There's no native Front-to-Freshchat migration path. You need API-based extraction, schema translation between email-thread and chat-session models, and careful handling of rate limits on both sides.
Migrating from Front to Freshchat is a fundamental architecture shift from an email-first, shared-inbox paradigm to a session-based messaging platform with no native import path, vendor-provided migration wizard, or official connector between the two systems. The data models differ at every level: Front's email threads must be flattened into Freshchat's linear chat sessions, Front's multi-handle contacts must be deduplicated to a single canonical email, and Front's first-class Account objects have no equivalent in standalone Freshchat (requiring Freshdesk Omni for company-level management). Every record—contacts, conversations, messages, attachments, and tags—requires custom extraction via Front's Core API, schema transformation, and loading through Freshchat's v2 REST API, with rate limits on both sides constraining throughput to roughly 40–45 effective requests per minute.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
Request the Front account export as soon as planning starts
Front routes these exports through support and acknowledges requests within 72 hours. The actual export delivery can take 1–2 weeks depending on data volume.
Freshchat's PUT replaces the full contact; always include all desired fields, not just
Freshchat's PUT replaces the full contact; always include all desired fields, not just changed ones, to avoid unintentional data loss. This is a common source of data corruption during migration—partial updates silently overwrite existing data with null values. Always GET the current record, merge your changes, then PUT the complete object.
Freshchat's /v2/channels endpoint doesn't return WhatsApp channels
The only way to get the channel ID for WhatsApp is looking first for a conversation you know is from WhatsApp, then calling /v2/conversations/:id—the payload returned contains the channel id. Plan for this workaround if you're mapping WhatsApp conversations. Create a test WhatsApp conversation manually, then extract the channel ID from the API response.
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 Front
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 Freshchat can hold your support model
Walk your current workflow through Freshchat: 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 Front → Freshchat 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.
Front → Freshchat specifics
- Freshworks ecosystem consolidation
- Teams already using Freshdesk, Freshsales, or Freshservice want a unified messaging layer rather than managing Front as a standalone tool.
- Chat-first support model
- Organizations shifting from email-heavy workflows to real-time messaging find Freshchat's bot builder, AI Agent Studio, and widget-based intake more aligned.
- Cost reduction
- Front's per-seat pricing (Starter at $19/seat/month, Growth at $59, Scale at $99, Premier at $229) scales fast for growing teams. Freshchat's free tier (up to 10 agents) and Growth plan ($19/agent/month) attract cost-sensitive operations.
- Marketing automation and proactive engagement
- Freshchat includes Contacts and Accounts, FAQs, AI Agent Studio, Campaigns for website/SMS/WhatsApp, and Support Analytics.
- Session-based routing
- Shifting from inbox-based triage to skill-based, real-time chat routing with IntelliAssign.
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 Front 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 Front 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 Front 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
Front → Freshchat specifics
- Pre-built Front extraction pipelines
- that handle pagination, rate limiting, attachment downloads, and comment threading without custom development.
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 Front → Freshchat 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 Front → Freshchat 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 Freshchat 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.
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Split contact names
into first/last. Handle edge cases: single-word names, names with multiple spaces, names with prefixes (Dr., Mr.)
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Normalize phone numbers
to E.164 using phonenumbers library (Python) or libphonenumber (Node.js)
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Convert picklist/enum values
to match Freshchat property types. On Growth plans, all custom properties are text—dropdown values become plain strings.
Data Format Converter Reshape the export into the format Freshchat's importer expects
Front → Freshchat specifics
- Field-level mapping and transformation
- for custom fields, tags-to-labels conversion, and picklist normalization.
- Strip complex HTML
- from email bodies. Freshchat's chat UI does not render nested HTML tables, <style> blocks, or complex CSS. Use a library like bleach (Python) to whitelist basic HTML tags (<p>, <br>, <a>, <b>, <i>, <ul>, <li>) and strip everything else.
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 Freshchat sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Freshchat 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 Freshchat'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.
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Test/spam data
Exclude test conversations, spam-flagged threads, and internal sandbox data.
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 Freshchat, 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 Front 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 Freshchat'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 Front 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 Front 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 Front and Freshchat 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 Freshchat, 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 Front 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
Map every Front rule to a Freshchat automation. Document what each rule did (trigger, condition, action), then recreate using Freshchat's automation builder or IntelliAssign. For rebuild planning, see Your Help Desk Migration's Secret Saboteur: Automations, Macros, and Workflows.
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Configure channels
Set up web chat widgets (Freshchat provides a JavaScript snippet for website embedding), WhatsApp Business API integration, Facebook Messenger, and other channels you were using in Front.
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Set up AI Agent Studio
If you were using chatbot integrations with Front, rebuild conversation flows in Freshchat's AI Agent Studio. Start with your top 5 most common customer intents.
Front → Freshchat specifics
- Validation and reconciliation
- built into every migration—record counts, field-level sampling, and relationship integrity checks.
- Agent onboarding
- Freshchat's interface is fundamentally different from Front's. Train agents on the difference between an email thread (Front) and a continuous chat timeline (Freshchat). Plan at least two 1-hour training sessions covering: the conversation view, team inbox, canned responses, the agent assignment workflow, IntelliAssign behavior, and private notes vs. public messages.
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
| Front field | Freshchat field | Notes |
|---|---|---|
| Contact | User/Contact | Map email + phone handles. Freshchat uses reference_id for external identifiers. |
| Account | Company (Freshdesk Omni) | Standalone Freshchat has no direct Account equivalent. Requires Freshdesk Omni for Company objects. |
| Conversation | Conversation | Front conversations are email threads; Freshchat conversations are chat sessions. Structure differs significantly. |
| Message | Message | Map sender, body, timestamp. Freshchat message types: normal, private, system. |
| Comment | Private Note | Front's internal comments → Freshchat private messages within conversation. |
| Tag | Conversation Property / Label | No direct equivalent. Convert to conversation properties or custom labels. |
| Inbox | Channel | Front inboxes map loosely to Freshchat channels (web, email, WhatsApp, etc.). |
| Teammate | Agent | Map by email. There is no DELETE /v2/agents endpoint in the public REST API; agent lifecycle management requires SCIM (Enterprise) or manual admin action. |
| Rule | Automation | Must be rebuilt manually. No import path. |
| Custom Field | Contact Property | Limited types available on lower-tier Freshchat plans. Enum/dropdown fields require conversion to text on Growth plan. |
| Front KB Article | Freshchat FAQ | No API import for FAQs; must be recreated manually or via FAQ API if available on your plan. |
| Chatbot flow | AI Agent Studio flow | No import path. Must be rebuilt in Freshchat's AI Agent Studio. |
Front Freshchat
| Front field | Freshchat field | Notes |
|---|---|---|
| name | first_name + last_name | Split on first space. If no space, entire string → first_name, last_name → empty string. |
| handles [].handle (email) | Direct map, validate RFC 5322 format. If multiple email handles, pick most recently active. | |
| handles [].handle (phone) | phone | Normalize to E.164 using libphonenumber or equivalent. |
| description | Custom property | Store as custom property or note. Max 1024 chars in Freshchat text properties. |
| custom_fields.* | properties.* | Type-check each; dropdowns → text if unsupported on your plan. |
| account.name | company.name | Requires Freshdesk Omni. |
| avatar_url | avatar.url | Download and re-upload. Freshchat accepts JPEG/PNG, max 5MB. |
| is_spammer | — | No equivalent; filter during extraction or store as boolean custom property. |
| created_at | created_time | ISO 8601 format. Timezone conversion may be needed. |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Contacts | medium | Contacts can be migrated via API or CSV, but multi-handle contacts must be deduplicated to a single canonical email, risking data loss for contacts with multiple email addresses. |
| Conversations | high | Front's threaded email conversations must be flattened into Freshchat's linear chat session model, and Freshchat has no CSV conversation import, making API-based migration the only option for preserving history. |
| Attachments | high | Front's native data export excludes attachments entirely, and large attachments (>25MB) often fail through third-party tools, requiring custom API handling with additional storage and transfer logic. |
| Tags | medium | Front's tags must be mapped to Freshchat's Labels or Conversation Properties, but Front's data export does not include tags, so API extraction is required. |
| Custom Fields | high | Front supports up to 50 custom fields per category, but Freshchat's custom property limits and available types vary by plan tier, and Zapier integration does not support custom contact fields. |
| Accounts / Companies | high | Standalone Freshchat has no Account or Company object, so Front's entire account hierarchy is lost unless the organization uses Freshdesk Omni. |
| Internal Comments | medium | Front's internal comments on conversations can map to Freshchat's private notes, but the parallel threading of comments alongside messages must be serialized into a linear sequence. |
| Agents / Teammates | low | Agent records can be recreated in Freshchat, but the Contact/Agent object split in Freshchat's API requires careful handling to avoid conflating the two during migration scripts. |
| Routing Rules / Automations | high | Front's inbox-based rules have no direct equivalent in Freshchat's channel/group/IntelliAssign routing model and must be manually rebuilt rather than programmatically migrated. |
| Knowledge Base / FAQs | medium | Front's separate Knowledge Base content must be manually recreated in Freshchat's built-in FAQ system, as there is no automated migration path between the two. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
No Native Migration Path
There is no vendor-provided migration wizard, official connector, or built-in import tool between Front and Freshchat, requiring fully custom extraction, transformation, and loading for every data entity.
Conversation Model Incompatibility
Front conversations are email threads containing messages and parallel internal comments, which must be flattened into Freshchat's linear, session-based chat conversation format.
Multi-Handle Contact Deduplication
Front contacts can have multiple email handles, phone numbers, and social accounts, but Freshchat deduplicates on a single email address, requiring canonical email resolution during transformation.
API Rate Limit Constraints
Both Front and Freshchat enforce rate limits starting at approximately 50 requests per minute, meaning a dataset of 50,000 conversations with 8 messages each can take 4–6 days of continuous API execution.
Account Hierarchy Loss
Standalone Freshchat lacks a Company or Account object, so Front's account-based routing and reporting structures are entirely lost unless the team upgrades to Freshdesk Omni.
Custom Field Schema Mismatch
Front supports up to 50 custom fields per category across contacts, accounts, conversations, and more, while Freshchat's custom property availability varies significantly by plan tier and object type.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can I export data from Front to Freshchat directly?
No. There is no native export/import path between Front and Freshchat. Front's built-in data export (via support request) provides CSV/EML files but excludes attachments, tags, templates, and individual inbox data. Freshchat has no CSV conversation import. You need API-based extraction or a third-party migration tool.
What are the API rate limits for Front and Freshchat during migration?
Front's API rate limit starts at 50 requests per minute and varies by plan, enforced per company. Freshchat's API rate limit is typically capped at around 50 requests per minute as well. Both return HTTP 429 responses when exceeded. For large migrations, plan for multi-day execution windows or purchase rate limit add-ons from Front (300 additional calls per minute per add-on).
How do Front tags map to Freshchat?
Freshchat has no direct tag equivalent. Front tags can be converted to Freshchat conversation properties (key-value pairs), appended as text fields, or mapped to labels if available on your plan. Tags used in Front rules must be manually recreated as Freshchat automation conditions.
Does Freshchat support Front's account (company) structure?
Standalone Freshchat does not have an Account or Company object. If your team relies on account-based routing or reporting in Front, you need Freshdesk Omni, which includes company-level contact management. Without it, the account hierarchy is lost.
Can Freshchat preserve original Front message timestamps?
Not reliably. Freshchat's public create-conversation and create-message API examples do not clearly expose a created_time input for backdating. Most teams store legacy timestamps in custom fields or prepend them in the message body. Verify what your Freshchat tier supports before building the pipeline.