FAQ
Can I pull data from multiple Intercom workspaces?
You can connect each workspace separately by creating multiple data flows in Coupler.io. Each connection requires its own OAuth authorization. Once you have imports from all workspaces, you can append them together in your destination using Coupler.io's Append transformation or by manually combining sheets.
How often can I schedule imports from Intercom?
You can schedule imports as frequently as you need—hourly, daily, weekly, or monthly. However, to avoid timeouts and API overload, we recommend:
Daily or weekly for conversations and tickets (with narrow date ranges using date filters)
Weekly or monthly for static data like teams, segments, and tags
Leave at least 30 minutes between imports of the same data flow to allow the API to recover.
Which report should I use to track customer support volume and resolution time?
Use "List of tickets" if your workspace is primarily ticket-based, or "List of conversations" if you're using conversations for support. Both include created/updated dates so you can calculate resolution time. Export to BigQuery or Google Sheets and add a calculated column: resolution_time = closed_date - created_date.
See the Data Overview article for a full list of available fields in each report and use cases by role.
What's the difference between "created after/before" and "updated after/before" filters?
Created: Filters by when the conversation, ticket, or contact was first created. Use this for weekly or daily imports of new data.
Updated: Filters by when the record was last modified (status change, new message, tag added, etc.). Use this if you want to catch recent changes to existing records.
For incremental imports, use created dates to avoid re-pulling old data.
Can I analyze sentiment or summarize conversations automatically?
Yes! Export your conversations to an AI destination like Claude or ChatGPT. Once the data reaches the AI, you can:
Summarize long conversation threads
Analyze customer sentiment
Extract key issues or feature requests
Categorize conversations by topic
This is faster than reading manually and helps you spot trends across hundreds of conversations.
Why is my append import duplicating data?
This usually happens when:
You used overlapping date ranges in consecutive imports (e.g., run 1 pulls Jan 1-7, run 2 also pulls Jan 1-7)
You re-ran the same date range without clearing the destination sheet first
Fix: Plan non-overlapping date ranges for each import:
Run 1: Jan 1-7
Run 2: Jan 8-14
Run 3: Jan 15-21
This way, each run captures new data without overlap.
See Common Issues for more on append mode timeouts.
How do I combine conversation and ticket data in one view?
If you need conversations and tickets in the same sheet:
Create one data flow for "List of conversations"
Create another for "List of tickets"
Export both to the same destination sheet
Use Coupler.io's Append transformation to combine them (if they have matching column names), or manually append them in your spreadsheet or data warehouse
Note: They may have different column structures, so you might need a manual cleanup step.
What if I need to pull specific data that isn't in the standard reports?
Intercom's standard reports cover most use cases (contacts, conversations, tickets, companies, teams, etc.). If you need custom fields or more granular filtering:
Check if advanced filters can narrow results to what you need
Use date filters to reduce data volume and test your setup
Contact Coupler.io support—they may be able to help with custom extractions or point you to Intercom API documentation
Can I use Intercom data in Looker Studio?
Yes! Export any Intercom report to Google Sheets, then connect that sheet to Looker Studio. You can then:
Build dashboards for ticket volume, resolution time, and team performance
Track conversation trends and customer engagement
Create filters by team, status, priority, or date range
Note: Very large datasets may load slowly in Looker Studio. If that happens, pre-aggregate or summarize your data in Google Sheets before connecting.
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