No native migration path exists. Extract SurveySparrow tickets via API, translate flat tickets into Dixa conversations, create end users before importing, and separate private comments into internal notes.
There is no native migration path between SurveySparrow Ticket Management and Dixa, and no third-party tool currently offers a verified connector for this direction. SurveySparrow uses a flat, survey-driven ticket schema with threaded comments, while Dixa organizes all support interactions as conversation-centric records with typed messages, end users, and offer-based queue routing — requiring every ticket to be decomposed and reconstructed. All migrations require custom work: extracting data via SurveySparrow's REST API (v3) or native export, transforming tickets into Dixa's conversation model with pre-created end users, and loading via Dixa's Conversation Import API.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
No native importer exists
Neither SurveySparrow nor Dixa offers a built-in migration tool for this direction. SurveySparrow supports ticket export in Excel and JSON format via Settings → Ticket Management → Export Data, and CSV export from the ticket list view. Dixa's Import Conversation endpoint (POST /v1/conversations/import) accepts historical conversations with preserved timestamps but requires end users to exist before import. Plan for a custom API-based migration from day one. (support.surveysparrow.com)
Rate limit math
At 10 req/s, importing 10,000 conversations (each requiring ~4 API calls: create end user, import conversation, add notes, patch attributes) takes roughly 67 minutes of pure API time — before accounting for backoff, retries, and validation queries. With realistic 429 responses and backoff, budget 2–3 hours. For 50,000 conversations, budget a full working day of API time spread across multiple tokens.
Keep Dixa load workers conservative at first
Historical import is one conversation per API call, so a smaller pool (2–3 concurrent workers) with backoff and idempotency keyed to SurveySparrow ticket ID is easier to make trustworthy than chasing peak throughput on day one. Increase concurrency only after validating your first 500 imports. (docs.dixa.io)
The failures that hurt most are usually silent
a private comment loaded as a public message (exposing internal discussion to the customer), a custom status mapped to the wrong Dixa state, a parent/child ticket link dropped, or survey source metadata flattened into nothing. (support.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 Surveysparrow Ticket Management
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 Dixa can hold your support model
Walk your current workflow through Dixa: 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 Surveysparrow Ticket Management → Dixa 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.
Surveysparrow Ticket Management → Dixa specifics
- Ongoing sync after migration
- Webhook-based forwarding via middleware or custom integration
- Sample comparison
- Pull 5–10% of records (minimum 50) for field-level comparison
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 Surveysparrow Ticket Management 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 Surveysparrow Ticket Management 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 Surveysparrow Ticket Management 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 Surveysparrow Ticket Management → Dixa 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 Surveysparrow Ticket Management → Dixa 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 Dixa 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.
Surveysparrow Ticket Management → Dixa specifics
- Multi-select dropdowns
- in SurveySparrow have no direct Dixa equivalent (Dixa's Select is single-value). Flatten to comma-separated text or split into multiple boolean attributes.
- Checkbox fields
- convert to boolean text or a Select with Yes/No options.
- Nested ticket fields
- SurveySparrow allows nested custom fields (e.g., Category → Subcategory) that need flattening before import. Store as category_subcategory or two separate attributes. (support.surveysparrow.com)
- Survey data
- (NPS scores, CSAT ratings, specific feedback text) should be mapped to Dixa custom attributes on the Conversation or User object. Alternatively, concatenate survey responses into an internal note at the beginning of the Dixa Conversation to provide context to agents. Complex matrix survey answers will look messy as raw text — format them into clean markdown tables for readability.
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 Dixa sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Dixa 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 Dixa'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 Dixa, 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 Surveysparrow Ticket Management 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 Dixa'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 Surveysparrow Ticket Management 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 Surveysparrow Ticket Management read-only rather than cancelled — cancelling the old contract on day one removes your only fallback.
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Preserve your backup
Keep a full JSON export of SurveySparrow data stored independently (S3, GCS, or local). You'll need it for validation, re-runs, and as a fallback read-only reference.
Surveysparrow Ticket Management → Dixa specifics
- Before cutover
- Delete imported conversations individually via DELETE /v1/conversations/{id} — at 10 req/s, deleting 10,000 conversations takes ~17 minutes. Script this as part of your migration tooling.
- Best practice
- Run the full migration in a Dixa test environment first. Only proceed to production after UAT passes.
- Point of no return
- Once agents start working on migrated conversations in production (adding replies, changing states), rollback becomes impractical. Define a clear go/no-go decision point with stakeholders before cutover.
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 Surveysparrow Ticket Management and Dixa 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 Dixa, 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 Surveysparrow Ticket Management 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 counts
Total ticket count in SurveySparrow vs. total conversation count in Dixa (query via GET /v1/conversations with filters)
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Note integrity
Verify private comment counts match internal note counts per conversation
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Rebuild routing
Configure Dixa queues, skills, and Flow Builder rules to replace SurveySparrow's team-based assignment and workflow triggers. These cannot be migrated — they must be rebuilt from scratch. Map each SurveySparrow team to a Dixa queue, then configure skill-based routing rules within each queue.
Surveysparrow Ticket Management → Dixa specifics
- Field validation rules
- don't transfer. Rebuild in Dixa's custom attribute configuration.
- Message integrity
- Verify public comment counts match message counts per conversation
- End user completeness
- Confirm all unique requester emails in SurveySparrow have corresponding Dixa end users
- Attachment spot-check
- Open 10–20 conversations with attachments in Dixa's UI and verify files are downloadable
- Private comment isolation
- Confirm private comments were loaded as internal notes, not visible as 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.
SurveySparrow Object Equivalent
| Surveysparrow Ticket Management field | Dixa field | Notes |
|---|---|---|
| Ticket | Conversation | One ticket = one conversation with message thread |
| Contact (Requester) | End User | Must be created before conversation import; match by email |
| Agent | Agent | Must exist in Dixa; map by email to Dixa agent UUID |
| Team | Team / Queue | Teams map to Dixa teams; routing requires queue setup |
| Ticket Comment (public) | Message (Inbound/Outbound) | Each public comment becomes a typed message |
| Ticket Comment (private) | Internal Note | Use Dixa's separate notes endpoint |
| Custom Ticket Field | Custom Attribute (Conversation) | Must create attribute definitions in Dixa first |
| Priority | Tag or Custom Attribute | Dixa has no native priority field |
| Status | Conversation State | Open→Open, Pending→Open, Resolved→Closed, Closed→Closed |
| Tag | Tag | Direct mapping |
| SLA fields | SLA Policy (rebuilt) | Historical SLA data is metadata only; rebuild policies in Dixa |
| Parent/Child Ticket | Custom attribute or backlink note | No direct equivalent in Dixa |
| Attachment | Message Attachment | Must re-host to publicly accessible URL |
SurveySparrow Dixa
| Surveysparrow Ticket Management field | Dixa field | Notes |
|---|---|---|
| ticket.id | Conversation.custom_attribute.legacy_id | Store as reference; Dixa generates its own UUID |
| ticket.subject | Conversation.subject (email channel) | Direct map |
| ticket.description | First inbound message content.value | Becomes the first message in the conversation |
| ticket.description_html | First message (Text type) | Strip unsupported HTML or convert to plain text |
| ticket.status | Conversation.state | Open→Open, Pending→Open, Resolved→Closed, Closed→Closed |
| ticket.priority | Tag or custom attribute | Map Low/Medium/High/Urgent to tags (e.g., priority:high) |
| ticket.created_at | Conversation.createdAt | ISO 8601 format required |
| ticket.updated_at | — | No direct equivalent; use latest message timestamp |
| contact.email | End User contact endpoint | Primary key for user matching |
| agent.email | agentId on assignment | Look up agent UUID by email in Dixa |
| team.name | Team or Queue | Map to Dixa team/queue by name |
| comment.body | Message.content | Retain thread order using timestamps |
| comment.private | — | Private→Internal Note, Public→Message |
| custom_fields | Conversation custom attributes | Create definitions first, then PATCH |
| first_response_due | — | Metadata only; rebuild SLA policy in Dixa |
| resolution_due | — | Metadata only |
Dataset Size Extraction Time
| Surveysparrow Ticket Management field | Dixa field | Notes |
|---|---|---|
| <500 tickets | 1–2 hours | 2–4 hours |
| 500–5,000 tickets | 4–8 hours | 1–2 days |
| 5,000–50,000 tickets | 1–2 days | 3–5 days |
| 50,000+ tickets | 2–5 days | 1–2 weeks |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Tickets / Conversations | high | Every ticket must be restructured into Dixa's conversation model with typed messages and linked end users, and the fundamental schema mismatch makes automated 1:1 mapping impossible without custom transformation logic. |
| Contacts / End Users | medium | SurveySparrow contacts map to Dixa end users but require deduplication (e.g., email casing differences) and must be created before conversations are imported due to Dixa's API sequencing requirement. |
| Comments / Messages | high | SurveySparrow's flat comment thread must be converted into Dixa's ordered, typed message sequence with correct inbound/outbound direction, and CSV/Excel exports often omit per-comment metadata like author and private/public flags. |
| Custom Fields / Custom Attributes | high | SurveySparrow supports nested custom ticket fields that must be mapped to Dixa's flat custom attributes on conversations or end users, with no guaranteed type equivalence for all field types. |
| Ticket Status | medium | SurveySparrow's multi-state status model (Open, Pending, Resolved, Closed, plus custom values) must be collapsed into Dixa's binary Open/Closed model, losing granularity unless preserved via tags or custom attributes. |
| Priority | medium | Dixa has no native priority field, so SurveySparrow's Low/Medium/High/Urgent priority values must be stored as custom attributes or tags, losing any built-in sorting or routing behavior. |
| Attachments | high | Attachments must be re-hosted at publicly accessible URLs for Dixa to download during import, and SurveySparrow's attachment URLs may expire or require authentication, risking data loss if not handled proactively. |
| Teams / Queues | medium | SurveySparrow's team-based assignment must be mapped to Dixa's queue-based routing model, which uses fundamentally different offer-based logic rather than static team membership. |
| Agent Assignment | low | Agent identities can generally be mapped between systems by email, though Dixa's offer-based routing means historical assignments are recorded but do not influence future routing behavior. |
| Timestamps | low | Dixa's Conversation Import API supports preserved timestamps on historical conversations, allowing original creation and update times to be maintained if extracted correctly from SurveySparrow's API. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Ticket-to-Conversation Model Mismatch
SurveySparrow's flat ticket-and-comment structure must be decomposed and reconstructed into Dixa's typed message sequence within a conversation object, requiring careful mapping of comment direction, authorship, and timestamps.
End User Pre-Creation Dependency
Dixa's Conversation Import API requires all referenced end users to exist before conversations can be imported, forcing a strict sequencing of contact migration before any ticket data can be loaded.
Status and Priority Model Gaps
SurveySparrow supports four-plus statuses (Open, Pending, Resolved, Closed, plus custom) and four priority levels, while Dixa only has Open/Closed statuses and no native priority field, requiring lossy mapping or custom attribute workarounds.
Attachment URL Accessibility
Dixa requires attachments to be hosted at publicly accessible URLs for download during import, but SurveySparrow attachment URLs may be session-scoped or time-limited, necessitating intermediate re-hosting.
Parent-Child Ticket Hierarchy Loss
SurveySparrow supports parent-child ticket relationships that have no native equivalent in Dixa, so hierarchical ticket structures must be flattened or encoded as tags/custom attributes.
No Native or Third-Party Migration Tooling
Neither platform provides a built-in importer for this direction and no iPaaS or migration tool offers a verified connector, meaning every migration must be custom-built via API scripting or managed services.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can I migrate tickets from SurveySparrow to Dixa without coding?
Not for historical data. SurveySparrow supports Excel/JSON/CSV export, but Dixa has no native import UI for historical conversations. You need scripted API calls to load data into Dixa via POST /v1/conversations/import. Middleware tools like Zapier can forward new tickets going forward but cannot bulk-import history.
What are Dixa's API rate limits for importing conversations?
Dixa enforces 10 requests per second per API token, with a burst allowance of 4 requests and a daily ceiling of 864,000 requests per token. There is no Retry-After header on 429 responses — implement exponential backoff with jitter in your migration script.
How do SurveySparrow ticket statuses map to Dixa?
SurveySparrow has four standard statuses (Open, Pending, Resolved, Closed) plus custom extensions. Dixa only has Open and Closed. Map Pending to Open and Resolved to Closed. Use tags or custom attributes in Dixa to preserve the original granular status for reporting.
How should I handle SurveySparrow private comments in Dixa?
Map public comments to Dixa conversation messages and private comments to Dixa internal notes. SurveySparrow exposes comment visibility with the private field, and Dixa has a dedicated notes endpoint (POST /v1/conversations/{id}/notes) separate from the conversation import.
How long does a SurveySparrow to Dixa migration take?
For under 500 tickets with simple fields, expect 1–2 days of scripting and testing. For 5,000–50,000 tickets with custom fields and attachments, plan for 1–3 weeks including test migrations and validation. A managed service like ClonePartner typically completes migrations in days.