Freshchat connector
Set up the Freshchat connector in Kaivo: authentication, configuration, the 10 BigQuery tables it syncs, and answers to common questions.
Written By Lauri Raivio
Last updated 16 days ago
Kaivo is a fully managed data platform that syncs your Freshchat data into a Google BigQuery warehouse and keeps it up to date automatically. There is no pipeline to build and no infrastructure to run, so you can spend your time analysing your customer support data instead of moving it.
What is the Freshchat connector
Sync your Freshchat conversation data into BigQuery with Kaivo to measure chat volume, response times, and agent performance.
Getting started with the Freshchat connector
- Sign up for Kaivo and create a workspace.
- Connect your Freshchat account.
- Choose which tables to sync.
- Wait for the initial sync to finish.
- Query your data in BigQuery or your favourite AI or BI tool.
Authenticating Freshchat
Authenticate with your API Key.
Configuring the Freshchat connector
When you set up the connector, you provide:
Tables and columns synced from Freshchat
Kaivo syncs 10 tables from Freshchat into a dedicated dataset in your BigQuery warehouse. Click any table to see its columns and types.
account (12 columns)
account (12 columns)
agents (19 columns)
agents (19 columns)
Subtable: agents__groups
Subtable: agents__social_profiles
channels (12 columns)
channels (12 columns)
Subtable: channels__tags
Subtable: channels__welcome_message__message_parts
conversations (17 columns)
conversations (17 columns)
conversations_messages (19 columns)
conversations_messages (19 columns)
Subtable: conversations_messages__message_parts
conversations_properties (12 columns)
conversations_properties (12 columns)
Subtable: conversations_properties__fields
Subtable: conversations_properties__choices
groups (7 columns)
groups (7 columns)
roles (6 columns)
roles (6 columns)
users (12 columns)
users (12 columns)
Subtable: users__properties
users_conversations (4 columns)
users_conversations (4 columns)
How the Freshchat sync works
After the first load, Kaivo keeps your BigQuery warehouse up to date for you. Where Freshchat supports it, each sync pulls only new and changed records so it stays fast; otherwise it refreshes the whole table. Every record keeps its original ID, so you won't get duplicate rows.
Frequently asked questions
How long does the initial sync take for Freshchat?
It depends on how much history is in your Freshchat account. Most initial syncs finish within minutes, while large accounts can take a few hours. After that, syncs only fetch new and changed records, so they're much faster.
Can I sync only some tables or columns?
Yes. You pick which tables to sync when you set up the connection and can change the selection later. Tables you don't select are never copied to your warehouse.
What happens when Freshchat's schema changes?
New fields are never added automatically. You choose which fields to sync, so data you haven't selected (sensitive personal data, for example) never lands in your warehouse. When a new field appears, it becomes available for you to add. What happens to removed or renamed fields depends on a table's sync mode: full-refresh tables always match what's currently in Freshchat, so dropped fields disappear, while incremental tables keep their existing columns and history, so an old field stays and newly added fields fill in over time.
How do I handle GDPR or data deletion requests?
Your data lives in your own Kaivo-managed BigQuery warehouse, so the most direct option is to delete or anonymise specific records right in BigQuery. If you delete data in Freshchat instead, full-refresh tables drop it on the next sync, while incremental tables keep it, so you would remove the row in BigQuery or ask us to run a full refresh. To remove everything, delete the Freshchat connector in Kaivo and all of its synced data is deleted with it.
Common use cases for Freshchat data
Conversation volume
Use conversations and conversations_messages to track chat volume over time.
Response and resolution
Use conversations_properties to measure response and resolution times.
Agent performance
Join conversations with agents and groups to compare workload and handling.
Use Freshchat data in your AI and BI tools
Once Freshchat data lands in your Kaivo-managed BigQuery warehouse, you can explore it with AI tools or any BI tool that connects to BigQuery. Here's how the most common destinations work with Freshchat data.
Claude
Use Kaivo's MCP server to give Claude secure, workspace-scoped access to your data. Setup guide →
Power BI
Microsoft's BI tool with a native BigQuery connector. Supports direct query and scheduled refresh. Setup guide →
Data Studio
Free Google BI tool with native BigQuery support. One-click connection to your Kaivo warehouse; great for SMB teams on Google Workspace. Setup guide →
Tableau
The premium analytics standard, with native BigQuery integration. Setup guide →
Google Sheets
Use Connected Sheets to query BigQuery directly from a spreadsheet, with no SQL. Setup guide →
Excel
Connect via Power Query's BigQuery connector. Setup guide →
Metabase
Open-source BI tool with strong BigQuery support. Setup guide →
See our pricing page for Freshchat connector pricing and plan details.
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