Every ticket & field
Pull the full Kustomer data model - tickets, conversations, CSAT, agents, SLAs - not just a fixed preset, so your support reports are never missing a field.
Kustomer
Kustomer
Customer SupportDataddo extracts the full Kustomer data set - tickets, conversations, CSAT, agents, SLAs - and delivers it wherever your team needs it: into AI tools and agents, your warehouse like Snowflake and BigQuery, BI dashboards, and back into your CRM and messaging tools. Blend it with product and CRM data, backfill full history, and keep it fresh on your schedule - all fully managed, with the data plane running where you choose. No pipelines to build, no lock-in.
YOUR SUPPORT SOURCE OF TRUTH
Turn Kustomer into support reporting your whole team trusts
Support data is only useful when it's complete, clean, and current. Dataddo extracts the full Kustomer data model, governs it, and serves it to every warehouse, dashboard, AI tool, and downstream system - continuously and without manual exports.
Blend with CRM & product
Combine Kustomer with your CRM and product data so customer-health and full-journey analytics live in one place.
Backfill full history
Load historical Kustomer data beyond the platform's export limits, so trend, cohort, and SLA analysis holds up.
Model & clean before load
Normalize statuses, tags, and owner names, and let the Data Quality Firewall stop bad rows before Kustomer data reaches your reports.
Fresh on your schedule
Incremental, scheduled syncs keep Kustomer data current - as often as hourly - without manual exports or full reloads.
Governed & monitored
Delivery alerts and quality checks catch gaps in Kustomer data early, so support dashboards never silently break.
DATASETS
What you can pull from Kustomer
The Kustomer connector provides a list of curated datasets - ready-made sets of records, fields, and metrics you can extract on a schedule. Need something specific? Any dataset can be forked and customized, or built from scratch, with our Universal Connector.
List of curated datasets
Idid
string
Tagstags
string
Snooze Statussnooze_status
string
Statusstatus
string
Snooze CountsnoozeCount
integer
Snooze Status Atsnooze_statusAt
datetime
Modified bymodifiedBy
string
Re-open CountreopenCount
float
Deleteddeleted
integer
Modified AtmodifiedAt
datetime
customer_id
string
queue_id
string
SLA IDsla_id
string
SLA Namesla_name
string
SLA Statussla_status
string
SLA versionslaVersion
string
Directiondirection
string
Message CountmessageCount
integer
Namename
string
Localelocale
string
Note CountnoteCount
integer
Outbound Message CountoutboundMessageCount
integer
Revrev
float
Satisfactionsatisfaction
float
Satisfaction level ChannelsatisfactionLevel_channel
string
Firstdone Created by TeamsfirstDone_createdByTeams
string
Ffirstresponse AssignedteamsfirstResponse_assignedTeams
string
Firstresponse AssignedusersfirstResponse_assignedUsers
string
Firstresponse CreatedbyteamsfirstResponse_createdByTeams
string
Satisfactionlevel CreatedatsatisfactionLevel_createdAt
datetime
Satisfaction level FormsatisfactionLevel_form
string
Satisfaction level Form responsesatisfactionLevel_formResponse
string
Satisfaction level ScheduledforsatisfactionLevel_scheduledFor
string
Satisfaction level SentatsatisfactionLevel_sentAt
datetime
Satisfaction level SentbysatisfactionLevel_sentBy
datetime
Satisfaction level StatussatisfactionLevel_status
string
Satisfaction level RatingsatisfactionLevel_rating
float
Satisfactionlevel UpdatedatsatisfactionLevel_updatedAt
datetime
InboundmessagecountinboundMessageCount
float
First Message In CreatedatfirstMessageIn_createdAt
datetime
First Message In SentatfirstMessageIn_sentAt
datetime
First Message In ChannelfirstMessageIn_channel
string
First Message In DirectiontypefirstMessageIn_directionType
string
First message in IdfirstMessageIn_id
string
First message in FromfirstMessageIn_from
sensitivestring
First message in TofirstMessageIn_to
sensitivestring
First message in SubjectfirstMessageIn_subject
string
First message in RecipientfirstMessageIn_recipient
sensitivestring
Last message in Created AtlastMessageIn_createdAt
datetime
Last Message In IDlastMessageIn_id
string
Last Message In Sent AtlastMessageIn_sentAt
datetime
Last Message Out Created AtlastMessageOut_createdAt
datetime
Last Activity atlastActivityAt
datetime
Last Message Out IdlastMessageOut_id
string
Last Message Out Sent AtlastMessageOut_sentAt
datetime
Assigned UsersassignedUsers
string
Channelschannels
string
Created AtcreatedAt
datetime
Custom Motivodecontatotreecustom_motivoDeContatoTree
string
Default langdefaultLang
string
Done CountdoneCount
integer
Endedended
integer
Ended AtendedAt
datetime
Ended by typeendedByType
string
Ended reasonendedReason
string
Need a dataset, metric, or attribute you don't see?
Tell us what's missing and we'll add it to the connector.
WITH VS. WITHOUT
Who carries the load when things change
Keep Kustomer data flowing to every warehouse, dashboard, and tool - and see who owns it when an API or schema changes:
| Without Dataddo |
With Dataddo
|
Outcome for you | |
|---|---|---|---|
| API or auth change | You discover the breakage and scramble to fix it. | We update the connector and restore the pipeline - often before you notice. | Syncs from Kustomer keep flowing |
| Schema drift | Columns change and pipelines break or corrupt data silently. | Detected automatically and handled by configurable rules. | Only clean Kustomer data lands in reports |
| Endpoint deprecated | You re-engineer the integration. | We own the update - the data contract holds. | Your Kustomer reporting keeps working |
| Missing connector | You build and maintain a custom integration. | We build it and maintain it, under a ~4-week SLA. | Any Kustomer data you need, on request |
| Silent degradation | You find out when a report or model run fails. | Proactive monitoring catches anomalies and delays first. | Gaps caught before dashboards break |
| Debugging | You dig through logs across disconnected tools. | Run histories, payload inspection, and end-to-end lineage in one place. | Faster root-cause, fewer reporting gaps |
Data residency by design
Choose where Kustomer data is processed - same cloud, region, or sovereign
Kustomer is a cloud API, so the question isn't on-prem vs cloud - it's which region your data flows through. With Dataddo you run the data plane in the region you choose, so Kustomer payloads move straight to your destinations without transiting a third-party SaaS. Pin it to an EU or US region for compliance, or self-host when a downstream target lives inside your own network.
| Data Plane location | Typical data sensitivity | Why this setup |
|---|---|---|
|
Public cloud 
AWS
Microsoft Azure
Google Cloud
|
Low to moderate - general business, marketing, and product data; sources that are already cloud-native. | Fastest to stand up and scales elastically. Best when the data has no residency restriction and often already lives in the same public cloud. |
|
Regional & EU sovereign clouds
EU sovereign clouds
Regional providers
Private cloud
|
Regulated / residency-bound - PII, financial, and health data governed by GDPR or local law. | Keeps processing inside a specific jurisdiction to meet data-residency and sovereignty rules, while still running as managed infrastructure. |
|
On-premises 
Kubernetes
Red Hat OpenShift
VMware Tanzu
|
Highly sensitive / restricted - data that contractually or legally cannot leave the corporate perimeter. | Data never leaves your network. Required for air-gapped, classified, or locked-down environments; the Control Plane still manages it via metadata only. |
DATA DELIVERY
Every way to deliver Kustomer data
Support data rarely has one home. Dataddo feeds Kustomer into AI and agents, loads it into your warehouse for support analytics, syncs customer health back into CRM and messaging tools, and delivers files to storage - all governed the same way.
| Transport type | What it does | Typical destinations | Typical business use cases |
|---|---|---|---|
| Data to AI™ & Agents | Feed Kustomer data into vector databases, LLMs, and AI agents so support copilots can reason over your tickets. | Load Kustomer tickets and conversations into vector stores for retrieval-augmented support assistants, Give AI agents and copilots governed access to live Kustomer support data | |
| ETL / ELT to your warehouse | Load Kustomer tickets and metrics into your warehouse for support and customer-health analytics. | Centralize Kustomer alongside CRM and product data in Snowflake, BigQuery, or Redshift, Backfill Kustomer history for SLA and cohort analysis | |
| Sync back to CRM & messaging | Push customer-health scores and segments built from Kustomer data back into CRM and messaging platforms. | Sync CSAT and health scores from Kustomer to your CRM, Trigger messaging and win-back flows from Kustomer support signals | |
| Files to storage & partners | Deliver raw or modeled Kustomer extracts to object storage and partners. | Archive raw Kustomer exports to S3, GCS, or Azure storage, Send scheduled Kustomer extracts to partners over SFTP |
Claude
Snowflake
BigQuery
Amazon Redshift
PostgreSQL
Salesforce
HubSpot
Braze
Klaviyo
Google Cloud Storage
Azure Blob Storage
SFTP
Case Studies
See what support and data teams build with Dataddo
65,000 Social Media Accounts. One Platform. Zero Manual Authorizations.
Beauty & Consumer Goods
How Livesport Activates Data, Saves Engineering Resources with BigQuery and Dataddo
Entertainment
How ID&T Group Activates Data from 1M+ Festival Fans and Dozens of Social Accounts
Entertainment
How Sensire Accelerated Migration of a Proprietary On-Premise Data Infrastructure to the Cloud with Dataddo
Healthcare
How Ringside.ai Builds a Data Product Better and Faster Using Dataddo
Marketing
How Publicis Groupe Brasil Uses Dataddo's API to Scale a Data Product
Advertising
Pricing
Pick the plan that fits how you use your data.
Move data into your warehouse, lake or business apps with Data Anywhere, or let your AI assistants query it directly with Data to AI™. Need tailored contracts or volume pricing? Talk to us about Enterprise.
FAQ
Kustomer as a source + Dataddo, answered
Where can I send Kustomer data?
To any of Dataddo's 150+ destinations - warehouses like Snowflake and BigQuery, BI dashboards, CRMs and marketing tools, object storage, and AI or vector stores - plus custom destinations on request.
Do I have to maintain the pipeline?
No. Dataddo is fully managed: we maintain the Kustomer connector, adapt to API and schema changes, and alert you if anything needs attention. There is no pipeline code for you to write or fix.
Can I analyze support alongside CRM and product data?
Yes. Dataddo loads Kustomer tickets and metrics into Snowflake, BigQuery, or Redshift, blended with your CRM and product data, so customer-health, SLA, and churn analysis works without manual exports.
What if Kustomer changes their API?
That's on us, not you. Dataddo's team proactively tracks Kustomer API and schema changes and updates the connector before they break your pipeline. Continuous monitoring watches every sync, and if anything needs attention you get an alert - so your Kustomer data keeps flowing without you touching a thing.
Which Kustomer data and fields are available?
The full Kustomer data model - tickets, conversations, CSAT, agents, and SLAs - not a fixed subset. If a field you need isn't exposed yet, we add it on request, usually at no extra cost.
How is my data secured?
Dataddo is SOC 2 Type II and ISO 27001 certified. Data is encrypted in transit and at rest, PII can be masked or hashed, and EU or US data residency is available. Self-host the data plane and payload data never leaves your network.
Will API rate limits be a problem?
Dataddo handles Kustomer pagination, rate limits, and retries for you, and pulls incrementally on a schedule so you stay within API quotas without impacting your source.
Am I locked in?
No. Pipelines are source- and destination-agnostic - you can add or switch destinations without rebuilding, and data lands in native formats you own.