Kustomer to Groove is a data model downshift — from a CRM-grade timeline to a flat shared inbox. No native migration path exists; plan for API extraction, KObject flattening, and delta sync.
Migrating from Kustomer to Groove involves a fundamental data model shift: Kustomer is a CRM-helpdesk hybrid where the customer record is the top-level object containing a continuous, chronological interaction timeline, while Groove is a flat, ticket-centric shared inbox where each conversation is a discrete, standalone object. There is no native migration path between the two platforms — the practical route is Kustomer API extraction, custom data transformation, and Groove API loading. The core engineering challenge lies in data mapping: Kustomer's customer-centric model, multi-channel conversation timelines, and custom KObjects have no direct equivalents in Groove's simpler schema of tickets, tags, and lightweight contacts. Custom work is required to flatten KObject data, resolve tag ID-to-name translations, handle multi-email customer deduplication, re-host attachments, and backdate timestamps via Groove's GraphQL v2 API.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
Agent Mapping Constraint
If a Kustomer user no longer exists in your organization (e.g., a former employee), you cannot assign a Groove ticket to them unless you provision a paid agent seat in Groove. The standard workaround is to map all inactive users to a single "Legacy Agent" account and prepend the original author's name to the message body.
Search API time limit
The Kustomer Search API (POST /v1/customers/search) only returns records updated within the past 2 years. For older records, use the Archive Search endpoint, which accepts the same query format but includes historical data.
Pre-migration setup is critical. Groove's own migration documentation warns
you must set up all your users in Groove before you migrate tickets. If migration is initiated before agents are invited, all tickets may end up assigned to a single user. Also create inboxes with matching names before starting.
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 Kustomer
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 Groove can hold your support model
Walk your current workflow through Groove: 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 Kustomer → Groove 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.
Kustomer → Groove specifics
- Simplicity
- Kustomer's CRM-grade data model (custom Klasses, KObjects, workflows engine, timeline views) is powerful but operationally heavy for teams that just need a shared inbox. Groove is purpose-built for small and mid-size teams that want email, live chat, and a knowledge base in one clean workspace.
- Reduced operational overhead
- Kustomer's implementation costs run $18,000–$30,000 with a 12–16 week lead time. Groove requires near-zero implementation effort.
- Team size
- Groove targets teams of 1–50 agents. Kustomer targets mid-market and enterprise with high-volume B2C support.
- Kustomer's customer-centric model → Groove's ticket-centric model
- In Kustomer, the customer is the top-level object and conversations are nested under it. In Groove, tickets are independent objects linked to a customer email. You lose the unified timeline view.
- KObjects have no Groove equivalent
- Any custom object data (orders, subscriptions, reservations) stored in KObjects cannot be natively represented in Groove. You must either flatten this data into ticket tags/custom fields, append it as notes, or archive it externally.
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 Kustomer 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 Kustomer 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 Kustomer 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
Kustomer → Groove specifics
- KObject Klass enumeration
- GET /v1/kobjects to retrieve all defined Klass names, then fetch schemas via GET /v1/kobjects/{klassName}
- Customers
- GET /v1/customers with cursor pagination
- Messages per conversation
- GET /v1/conversations/{id}/messages (page size max 100)
- KObjects per customer
- GET /v1/customers/{id}/kobjects/{klassName} for each Klass name retrieved in step 3
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 Kustomer → Groove 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 Kustomer → Groove 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 Groove has no equivalent, decide between a new custom field, a tag, or a note appended to the ticket body — and record which.
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Resolve identity and threading
Decide how source IDs are preserved — most platforms will not let you set the primary key, so keep the original ID in a custom field. Without it, reconciliation becomes fuzzy matching and every future support question about an old ticket is unanswerable.
Losing the original ticket ID makes reconciliation and rollback effectively impossible.
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Plan attachments, inline images and threading order
Confirm size limits, allowed MIME types, and whether inline images survive as attachments or need rehosting. Decide the comment ordering and author attribution rules: comments loaded out of order, or all attributed to the API user, destroy the conversation history agents rely on.
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Freeze and sign off the mapping spec
Version the spec, walk the support lead through it row by row, and get explicit sign-off. Any change after this point goes through change control — mid-flight mapping edits are how partial loads happen.
Don't move on until
- Every source field is mapped, deliberately dropped, or parked in a custom field
- Status, priority and channel value maps agreed with the support lead
- Mapping spec version-controlled and signed off
04 Test Migration Prove the pipeline on a small, representative slice.
Objective A pilot load into a Groove sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Groove 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 Groove'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 Groove, 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 Kustomer 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 Groove'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 Kustomer 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 Kustomer read-only rather than cancelled — cancelling the old contract on day one removes your only fallback.
Kustomer → Groove specifics
- Initial Sync
- Extract all historical data up to a specific timestamp (e.g., Friday at 11:59 PM). Load into Groove. This takes the bulk of processing time.
- Delta Sync
- Run the script again, modifying the Kustomer extraction query to only fetch conversations where updatedAt > [Initial Sync Timestamp]. This captures tickets created, replied to, or closed during the initial sync.
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 Kustomer and Groove 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 Groove, 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 Kustomer 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 deduplication
Are there duplicate contact records in Groove for the same customer email?
Kustomer → Groove specifics
- Count validation
- Does the total number of Kustomer conversations match the total number of Groove tickets?
- State validation
- Are Kustomer "Done" conversations correctly marked as "Closed" in Groove?
- Thread integrity
- Did multi-message threads arrive in the correct chronological order?
- Attachment accessibility
- Can you open attachments on Groove tickets from older conversations? Do inline images render?
- Visibility checks
- Are flattened KObjects strictly marked as Private Notes so internal data isn't exposed to customers?
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.
Cus mers
Groove deduplicates contacts by email address only, so Kustomer customers with multiple emails or duplicate records require a pre-load deduplication and merge pass to avoid fragmented contact histories.
| Kustomer field | Groove field | Notes |
|---|---|---|
| Primary identifier in Groove | ||
| name / displayName | first_name, last_name | Split required |
| phones | Not directly supported | Append to notes or custom field |
| company | Not supported | Flatten to tag or note |
| custom attributes | Not supported | Selective flattening required |
| createdAt | created_at | Groove GraphQL v2 accepts this on contact creation |
Conversations Tickets
Kustomer conversations are nested under customer records and may span multiple channels, requiring significant transformation to produce discrete, properly attributed Groove tickets with correct status mapping.
| Kustomer field | Groove field | Notes |
|---|---|---|
| conversation.id | Reference only | Store as tag or external ID for audit trail |
| conversation.name / subject | ticket.title | Groove titles are optional |
| conversation.status | ticket.state | Map: open→opened, done→closed, snoozed→pending |
| conversation.priority | ticket.priority | Groove supports low, normal, high, urgent |
| conversation.assignedUsers | ticket.assignee | Must pre-create agents in Groove |
| conversation.assignedTeams | ticket.assigned_group | Must pre-create groups in Groove |
| conversation.tags | ticket.tags | Kustomer stores tag IDs; resolve to names before loading |
| conversation.channels | Inbox routing | Map channel to Groove inbox |
| conversation.createdAt | ticket.created_at | Groove GraphQL v2 accepts backdating; see Timestamp section |
Messages Messages
Message direction, authorship context, HTML content sanitization, and timestamp backdating via Groove GraphQL v2 all require explicit mapping logic, with internal notes needing to be separated from public replies.
| Kustomer field | Groove field | Notes |
|---|---|---|
| message.body | message.body | HTML content; check Groove's HTML sanitization |
| message.direction (in/out) | message.author context | Inbound = customer, outbound = agent |
| message.sentAt | Timestamp | Groove GraphQL v2 accepts timestamp on message creation |
| message.attachments | message.attachments | Download from Kustomer CDN, re-upload to Groove |
| Internal notes | message.note | Groove supports internal notes on tickets |
Kustomer Target
| Kustomer field | Groove field | Notes |
|---|---|---|
| article.title | article.title | Direct map |
| article.body | article.body | HTML; check for inline image URL rewrites |
| article.category | article.category | Pre-create categories in Groove |
| article.status | article.status | Map published/draft states |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Tickets (Conversations) | high | Kustomer conversations are nested under customer records and may span multiple channels, requiring significant transformation to produce discrete, properly attributed Groove tickets with correct status mapping. |
| Contacts (Customers) | medium | Groove deduplicates contacts by email address only, so Kustomer customers with multiple emails or duplicate records require a pre-load deduplication and merge pass to avoid fragmented contact histories. |
| Messages | medium | Message direction, authorship context, HTML content sanitization, and timestamp backdating via Groove GraphQL v2 all require explicit mapping logic, with internal notes needing to be separated from public replies. |
| Attachments | high | Attachments must be downloaded from Kustomer's CDN and re-uploaded to Groove during the load phase, creating a dependency on external CDN availability and significantly increasing migration time and failure surface. |
| KObjects (Custom Objects) | high | Groove has no native equivalent for Kustomer's custom KObject schemas, meaning all structured custom object data must be flattened, appended as notes, or archived externally with permanent loss of queryable structure. |
| Custom Fields | medium | Kustomer customer-level custom attributes have no direct Groove equivalent and require selective flattening decisions, with unsupported fields either discarded or appended as unstructured note content. |
| Tags | medium | Kustomer stores tags as internal IDs that must be resolved to human-readable names via the tags API before load, and a mapping rule must determine which tags become Groove Folders versus Groove Tags. |
| Companies | high | Groove has no native company object, so Kustomer's first-class company records and their customer associations are permanently lost unless flattened into a tag or appended as a note on each contact or ticket. |
| Agents and Groups | medium | Inactive Kustomer agents cannot be assigned in Groove without paid seat provisioning, requiring a remapping strategy to a legacy placeholder agent with original author names prepended to affected message bodies. |
| Knowledge Base Articles | low | KB articles map relatively cleanly between platforms on title, body, category, and status, though inline image URLs within article bodies must be rewritten to point to Groove-hosted assets after migration. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Customer-Centric to Ticket-Centric Restructuring
Kustomer's top-level customer object with nested conversation timelines must be restructured so that each conversation becomes an independent Groove ticket linked only by a customer email address, eliminating the unified timeline view.
KObjects Have No Groove Equivalent
Custom KObject data (orders, subscriptions, reservations) built on arbitrary schemas in Kustomer cannot be natively represented in Groove and must be flattened into tags, custom fields, or appended as internal notes, or archived externally.
Multi-Channel Conversation Splitting
A single Kustomer conversation spanning multiple channels (e.g., email and chat) must be either split into separate Groove tickets or merged into one thread with channel metadata preserved in the message body.
Kustomer API Rate Limit Management
Kustomer enforces organization-wide API rate limits (300–2,000 rpm depending on plan) shared across all active integrations, requiring careful scheduling, header monitoring of X-RateLimit-Remaining, and cursor-based pagination resumption to avoid dropped payloads.
Tag ID Resolution and Folder Mapping
Kustomer stores tags as IDs rather than strings, requiring a pre-extraction resolution step, plus a mapping logic pass to split tags between Groove Folders (primary organization) and Groove Tags (secondary categorization).
Inactive Agent and Deduplication Handling
Former Kustomer agents cannot be assigned in Groove without provisioning paid seats, requiring remapping to a placeholder account, while duplicate customer records sharing an email must be merged into a canonical contact before load.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can I export Kustomer data directly to Groove?
No, there is no native export or push-button integration. You must extract data via Kustomer's REST API and load it into Groove using its REST v1 or GraphQL v2 API. Import2 may offer a basic connector but lacks support for KObjects and complex transformations.
What data is lost when migrating from Kustomer to Groove?
KObjects (custom objects), company associations, sentiment scores, business rules/workflows, and the unified customer timeline view do not have equivalents in Groove. Multi-channel conversation context is also flattened.
What happens to Kustomer KObjects when migrating to Groove?
Groove has no custom object system. KObjects must be flattened into tags, appended as formatted private notes on relevant tickets, or archived externally. Appending as private notes preserves the most agent-accessible context.
Does Groove support importing tickets with historical timestamps?
Groove's GraphQL v2 API supports setting createdAt during ticket and message creation. The REST v1 API's support for backdating is less clearly documented. Test in a sandbox before running a full migration.
How long does a Kustomer to Groove migration take?
A custom API-led migration typically requires 40–80 engineering hours for medium complexity. Elapsed time depends on data volume, Kustomer's rate limits (300–2,000 rpm depending on plan), and whether delta sync is needed.