (beyond Open/Closed), Light Agents, CSAT history, merged ticket cross-links, and Zendesk Talk call recordings have no direct Gladly counterpart. Plan for these gaps before migration, not during.
Throughput math
For 50,000 Zendesk tickets mapping to 20,000 unique customers with an average of 2.5 tickets each, expect the Gladly ingestion phase alone to take 2–5 hours when accounting for rate-limit backoff and retry logic. Plan overnight migration windows. Gladly's production reporting data also has about a one-hour latency, so do not rely on reports as a same-hour go-live validation source. (help.gladly.com)
If you skip canonical email design, you will split the same human into multiple Gladly profiles
Gladly's historical import groups all activity by email, and records without a valid email are silently rejected. Multi-brand or multi-channel Zendesk instances are especially vulnerable — the same customer may exist as [email protected] in one brand and [email protected] in another. Build an email canonicalization map before import. (help.gladly.com)
Order-of-operations failure mode
If you load conversation items before the customer profile exists, items created via the timeline API will generate a new customer profile automatically — but without the full profile data (name, phone, custom attributes). This creates duplicate, incomplete customer records that must be manually merged via the Gladly UI or by contacting Gladly support. There is no bulk merge API.
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 Zendesk
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 Gladly can hold your support model
Walk your current workflow through Gladly: 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 Zendesk → Gladly 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.
Zendesk → Gladly specifics
- Person-centric architecture
- Gladly organizes all interactions around a single customer profile — every email, chat, SMS, and phone call lives on one continuous timeline. Zendesk treats each interaction as a separate ticket. For brands with high repeat-contact rates (e-commerce, DTC, hospitality), this eliminates repeated context-gathering across tickets. For a deeper overview of Gladly's architecture, see our Gladly platform guide.
- Channel-native design
- Gladly treats every channel (voice, email, chat, SMS, social) as a first-class citizen within a single conversation thread. Zendesk requires separate channel configurations and creates distinct tickets per channel, which fragments context across ticket records.
- Simplified pricing model
- Gladly charges per agent (Hero) with unlimited conversations. Zendesk's usage-based add-ons for AI, advanced analytics, and the High Volume API can inflate costs at scale — particularly for high-volume support teams exceeding 100,000 tickets/month.
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 Zendesk 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 Zendesk 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 Zendesk 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
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 Zendesk → Gladly 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 Zendesk → Gladly 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 Gladly 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 Gladly sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Gladly 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 Gladly'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 Gladly, 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 Zendesk 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 Gladly'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 Zendesk 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 Zendesk read-only rather than cancelled — cancelling the old contract on day one removes your only fallback.
Zendesk → Gladly specifics
- Re-route inbound email
- back to Zendesk by reverting DNS/forwarding changes (MX record TTL should be set low — 300 seconds — before cutover to enable fast rollback).
- Re-enable Zendesk chat/messaging
- widgets on your site by swapping the JavaScript snippet.
- Zendesk phone routing
- can be restored by updating your IVR/telephony provider to point back to Zendesk Talk.
- Records created in Gladly during the interim
- remain in Gladly but are not automatically synced back to Zendesk. Export any Gladly-created tickets via the Gladly API and import them into Zendesk if needed for continuity.
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 Zendesk and Gladly 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 Gladly, 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 Zendesk 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.
How Does Zendesk Data Map Gladly
| Zendesk field | Gladly field | Notes |
|---|---|---|
| Ticket | Conversation + Conversation Items | Each ticket becomes a conversation; each comment becomes a conversation item on the customer's timeline. Ticket numbers are not preserved. |
| Ticket Comment (public) | Conversation Item (email/chat/SMS) | Mapped as timeline events. Original timestamps preserved via historical import using occurredAt field. |
| Ticket Comment (internal note) | Conversation Item (note) | Internal notes import as note-type items on the timeline. |
| User (end-user / requester) | Customer | Email is the unique identifier. Duplicate emails across Zendesk users will cause 409 Conflict responses in Gladly. |
| User (agent) | Agent | Agent profiles must be created in Gladly before import. Gladly uses email-based matching. |
| Organization | No direct equivalent | Gladly has no Organization object. Map to custom attributes on the Customer profile or use Gladly's Lookup Adapters to pull org data from your CRM at query time. |
| Group | Team | Zendesk Groups map to Gladly Teams. Team membership must be configured manually or via API. |
| Tags | Topics + Freeform Topics | Zendesk tags map to Gladly Topics (predefined) or freeform topics. Topics must be created before import; the API rejects payloads referencing nonexistent Topics. |
| Custom Fields (ticket) | Conversation Custom Attributes | Must be configured by Gladly Professional Services before they display in the UI. |
| Custom Fields (user) | Customer Custom Attributes | Same constraint — must be pre-configured in Gladly. |
| Macros | Answers (partial) | No programmatic migration path. Must be rebuilt manually in Gladly as Answers or Rules. |
| Triggers / Automations | Rules | No migration path. Gladly Rules use a different logic model and do not act on closed conversations. Rebuild from scratch. |
| SLAs | No direct equivalent | Gladly does not have a native SLA engine comparable to Zendesk's. Monitor via reporting and Rules-based escalation. |
| Views | Inboxes (partial) | Zendesk Views are filter-based lists. Gladly Inboxes are routing containers. Different paradigm — requires redesign. |
| Help Center Articles | Answers | Can be migrated via Gladly's Answers API. Formatting differences (Zendesk uses Markdown/HTML; Gladly Answers have their own structure) require manual review. |
| Attachments | Not imported via historical path | Gladly's historical import is text-based only. Attachments, images, and recordings are not supported. (help.gladly.com) |
| Satisfaction Ratings (CSAT) | Not imported | No historical CSAT import into Gladly. Export and archive separately from Zendesk Explore. |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Tickets / Conversations | medium | Tickets migrate as conversations and conversation items but lose ticket numbers, statuses beyond Open/Closed, and the discrete lifecycle model, requiring careful many-to-one mapping per customer. |
| Contacts / Customers | medium | End-users map to Customer profiles by email, but duplicate emails cause 409 Conflict errors and phone numbers must be normalized to E.164 format before import. |
| Attachments | high | Gladly's historical import is text-only and does not support attachments, images, or recordings, meaning all file-based data is permanently lost unless archived separately. |
| Custom Fields | medium | Ticket and user custom fields must be pre-configured by Gladly Professional Services before import, multi-select fields are unsupported, and values are stored as unvalidated strings risking data inconsistency. |
| Organizations | high | Gladly has no Organization object, so all org-based routing, reporting, and access control logic must be completely rearchitected using custom attributes or external Lookup Adapters. |
| Macros / Answers | high | There is no programmatic migration path; every Zendesk Macro must be manually recreated as a Gladly Answer, which is time-intensive for teams with large macro libraries. |
| Triggers and Automations | high | Gladly Rules use a fundamentally different logic model that does not act on closed conversations, requiring a complete rebuild of all Zendesk Triggers and Automations from scratch. |
| SLA Policies | high | Gladly has no native SLA engine comparable to Zendesk's, so SLA enforcement must be approximated through reporting and Rules-based escalation workarounds. |
| Tags / Topics | low | Tags map to Gladly Topics or freeform topics, but all predefined Topics must be created in Gladly before import or the API will reject the payload. |
| CSAT / Satisfaction Ratings | high | There is no historical CSAT import into Gladly; all satisfaction rating data must be exported from Zendesk Explore and archived externally before cutover. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Ticket-to-Conversation Schema Translation
Zendesk's discrete, numbered tickets must be restructured into Gladly's person-centric timeline as conversation items on a single customer profile, requiring a many-to-one mapping that fundamentally changes how history is organized.
Attachment and Media Loss
Gladly's historical import path is text-based only, meaning all attachments, inline images, call recordings, and embedded media from Zendesk tickets are silently dropped during import.
Organization Object Gap
Gladly has no Organization object, so Zendesk Organizations used for routing, reporting, or access control must be rearchitected as custom attributes on Customer profiles or handled via external Lookup Adapters.
Workflow and Automation Rebuild
Zendesk Macros, Triggers, Automations, SLAs, and Views cannot be migrated programmatically and must be manually rebuilt in Gladly as Answers, Rules, and Inboxes using a fundamentally different logic model.
Duplicate Requester Conflicts
Gladly uses email as the unique customer identifier, so duplicate email addresses across Zendesk end-user records will produce 409 Conflict errors during import, requiring deduplication before loading.
API Rate Limit Throughput Bottleneck
Gladly enforces a hard cap of 10 requests per second across all API methods, which combined with Zendesk's extraction limits of 10 requests per minute on incremental exports, directly constrains migration duration for large instances.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
How long does a Zendesk to Gladly migration take?
A Zendesk to Gladly migration typically takes 2–4 weeks from discovery to go-live. Instances under 10,000 tickets with simple configurations can complete in 7–10 business days. Enterprise instances with 500,000+ tickets and complex automations may take 4–6 weeks.
What data cannot be migrated from Zendesk to Gladly?
Attachments, CSAT ratings, SLA metrics, side conversations, ticket forms, suspended tickets, and merged ticket cross-links cannot be migrated to Gladly. Macros, Triggers, Automations, and Views must be manually rebuilt as Gladly Rules, Answers, and Inboxes. Zendesk Organizations have no Gladly equivalent and must be mapped to custom attributes.
Can Zendesk ticket history be preserved in Gladly?
Yes. Zendesk ticket comments import as conversation items on the customer's Gladly timeline with original timestamps preserved. However, imported items are text-based only — no attachments, images, or recordings — and are not searchable in Gladly's search function, though agents can view them on the customer profile.
How much does a Zendesk to Gladly migration cost?
DIY API migration costs 120–200 hours of engineering time ($15,000–$40,000 at typical rates). Managed migration services range from $5,000–$25,000 depending on volume and complexity. Gladly's native historical import incurs a Professional Services fee negotiated during your contract.
Does Gladly have a native Zendesk import tool?
Gladly supports Zendesk as a native source for historical conversation imports. You export Zendesk data in JSON format, upload it to a Gladly-provided Dropbox link, and their implementation team processes the import. This is text-based only — no attachments — and imported items are not searchable or reportable.