There's no native migration path from LiveChat to SurveySparrow. Every migration requires API extraction, data model translation from threaded chats to flat tickets, and careful field mapping.
There is no native migration path between LiveChat and SurveySparrow Ticket Management — no built-in importer exists on either side, and no third-party tool currently offers a verified connector for this direction. The fundamental challenge lies in translating LiveChat's conversation-centric data model (chats containing threaded sessions with message-level events) into SurveySparrow's flat ticket structure (subject, description, priority, status, and comments). Every migration requires custom work: extracting data via LiveChat's Agent Chat API (v3.5), transforming threaded transcripts into a compatible ticket-and-comment schema, and loading records through SurveySparrow's REST API (v3).
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
No native importer exists
Neither LiveChat nor SurveySparrow offers a built-in migration tool for this direction. Plan for an API-based or CSV-intermediary migration from day one.
Do not create one SurveySparrow ticket per list_archives row
LiveChat's archive list is thread-based, so the same chat can appear multiple times — once per thread. Deduplicate by chat.id, or intentionally split one chat into multiple target tickets. (platform.text.com)
When this migration doesn't make sense
If your team relies on real-time chat, chat routing rules, chatbot flows, or handles more than 1,000 concurrent chat sessions, SurveySparrow Ticket Management is not a drop-in replacement. It's designed for feedback-driven case management, not live chat.
Data that doesn't live here
Accounts, Leads, Opportunities, and generic custom objects are not native first-class objects in SurveySparrow's public APIs. If you need them, they usually live in your CRM. Decide whether SurveySparrow gets a shadow copy or just foreign keys. (developers.surveysparrow.com)
SurveySparrow Ticket Management has no native tag system for tickets
If your LiveChat workflow relies heavily on tags for routing and reporting, create a multiselect custom ticket field to preserve this data. Plan the field structure — including all valid option values — before migration.
Subject line rule
SurveySparrow ticket subjects are capped at 200 characters. Build subjects from the first customer message (truncated) or a structured format like "LiveChat #{chat_id_short} — {first_20_words}". Push the full opening context into description. (developers.surveysparrow.com)
Map by internal_name, not labels
SurveySparrow ticket fields expose an internal_name that stays the same even if the display label changes. Use it for field mapping so future label changes don't break your migration logic. (developers.surveysparrow.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 LiveChat
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 Surveysparrow Ticket Management can hold your support model
Walk your current workflow through Surveysparrow Ticket Management: 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 LiveChat → Surveysparrow Ticket Management 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.
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Record count comparison
Total chats extracted (deduplicated by chat.id) vs. tickets created. Compare against LiveChat's Reports → Chats total as the control number.
Helpdesk Evaluator Sanity-check that Surveysparrow Ticket Management is the right target before you commit
LiveChat → Surveysparrow Ticket Management specifics
- Unified feedback and support
- SurveySparrow ties customer complaints directly to survey data — NPS scores, CSAT ratings — in one platform instead of correlating across two separate tools. Teams can auto-create tickets from low NPS scores (e.g., detractor responses ≤ 6) and route them to the same agents handling existing cases.
- Workflow consolidation
- Organizations already using SurveySparrow for surveys want tickets auto-created from low scores rather than maintaining a separate LiveChat instance with its own agent roster and routing rules.
- Cost reduction
- Running a dedicated chat platform alongside a survey tool creates overlapping per-agent costs. SurveySparrow's ticket management is included in Business and Enterprise plans, eliminating the separate LiveChat per-agent fee.
- Simpler support needs
- Teams that handle fewer than 500 conversations per month and prefer async ticket-based workflows find SurveySparrow's lighter model sufficient.
- Small business (<5,000 chats)
- API-based migration. Use CSV only if archive-quality data is acceptable.
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 LiveChat 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 LiveChat 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 LiveChat 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 LiveChat → Surveysparrow Ticket Management 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 LiveChat → Surveysparrow Ticket Management 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 Surveysparrow Ticket Management 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.
LiveChat → Surveysparrow Ticket Management specifics
- Custom field values
- Check 10+ records per custom field for correct data type and value.
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 Surveysparrow Ticket Management sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Surveysparrow Ticket Management 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 Surveysparrow Ticket Management'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.
LiveChat → Surveysparrow Ticket Management specifics
- Attachment spot-check
- Confirm linked files are accessible and not returning 404s or authentication errors.
- Long transcript test
- Identify the 10 longest chat transcripts and verify they migrated completely without truncation.
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 Surveysparrow Ticket Management, 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 LiveChat 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 Surveysparrow Ticket Management'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 LiveChat 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 LiveChat read-only rather than cancelled — cancelling the old contract on day one removes your only fallback.
LiveChat → Surveysparrow Ticket Management specifics
- Big bang
- Migrate everything in one cutover window. Fastest, but highest risk. Suitable when total migration execution time is under 8 hours.
- Incremental
- Migrate historical data first, then sync new data via middleware until cutover. Best for large datasets with ongoing operations that can't pause.
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 LiveChat and Surveysparrow Ticket Management 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 Surveysparrow Ticket Management, 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 LiveChat 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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Contact count
Unique customers with email addresses vs. contacts created in SurveySparrow.
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Record count reconciliation
Compare source chat count (from LiveChat API, deduplicated by chat.id) against created ticket count. Use LiveChat's Reports → Chats total as the control number, not just job logs. (support.surveysparrow.com)
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Rebuild automations
LiveChat triggers, routing rules, and chatbot flows don't migrate. Recreate relevant workflows in SurveySparrow's Ticket Management workflow builder (trigger on ticket creation, status change, priority change). Map each LiveChat automation to its SurveySparrow equivalent before cutover. (support.surveysparrow.com)
LiveChat → Surveysparrow Ticket Management specifics
- Sampling
- Spot-check 5–10% of tickets for content accuracy — verify the description matches the first customer message and comments are in chronological order.
- Relationship integrity
- Verify tickets are linked to correct contacts by cross-referencing livechat_id custom field with source data.
- Migration log analysis
- Count successes, failures by error type, and any records that need manual remediation.
- Field-level validation
- For each custom field, verify 10+ records for correct data type and value. Pay special attention to multiselect fields (tags) and date fields (timezone).
- Agent onboarding
- SurveySparrow's ticket interface differs significantly from LiveChat's chat console. Run a focused 30–60 minute training session covering: ticket views and filters, comment workflows (internal vs. public), SLA tracking, and how to search for migrated historical data using the livechat_id custom field.
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 SurveySparrow Object
| LiveChat field | Surveysparrow Ticket Management field | Notes |
|---|---|---|
| Chat (with threads) | Ticket | One chat → one ticket. Thread content concatenated into description + comments |
| Customer | Contact | Map by email. LiveChat customer properties → Contact custom properties |
| Agent | User | Map by email. Agent groups have no direct equivalent |
| Group | (No equivalent) | Store as ticket custom field if needed |
| Tag | Ticket custom field | SurveySparrow has no native tag system on tickets — use a multiselect custom field |
| Pre-chat survey | Ticket custom fields | Map each form field to a dedicated ticket custom field |
| Post-chat rating | Ticket custom field | Numeric or picklist field |
| File attachment | Ticket comment attachment | Host externally and link, or attach if API supports the file type (PDF, PNG, JPEG, MP3, CSV, WAV; max 15 MB) |
| Chat properties | Ticket custom fields | Key-value pairs mapped to individual fields |
Customer SurveySparrow Contact
Both platforms support standard contact fields like email, name, and phone, and SurveySparrow offers CSV contact import, making this a straightforward mapping.
| LiveChat field | Surveysparrow Ticket Management field | Notes |
|---|---|---|
| Primary identifier on both platforms. Required for contact creation in SurveySparrow. | ||
| name | full_name | Direct mapping |
| phone | phone | Direct mapping if collected via pre-chat form |
| custom_variables.* | Contact Properties | Create custom contact properties in SurveySparrow first via Settings → Contact Properties |
| last_visit | (No equivalent) | Store in a custom property or drop |
| statistics.chats_count | (No equivalent) | Drop or store as custom property |
| avatar | (No equivalent) | Not supported as a contact field |
Chats Tickets
LiveChat's threaded chat model has no direct equivalent in SurveySparrow, requiring complex flattening logic and early decisions on whether to map per-chat, per-thread, or per-issue.
| LiveChat field | Surveysparrow Ticket Management field | Notes |
|---|---|---|
| id | Custom field (livechat_id) | Store as reference for validation and rollback |
| Thread events (messages) | description + Ticket Comments | First customer message → description; subsequent messages → comments in chronological order |
| tags | Custom field (multiselect) | Create ticket custom field with all tag values as options |
| agents [].name | assignee | Map agent email → SurveySparrow user ID via lookup table |
| created_at | created_at | ISO 8601 format. LiveChat uses 2024-01-15T10:30:00.000Z; SurveySparrow accepts the same format. |
| threads [].active | status | Map: active → Open; inactive/closed → Resolved |
| CSAT rating | Custom field | Create numeric custom ticket field (1–5 scale) |
| properties.routing.group_id | Custom field or team_id | Build a group_id → team_id lookup table first |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Tickets (Chats) | high | LiveChat's threaded chat model has no direct equivalent in SurveySparrow, requiring complex flattening logic and early decisions on whether to map per-chat, per-thread, or per-issue. |
| Chat Transcripts (Messages) | high | Multi-message conversation threads must be serialized into a single ticket description or appended as sequential comments, risking loss of conversational context and message attribution. |
| Contacts (Customers) | low | Both platforms support standard contact fields like email, name, and phone, and SurveySparrow offers CSV contact import, making this a straightforward mapping. |
| Agents (Users) | medium | LiveChat agents organized into groups must be manually mapped to SurveySparrow users, with no automated way to preserve group-based routing rules or permissions. |
| Tags | medium | LiveChat's chat-level tags have no documented direct equivalent in SurveySparrow's ticket schema, requiring mapping to custom ticket fields or inclusion in the ticket description. |
| Custom Fields | medium | LiveChat customer custom properties must be mapped to SurveySparrow's custom ticket fields or contact fields, with potential type mismatches requiring manual field creation. |
| Attachments (Files) | high | File events embedded in LiveChat threads require individual download, re-hosting, and re-linking, with high risk of silent loss if not explicitly handled in the migration pipeline. |
| Ticket Status and Priority | low | SurveySparrow supports standard status and priority fields, so mapping from LiveChat's chat states is straightforward with a simple lookup table. |
| Timestamps and Audit Trail | medium | Original chat creation and message timestamps may not be preservable as ticket creation dates in SurveySparrow, potentially breaking historical reporting and SLA audit trails. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Threaded Chat Flattening
LiveChat's multi-thread, multi-event chat structure must be collapsed into SurveySparrow's single ticket description plus linear comments, with no native 1:1 mapping for chat transcripts.
Chat-to-Ticket Deduplication
LiveChat's archive list is thread-based, meaning the same chat can appear multiple times, requiring deduplication by chat ID to avoid creating duplicate tickets in SurveySparrow.
No Native Migration Path
Neither platform provides a built-in importer or exporter for this direction, and no third-party tool has a verified SurveySparrow Ticket Management destination connector as of mid-2025.
API Rate Limit Management
Both platforms enforce API rate limits, and LiveChat's list_archives endpoint defaults to 10 records per page with a maximum of 100, requiring careful pagination and throttling for large datasets.
Attachment and File Loss
LiveChat stores file events (images, documents) as part of chat threads, but migrating these attachments into SurveySparrow tickets requires downloading, re-hosting, and re-linking each file individually.
Agent and Group Mapping
LiveChat organizes agents into groups with routing rules, while SurveySparrow uses a simpler user-assignee model, requiring manual mapping of agent identities and team structures.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can I directly import LiveChat data into SurveySparrow Ticket Management?
No. Neither platform offers a native import/export path for this direction. You need to extract data via LiveChat's Agent Chat API (v3.5), transform it to match SurveySparrow's ticket schema, and load it through SurveySparrow's REST API (v3). No third-party tool currently offers a verified pre-built connector for this migration either.
What happens to LiveChat chat transcripts during migration?
LiveChat stores conversations as threaded chats with multiple message events. SurveySparrow tickets use a flat structure — a subject, description, and comments. You need to flatten chat threads: typically the first message becomes the ticket description, and subsequent messages become ticket comments. Multi-thread chats require a decision on whether to consolidate or split into separate tickets.
What is the biggest data-loss risk in this migration?
The most common failure is duplicate or broken ticket history from treating LiveChat archive rows as unique chats. The list_archives endpoint is thread-based, so the same chat can appear multiple times. You must deduplicate by chat.id to avoid creating duplicate tickets in SurveySparrow.
What are the API rate limits for LiveChat and SurveySparrow?
LiveChat enforces 1,000 requests per 10-minute window per license, shared across all tokens and integrations. SurveySparrow's rate limits vary by plan tier and are not publicly documented with exact numbers — start with conservative throttling around 60 requests per minute and adjust based on response headers.
Does SurveySparrow Ticket Management support tags like LiveChat?
No. SurveySparrow Ticket Management does not have a native tag system on tickets. If your LiveChat workflow relies on tags for categorization or routing, you need to create custom ticket fields in SurveySparrow to store tag data. Plan this field structure before migration.