Gladly to Intercom migration requires splitting continuous customer timelines into discrete conversations under Intercom's 500-part limit. Expect 2–4 weeks for 50K–150K items.
Migrating from Gladly to Intercom is a high-complexity data-model translation project with no native migration path. Gladly's person-centric architecture stores all customer interactions in a single, lifelong Conversation Timeline without ticket numbers, while Intercom treats each interaction as a discrete Conversation object hard-limited to 500 parts. Custom extraction and transformation scripts are required to split timelines into separate conversations, normalize unsupported channel types (voice, voicemail, Instagram Direct) into internal notes, flatten nested topic hierarchies into tags, and convert timestamps from ISO 8601 to Unix epoch — along with manual rebuilding of routing rules, SLA policies, People Match logic, and AI configurations.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
TL;DR — Gladly to Intercom Migration
Migrating from Gladly to Intercom is a high-complexity data-model translation project. Gladly stores all customer interactions in a single, lifelong Conversation Timeline with no ticket numbers. Intercom stores interactions as discrete Conversations, each hard-limited to 500 parts (Intercom API Reference: Conversations). A typical migration takes 2–4 weeks for 50,000–150,000 conversation items. The single biggest risk is silent data truncation — Gladly's API returns at most 100 conversations per customer and caps timeline items at 1,000 per conversation, flagged only by the Gladly-Limited-Data response header (Gladly API Docs: Rate Limits). If your extraction script ignores that header, you lose history with no error message. Gladly automations, routing rules, SLA configurations, and People Match rules cannot be migrated and must be rebuilt manually in Intercom. Teams with fewer than 5,000 conversation items and simple schemas can attempt a scripted DIY migration (100–160 engineer-hours). For anything larger — especially with voice history, GDPR data-residency requirements, or zero-downtime needs — a managed migration service is the safer path. Written July 2025. Targets Gladly REST API and Intercom API v2.11.
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 Intercom can hold your support model
Walk your current workflow through Intercom: 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 → Intercom 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 → Intercom specifics
- Long timelines must be split
- A single Gladly Customer Timeline must be divided into multiple Intercom Conversations. Gladly does have Conversation objects within the unified timeline, and each maps to one Intercom Conversation. But if a single Gladly Conversation exceeds 500 items, it must be split further into linked Intercom Conversations.
- Unsupported channel types need normalization
- Gladly treats voice calls, SMS, and Instagram Direct as equal nodes on a timeline. Intercom's conversation creation API only supports inapp, email, sms, whatsapp, and facebook as message types (Intercom API: Create Conversation). Voice calls, voicemails, and Instagram Direct have no native equivalent and must be stored as internal notes.
- Topic hierarchies flatten
- Gladly topics can be nested. Intercom tags are flat. You must decide whether to flatten hierarchies or encode path strings (e.g., Billing > Refunds > Partial).
- Timeline item cap
- The conversation timeline endpoint returns at most 1,000 items per conversation. If exceeded, the Gladly-Limited-Data header flags the truncation. For enterprise customers with multi-year histories, this is a real risk.
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 -
Split-Conversation Audit
Query Intercom for all conversations tagged gladly_continuation: true. Verify each has a corresponding parent conversation with a matching gladly_original_conversation_id.
Gladly → Intercom specifics
- People Match rules
- Gladly's identity resolution logic (matching customers across channels by email, phone, social) has no API-portable equivalent. Intercom identifies contacts by external_id, email, or Intercom ID.
- Routing rules and SLA policies
- Must be rebuilt as Intercom Workflows and SLA rules manually. Budget 1–2 days depending on rule complexity.
- Voice/SMS channel metadata
- Intercom has no native voice channel. Call recordings, durations, and IVR paths are preserved only as note text or attachment URLs.
- Sidekick (AI) configuration
- Guides, Journeys, and AI training data cannot be exported or transferred.
- Conversation status granularity
- Gladly statuses (OPEN, CLOSED, WAITING) must be mapped to Intercom's three states: open, closed, or snoozed. Map WAITING to snoozed with a calculated timestamp.
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 → Intercom 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 → Intercom 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 Intercom 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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Check for truncation
If Gladly-Limited-Data was flagged during extraction, verify you have the complete dataset from the Export API.
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Split any conversation with >450 items
into multiple Intercom Conversations. Tag overflow conversations with a shared gladly_original_conversation_id custom attribute and a gladly_continuation: true flag.
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Normalize unsupported channels
Map voice calls, voicemails, and Instagram Direct items to internal notes with explicit channel markers.
Gladly → Intercom specifics
- Timestamps
- Gladly exports ISO 8601. Intercom's API expects Unix timestamps (seconds since epoch) for historical imports. Convert during the transform phase: int(datetime.fromisoformat(gladly_ts).timestamp()).
- Custom Attributes
- Pre-create Intercom CDAs with the correct type — string, integer, float, boolean, date, or options — before loading contact data.
- Identity Strategy
- Intercom's CSV imports match contacts in this order: Intercom ID → user_id → email. If you mix email and user_id inconsistently, you will create duplicates. Intercom also supports CSV bulk imports for contacts as an alternative to API-only loading — useful for the initial contact seed if you have clean, deduplicated customer data.
- Group items by Gladly Conversation ID
- Each Gladly Conversation maps to one Intercom Conversation.
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 Intercom sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Intercom 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 Intercom'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 Intercom, 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 Intercom'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.
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 Intercom 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 Intercom, 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.
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 Object
| Gladly field | Intercom field | Notes |
|---|---|---|
| Customer | Contact (User or Lead) | Gladly merges profiles automatically. Intercom deduplicates on external_id or email (Intercom: Contacts). Role (user vs lead) must be assigned during import. Gladly allows multiple emails/phones per customer. |
| Conversation | Conversation | Gladly Conversations exist within a customer timeline. Each maps to a discrete Intercom Conversation. |
| Conversation Item (Email) | Conversation Part (comment) | Each email becomes a conversation part. HTML is not supported in the initial conversation body — only in reply parts (verified against Intercom API v2.11; check the Create Conversation endpoint docs for current behavior). |
| Conversation Item (Chat) | Conversation Part (comment) | Direct mapping. |
| Conversation Item (SMS) | Conversation Part (comment) | SMS content migrates as text. Channel metadata (carrier, delivery status) is lost. |
| Conversation Item (Phone Call) | Conversation Part (note) | No native equivalent. Migrate as internal notes with call metadata (duration, recording URL). |
| Conversation Item (Voicemail) | Conversation Part (note) | Same as phone calls — recording URLs preserved as attachment links in S3/GCS. |
| Conversation Item (Instagram Direct) | Conversation Part (note) | Intercom does not support Instagram Direct as a create-conversation message type. Store as notes. |
| Conversation Item (Note) | Conversation Part (note) | Direct mapping for internal notes. |
| Topic | Tag | Gladly Topics map to Intercom Tags. Create tags before importing conversations. Flatten nested hierarchies. |
| Task | Ticket | Gladly Tasks (due date, assignee, description) map best to Intercom Tickets with custom ticket types. |
| Answer (Public) | Article | Gladly Answers map to Intercom Articles. Sections map to Collections. Audiences map to Help Center audiences. |
| Agent | Teammate / Admin | Agent profiles must exist in Intercom before conversation import for proper attribution. Map by email address. |
| Team | Team | Direct mapping. Create teams in Intercom before import. |
| Inbox | Team Inbox | Map Gladly Inboxes to Intercom team inboxes or views. |
| Custom Attributes | Custom Data Attributes | Create CDAs via the Data Attributes API before importing records. |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Contacts | medium | Gladly allows multiple emails and phones per customer and auto-merges profiles, while Intercom deduplicates on external_id or email, requiring careful identity strategy to avoid creating duplicate contacts. |
| Conversations | high | Gladly's lifelong timelines must be split into discrete Intercom Conversations with a 500-part limit, and the API silently truncates conversation lists at 100 per customer, risking undetected history loss. |
| Conversation Items (Chat/Email) | medium | Chat and email items map to conversation parts, but HTML is not supported in the initial conversation body and timeline items are silently capped at 1,000 per conversation. |
| Conversation Items (Voice/Voicemail) | high | Intercom has no native voice channel, so call recordings, durations, and IVR paths can only be preserved as internal note text or attachment URLs with significant metadata loss. |
| Topics / Tags | medium | Gladly's nested topic hierarchies must be flattened into Intercom's flat tag structure, losing organizational depth unless path strings are encoded into tag names. |
| Tasks / Tickets | medium | Gladly Tasks with due dates and assignees map to Intercom Tickets, but require pre-configured custom ticket types to preserve structured lifecycle attributes. |
| Answers / Articles | low | Gladly Answers map relatively cleanly to Intercom Articles, with sections mapping to Collections and audiences mapping to Help Center audiences. |
| Agents / Teammates | low | Agent profiles map to Intercom Teammates by email address, but all agents must exist in Intercom before conversation import to ensure proper attribution. |
| Custom Attributes | medium | Intercom Custom Data Attributes must be pre-created with exact type definitions via the Data Attributes API before any contact data can be loaded, adding sequencing complexity. |
| Routing Rules / SLA Policies | high | Gladly routing rules, SLA configurations, and People Match identity resolution logic have no API-exportable equivalent and must be fully reconstructed manually in Intercom Workflows. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Continuous Timelines vs. Discrete Conversations
Gladly's single lifelong customer timeline must be decomposed into multiple discrete Intercom Conversations, and any Gladly Conversation exceeding 500 items must be further split into linked Intercom Conversations.
Silent Data Truncation Risk
Gladly's API silently caps conversation lists at 100 per customer and timeline items at 1,000 per conversation, signaled only by a response header that extraction scripts must explicitly check to avoid undetected data loss.
Unsupported Channel Type Normalization
Voice calls, voicemails, and Instagram Direct messages have no native Intercom conversation type and must be stored as internal notes with metadata such as call duration and recording URLs.
Topic Hierarchy Flattening
Gladly's nested topic structures must be flattened into Intercom's flat tag model, requiring decisions on whether to encode hierarchy paths as concatenated tag strings or discard depth.
Rate Limit Throughput Bottleneck
Gladly's REST API is limited to 10 requests per second with non-paginated, truncated responses on key endpoints, making it the primary throughput constraint for large-scale extractions.
Automation and Rules Rebuild
Gladly routing rules, SLA policies, People Match identity resolution logic, and Sidekick AI configurations have no API-portable equivalents and must be manually recreated as Intercom Workflows and SLA rules.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
How long does a Gladly to Intercom migration take?
A Gladly to Intercom migration takes 2–4 weeks for a typical mid-market team with 50,000–150,000 conversation items. This includes planning, schema mapping, test migration, full migration, delta sync, and post-go-live validation. Enterprise migrations with voice history and 500,000+ items can take 4–6 weeks.
What is the biggest risk in a Gladly to Intercom migration?
Silent data truncation. Gladly's API returns at most 100 conversations per customer and 1,000 items per conversation. If your extraction script does not check the Gladly-Limited-Data response header, you will lose customer history without any error message. This is the most common cause of post-migration data loss.
What data cannot be migrated from Gladly to Intercom?
Gladly automations, routing rules, People Match configuration, Sidekick AI training data, SLA policies, and real-time analytics cannot be migrated and must be rebuilt manually in Intercom. Voice call recordings can be preserved as linked URLs in internal notes but not as native Intercom voice interactions.
Can Gladly conversation history be preserved in Intercom?
Yes, but it requires transformation. Gladly's continuous customer timelines must be split into discrete Intercom Conversations, each with a maximum of 500 parts. Intercom's Create Conversation endpoint accepts a created_at timestamp for historical imports. Voice and SMS items migrate as internal notes since Intercom has no native voice channel.
How much does a Gladly to Intercom migration cost?
DIY API scripting costs 100–160 engineer-hours ($15,000–$30,000 at loaded engineering rates). Third-party migration tools typically charge $2,000–$8,000 for 50K–150K records. Managed migration services range from $5,000–$20,000 depending on volume, complexity, and whether voice/SMS history is included.