Freshdesk to Gladly is a data-model translation from tickets to customer timelines. Gladly's historical import is text-only with no attachments. Plan 2 to 4 weeks for mid-size instances.
There is no native migration path from Freshdesk to Gladly; the move requires a full data-model translation from Freshdesk's ticket-centric architecture to Gladly's customer-centric Conversation Timeline model. Every Freshdesk ticket belonging to the same requester must be consolidated into a single Gladly Customer profile, with each ticket's thread becoming Conversation Items on that customer's timeline. Gladly's historical import is text-only and does not support attachments, images, recordings, metrics, or routing assignments, making silent data loss the single biggest risk. Automations, SLA Policies, and Dispatch Rules cannot be migrated programmatically and must be manually rebuilt in Gladly's Rules engine.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
TL;DR: Freshdesk to Gladly Migration
Migrating from Freshdesk to Gladly is a data-model translation, not a field-mapping exercise. Freshdesk organizes support around discrete tickets. Gladly organizes support around the customer, storing all interactions in a single lifelong Conversation Timeline with no ticket numbers. The single biggest risk is silent data loss: Gladly's documented historical import is text-only and does not support attachments, images, recordings, metrics, or routing assignments. Imported history is not searchable or reportable in Gladly — it is reference-only on the customer profile. Realistic timeline: 2 to 4 weeks for a mid-size instance (10,000 to 100,000 tickets). Freshdesk Automations, SLA Policies, and Dispatch Rules cannot be migrated programmatically and must be rebuilt manually. Teams with fewer than 5,000 tickets and simple schemas can attempt a scripted DIY migration. For anything larger, or with zero-downtime requirements, a managed migration service is the safer path.
Gladly's historical import is text-based only
It does not support the import of historical images, recordings, attachments, metrics, or routing/conversation assignments. Imported history (other than contact information) is not searchable or reportable in Gladly. Plan for this constraint before you begin. (help.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 Freshdesk
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 Freshdesk → 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.
Freshdesk → Gladly specifics
- Person-centric model over ticket-centric model
- Gladly replaces discrete ticket numbers with a single, continuous conversation per customer across all channels — email, chat, SMS, voice, social. Agents see every past interaction in one timeline instead of searching for ticket IDs. In official customer stories, Deckers said its prior ticket-based CRM could not meet its goals and later reported a 40 percent service-level improvement within a month, while Birdies said ticketing created duplicate work and hid repeat contacts. (help.gladly.com)
- Native omnichannel routing
- Gladly treats channel-switching (chat to phone to email) as a single thread. Freshdesk requires add-ons for voice and treats each channel interaction as a separate ticket.
- B2C relationship focus and pricing model
- Gladly is purpose-built for e-commerce and retail brands that measure customer lifetime value, not just ticket resolution time. Gladly uses per-agent pricing with unlimited conversations, whereas Freshdesk's pricing scales with ticket volume and add-on modules.
- The default API scope is the last 30 days
- Use the updated_since parameter for incremental pulls, or use the full account export for complete history. (developers.freshdesk.com)
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 Freshdesk 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 Freshdesk 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 Freshdesk 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
Freshdesk → Gladly specifics
- Ticket status lifecycle
- (Open, Pending, Resolved, Closed). Gladly Conversations are either open or closed. There is no Pending or Resolved state.
- Ticket priority
- (Low, Medium, High, Urgent). Gladly does not use priority levels on conversations.
- Private note state
- Freshdesk conversations have a private boolean. Gladly's historical CSV schema has no privacy field. Prefix internal notes with a clear marker (e.g., [INTERNAL NOTE]) or exclude them.
- Satisfaction ratings
- Historical CSAT scores from Freshdesk do not transfer. Gladly has its own CSAT mechanism.
- Time-tracking and SLA metrics
- Historical SLA data (first response time, resolution time) does not migrate. Export these to a data warehouse before cutover if you need reporting continuity.
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 Freshdesk → 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 Freshdesk → 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 Freshdesk 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 Freshdesk 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 Freshdesk read-only rather than cancelled — cancelling the old contract on day one removes your only fallback.
Don't move on until
- Full historical load complete and counts matched
- Inbound channels repointed and verified with live test tickets
- Rollback decision point passed explicitly, not by default
06 Validation Prove the migration is complete, then close it out.
Objective Documented evidence that data, workflow and reporting all survived, and a signed acceptance.
Keep these open
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Run the full reconciliation
Compare source and target on every object: total counts, counts by status, counts by group, attachment counts, and field-level spot checks on a random sample. Produce one report you can hand to an auditor.
Migration Validation Tool Reconcile Freshdesk 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 Freshdesk 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
Compare total contacts, tickets, and conversation entries between Freshdesk and Gladly. The Gladly Conversation Item count will be higher than the Freshdesk ticket count due to the one-to-many mapping (one ticket becomes multiple items).
Freshdesk → Gladly specifics
- Field-level spot check
- Select a random 5% sample of customer profiles and verify that email, name, phone, custom attributes, and conversation history match the source.
- Timestamp verification
- Confirm that conversation items appear in chronological order on the Gladly timeline. Out-of-order timestamps indicate a transformation bug.
- Agent assignment check
- Verify that agents appear correctly in Gladly and that team/inbox assignments match the original group structure.
- Legacy link verification
- Open 10 to 15 imported conversation items and confirm that link.url values resolve to the correct Freshdesk ticket.
- Knowledge base review
- Open imported Answers in Gladly and compare content and formatting against the original Freshdesk Solution Articles.
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 Freshdesk Data Map Gladly Objects
| Freshdesk field | Gladly field | Notes |
|---|---|---|
| Ticket | Conversation Items (on Customer Timeline) | Each ticket becomes a set of timeline items under the matching Customer. No 1:1 ticket equivalent exists in Gladly. |
| Contact | Customer | Matched by email. Email is a unique identifier in Gladly. Phone and name are also stored on the Customer profile. |
| Company | Customer attribute (or external lookup via Lookup Adaptor) | Gladly has no native Company object. Company name must be stored as a custom attribute on the Customer profile, or surfaced at runtime via Gladly's Lookup Adaptor. |
| Conversation (replies/notes) | Conversation Item | Each reply or note becomes a separate Conversation Item on the customer's timeline. |
| Agent | Agent | Mapped by email. Agents must be provisioned in Gladly before import. Historical assignment is not preserved in the import. |
| Group | Team / Inbox | Freshdesk Groups map to Gladly Teams or Inboxes depending on routing configuration. |
| Tag | Topic | Freshdesk Tags map to Gladly Topics. Topics must be pre-created in Gladly. |
| Custom Field (ticket-level) | Customer Custom Attribute | Gladly does not support ticket-level custom fields. Data must move to the Customer profile or be appended to conversation item text. |
| Product | Brand | Freshdesk Products map to Gladly Brands. Multi-product instances require explicit brand mapping before import. |
| Solution Article | Answer | Gladly Answers can be imported via CSV with Markdown formatting. |
| Attachment | Not supported in historical import | Gladly's import is text-based only. Attachments must be stored externally and linked. |
| Automation / Dispatch Rule | Rule (manual rebuild) | Cannot be migrated programmatically. Must be recreated in Gladly's Rules engine and People Match. |
| SLA Policy | SLA (manual rebuild) | No programmatic migration path. |
| Canned Response | Answer (manual rebuild) | Can be imported as Answers via CSV but requires manual formatting. |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Tickets | high | Tickets have no direct equivalent in Gladly and must be decomposed into Conversation Items on a customer timeline, with status, priority, and ticket ID preserved only as text in the title or body fields. |
| Contacts | medium | Contacts map to Gladly Customers by email, but secondary emails (other_emails) and phone numbers require special handling since imported phones are marked as OTHER rather than MOBILE. |
| Companies | high | Gladly has no native Company object, so company data must be stored as a custom attribute on the Customer profile or surfaced at runtime via Gladly's Lookup Adaptor. |
| Attachments | high | Gladly's historical import is text-only and does not support attachments, images, or recordings, meaning all file-based data must be externalized and linked or will be lost. |
| Custom Fields | high | Freshdesk ticket-level custom fields must be remapped to customer-scoped attributes in Gladly, requiring merge logic when a customer has conflicting values across multiple tickets. |
| Tags | low | Freshdesk Tags map to Gladly Topics, but Topics must be pre-created in Gladly before import and naming conventions may need to be adjusted. |
| Agents | low | Agents map by email and must be provisioned in Gladly before import, though historical ticket assignment data is not preserved in the import process. |
| Automations and SLA Policies | high | No programmatic migration path exists; all automations, dispatch rules, and SLA policies must be manually recreated in Gladly's Rules engine. |
| Solution Articles | medium | Freshdesk Solution Articles can be imported as Gladly Answers via CSV with Markdown formatting, but manual formatting adjustments are typically required. |
| CSAT and SLA Metrics | high | Historical satisfaction ratings, first response times, and resolution time metrics do not transfer to Gladly and must be exported to a data warehouse before cutover for reporting continuity. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Ticket-to-Timeline Data Model Translation
Freshdesk creates discrete tickets per issue, but Gladly has no ticket object — all interactions for a customer must be grouped by requester email and flattened into a single Conversation Timeline, requiring deduplication and consolidation logic.
Text-Only Historical Import Constraint
Gladly's historical import accepts only text-based data and does not support attachments, images, call recordings, or SLA metrics, meaning these assets must be stored externally and linked or accepted as lost.
Custom Field Scope Mismatch
Freshdesk custom fields are ticket-scoped (prefixed with cf_), but Gladly only supports customer-scoped custom attributes, so conflicting values across multiple tickets from the same customer require merge logic to determine which value survives.
Status and Priority Model Gaps
Freshdesk's four-state ticket lifecycle (Open, Pending, Resolved, Closed) and four priority levels have no equivalent in Gladly, which only supports open or closed Conversations with no priority concept.
Automation and SLA Manual Rebuild
Freshdesk Automations, Dispatch Rules, and SLA Policies cannot be migrated programmatically and must be manually recreated in Gladly's Rules engine and People Match configuration.
Single Open Conversation Constraint
Gladly allows only one open Conversation per customer at a time, so customers with multiple unresolved Freshdesk tickets cannot have them all imported as open Conversations simultaneously.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can I migrate Freshdesk to Gladly without losing data?
You can migrate all text-based conversation history without loss. However, Gladly's historical import does not support attachments, inline images, recordings, or SLA metrics. These data types will not transfer through any standard import path. The practical workaround is storing attachments in an external archive and linking to them from the imported conversation items.
How long does a Freshdesk to Gladly migration take?
A small instance (under 5,000 tickets) takes 5 to 8 business days. A mid-size instance (10,000 to 50,000 tickets) takes 2 to 3 weeks. Enterprise instances over 100,000 tickets with complex schemas typically require 3 to 5 weeks including testing and validation.
What data cannot be migrated from Freshdesk to Gladly?
Freshdesk Automations, Dispatch Rules, SLA Policies, Canned Responses (as functional macros), satisfaction survey results, time-tracking entries, and ticket-level custom field structures cannot be migrated programmatically. They must be rebuilt manually in Gladly. Attachments, images, and call recordings are also excluded from Gladly's historical import.
Does Gladly have a native import tool for Freshdesk?
No. Gladly supports direct JSON imports from Zendesk but not from Freshdesk. For Freshdesk data, you must export and transform it into Gladly's CSV template format. Gladly's implementation team then processes the upload via a shared Dropbox link.
How do I handle open Freshdesk tickets during migration to Gladly?
Gladly allows only one open Conversation per customer, so you cannot import multiple open tickets for the same person. Treat open work as a cutover workflow decision: consolidate, close, or recreate only the live backlog that still needs action. Run both systems in parallel for 1 to 2 weeks until open Freshdesk tickets are resolved.