Intercom

Intercom is a customer communication platform that helps businesses connect with their customers through messages, support tickets, and knowledge base articles. With Coupler.io, you can automatically export your Intercom data—contacts, conversations, tickets, companies, and more—to Google Sheets, Excel, BigQuery, Looker Studio, or AI destinations like Claude and ChatGPT, without writing any code.

Why connect Intercom to Coupler.io?

• Centralize customer data: Pull contacts, conversations, and tickets into a single spreadsheet or data warehouse for easier analysis

• Build dashboards: Feed Intercom data into Looker Studio or other BI tools to track support metrics and customer engagement

• Automate workflows: Schedule regular exports so your data is always fresh without manual intervention

• Analyze with AI: Send Intercom conversations and tickets to Claude or ChatGPT for sentiment analysis, summarization, or insights

• No coding required: Set up your data flow in minutes using Coupler.io's simple interface

Prerequisites

• An active Intercom account with admin or owner access

• A destination (Google Sheets, Excel, BigQuery, Looker Studio, or an AI destination)

• Ability to authorize Coupler.io to access your Intercom workspace

Quick start

Start by connecting your Intercom account and selecting a report type (like "List of conversations" or "List of tickets"). Then choose your destination and run a manual data flow to test it before scheduling.

How to connect

1

Log in to Coupler.io and create a new data flow. Choose Intercom as your source.

2

Authorize Coupler.io to access your Intercom workspace. You'll be redirected to Intercom's sign-in page. Log in with your Intercom credentials and grant Coupler.io permission to read your data. Once authorized, you'll return to Coupler.io automatically.

3

Select a report type from the dropdown. Choose what data you want to export—for example:

• List of conversations

• List of tickets

• List of contacts

• List of companies

• List of articles

• List of teams

• List of segments

• And more

4

Set optional date filters if needed. For conversations, tickets, and contacts, you can filter by created or updated date using the date picker or macros (e.g., to pull only data from the last 30 days).

5

Configure advanced filters (optional). If you want to narrow down your data further—for example, only conversations with a specific status or tickets assigned to a particular team—you can use the advanced filters option.

6

Choose your destination. Select where you want your Intercom data to land: Google Sheets, Excel, BigQuery, Looker Studio, or an AI destination like Claude or ChatGPT.

7

Run the data flow manually to test it and verify the data looks correct before scheduling.

Available report types

Report Type	Description
List of admins	All admin users in your workspace
List of articles	Knowledge base articles (help center)
List of companies	All companies in your workspace
List of contacts	All customer contacts and their details
List of conversations	All conversations with message history
List of data attributes	Custom data attributes for contacts and companies
List of help centres	All help center instances
List of help center collections	Collections within your help centers
List of news items	News and updates published in your workspace
List of newsfeeds	All newsfeeds
List of segments	All customer segments
List of subscription types	Available subscription types
List of tags	All tags used in your workspace
List of teams	All support teams
List of ticket types	Ticket type configurations
List of tickets	All support tickets and their details

Intercom data overview

Intercom provides rich customer communication data across multiple entities. Depending on the report type you select, you can access everything from individual contacts and their conversation history to support tickets, company information, and team structures.

Report types and entities

Report Type	Best For	Key Fields
List of conversations	Tracking customer interactions	Conversation ID, participant details, message count, created/updated dates, status
List of tickets	Support team analytics	Ticket ID, status, priority, assigned team/admin, created/updated dates, customer details
List of contacts	Customer segmentation and analysis	Contact ID, email, name, phone, company, custom attributes, created/updated dates
List of companies	Account-based analytics	Company ID, name, industry, employee count, custom attributes
List of articles	Content performance	Article ID, title, status, publish date, content type
List of teams	Team management and assignment tracking	Team ID, name, admin assignments, member count
List of segments	Audience targeting	Segment ID, name, contact count, criteria

Conversations data

Key metrics

Metric	Description
Conversation count	Total number of conversations
Average messages per conversation	Message volume per interaction
Conversations by status	Open, closed, or snoozed
Messages from contacts vs admins	Inbound vs outbound message ratio
First response time	Time from customer message to first admin reply

Key dimensions

Dimension	Description
Conversation status	Open, closed, snoozed
Customer email/name	Identifier of the contact
Team assigned	Which team is handling the conversation
Created date	When the conversation started
Updated date	Last activity timestamp
Participant type	Admin, contact, or system

Tickets data

Key metrics

Metric	Description
Ticket count	Total open, closed, or in-progress tickets
Tickets by priority	High, normal, low priority breakdown
Tickets by status	Status distribution (e.g., open, resolved, pending)
Average resolution time	Time from creation to closure
Tickets assigned per admin	Workload distribution

Key dimensions

Dimension	Description
Ticket status	Open, in-progress, closed, pending
Ticket priority	High, normal, low
Assigned team/admin	Who owns the ticket
Customer email/name	Ticket creator
Created date	When the ticket was opened
Updated date	Last status or comment change
Ticket type	Type classification

Contacts data

Key metrics

Metric	Description
Total contacts	All unique contacts in your workspace
Contacts by company	Breakdown by associated company
Contacts by segment	How many contacts in each segment
Contacts with conversations	Contacts who have engaged

Key dimensions

Dimension	Description
Email	Contact email address
Name	Contact full name
Phone	Contact phone number
Company	Associated company name or ID
Custom attributes	Any custom fields you've defined
Created date	When the contact was added
Updated date	Last profile update
Signed up date	When they became a customer

Common metric combinations

Here are ways to combine Intercom data for powerful insights:

• Support efficiency: Join tickets with teams to calculate average resolution time and ticket load per team

• Customer engagement: Combine contacts with conversations to identify your most active customers

• Help center performance: Link articles to company data to see which knowledge base content is most relevant to your audience

• Team workload: Append ticket and conversation data from different teams to compare support volume

Use cases by role

Support leaders

Track team performance and customer satisfaction.

Export your tickets and conversations to Looker Studio to build a real-time dashboard showing:

• Ticket volume by status and priority

• Average resolution time per team

• Admin workload and response times

• Conversation trends over time

Use advanced filters to focus on specific teams, time periods, or ticket types. Aggregate your data to calculate metrics like first response time or customer satisfaction trends.

Product and customer success

Understand customer feedback and engagement patterns.

Export conversations and contacts to analyze:

• Which features customers ask about most

• Customer sentiment in conversations (especially with AI analysis via Claude or ChatGPT)

• Active segments and their engagement levels

• Common support topics by company or industry

Join contact and conversation data to identify your most engaged customers and track their journey.

Marketing and growth

Use Intercom data to improve messaging and targeting.

Pull contacts and segments into Google Sheets or BigQuery to:

• Segment customers by engagement (conversation count, ticket frequency)

• Identify upsell or renewal opportunities based on ticket patterns

• Track how often key segments contact support

• Analyze help center article performance to inform content strategy

Append multi-workspace data to get a unified view of all customer interactions.

Data and analytics

Build comprehensive customer communication dashboards.

Stream all Intercom data to BigQuery or your data warehouse:

• Combine conversations, tickets, and contacts for 360-degree customer view

• Track support metrics (volume, resolution time, team performance)

• Analyze help center usage and content performance

• Create custom segments based on interaction patterns

Use joins and aggregations to calculate derived metrics like "conversations per contact" or "average tickets per company."

Platform-specific notes

• Conversations timeout: Large conversation datasets (especially with full message history) can exceed the 9-minute import limit. Use date filters to pull data in smaller chunks (e.g., weekly or daily) and append them together.

• Ticket searching: The "List of tickets" report allows advanced filtering—use it to narrow results by status, priority, or assigned team before importing.

• Custom attributes: Both contacts and companies support custom data attributes. These appear as columns in your export and can be used for advanced filtering.

• Date macros: For "created after," "created before," "updated after," and "updated before" filters, you can use macros like {{30daysago}} or {{today}} to automate date ranges.

• Message history: Conversation exports include full message threads, which increases data size. If imports are timing out, consider using date filters to pull conversations in smaller batches.

• Company associations: Contacts can be linked to companies; conversations and tickets may also reference company data depending on your setup.

Intercom common issues

Connection issues

Authorization fails or I get redirected back to Coupler.io without connecting

This usually happens when:

• Your Intercom account has restricted access or special permission requirements

• You're not logged into Intercom or your session expired

• Browser cookies or cache are interfering with the OAuth flow

Fix:

1. Clear your browser cache and cookies, or try incognito/private mode

2. Make sure you're logged into Intercom before starting the authorization

3. Check that your Intercom account has admin or owner permissions

4. If the issue persists, contact Intercom support to verify your account doesn't have restricted API access

"Permission denied" error after authorizing

You've successfully authorized, but Coupler.io is being denied access to your data.

Fix:

1. Log in to Intercom and check your API access settings

2. Verify your account role (must be admin or owner)

3. In Intercom, go to Settings > Apps and integrations > Manage apps and check if Coupler.io's authorization is active

4. If needed, revoke and re-authorize Coupler.io

Missing data

Conversations or tickets are missing—I know I have more data than what imported

This is commonly caused by:

• Date filters that are too restrictive

• Advanced filters that are hiding results

• The import timing out before all data could be retrieved (see timeout issues below)

Fix:

1. Check your "created after" and "created before" date filters—expand the date range

2. Review any advanced filters you've applied (status, team, priority, etc.) and temporarily remove them to see if that reveals missing data

3. If you're seeing timeouts, split your import into smaller date ranges (e.g., weekly instead of monthly)

Conversation data is missing message details or participant info

Full message threads and participant details are included in the export, but sometimes they don't appear as separate columns. This is expected behavior—messages are typically nested within the conversation record.

Fix:

1. Check if the data is there but in a different format (nested JSON or combined field)

2. If you need flattened message-level data, consider using the advanced filters or contacting support for a custom extraction

3. For simple conversation metrics, focus on the conversation-level fields (status, created date, participant count)

Contacts are missing company information

Not all contacts may have an associated company in Intercom.

Fix:

1. Check your Intercom workspace—some contacts may genuinely not have a company assigned

2. Export both "List of contacts" and "List of companies" separately, then join them in your destination (Google Sheets, BigQuery, etc.) using company ID or name

3. Use Coupler.io's Join transformation to combine them automatically

Timeout errors

"Timeout: we could not import your data within 9 minutes"

This is the most common issue with Intercom imports, especially for conversations or large ticket datasets. The Intercom API is slow to process large requests, and Coupler.io has a 9-minute timeout.

Fix:

1. Use date filters: Instead of pulling all conversations at once, import them in smaller chunks (e.g., weekly or daily). For example:

   • Run 1: Created after 2024-01-01, Created before 2024-01-07

   • Run 2: Created after 2024-01-08, Created before 2024-01-14

   • Then append all runs into one sheet

2. Reduce the date range: If pulling the last 30 days times out, try 7-14 days

3. Use advanced filters: Filter by status (e.g., only open tickets) or team to reduce data volume

4. Schedule incremental imports: Instead of re-importing all data, schedule frequent small imports (e.g., daily or weekly) to keep data current without overloading the API

5. For high-volume accounts: If timeouts persist even with narrow date ranges, contact support—your workspace may need a custom extraction approach

Do NOT try to run the same import multiple times quickly. Wait for one to complete, then adjust the date range or filters and try again.

Rate limit and API errors

"Error 429" or "Request failed with status code 500" when running the import

This indicates the Intercom API is temporarily unavailable or rate-limited.

Fix:

1. Wait 5-10 minutes and try again

2. If running multiple data flows, space them out—don't run them simultaneously

3. Check Intercom's status page to see if there's an ongoing incident

4. If the error persists, contact Coupler.io support with your import details

Append mode import fails on the second run

If you're using append mode and the second import times out or fails, it could be:

• The retry attempt is trying to fetch all historical data again (creating duplicates or conflicts)

• Your date filter wasn't updated between runs

Fix:

1. Use a new date range for each run (don't re-import overlapping dates)

2. Consider using replace mode instead of append if you're pulling all data each time

3. For incremental imports, schedule them with non-overlapping dates (e.g., Monday-Sunday each week)

Intercom 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:

1. Create one data flow for "List of conversations"

2. Create another for "List of tickets"

3. Export both to the same destination sheet

4. 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:

1. Check if advanced filters can narrow results to what you need

2. Use date filters to reduce data volume and test your setup

3. 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 Data Studio?

Yes! Export any Intercom report to Google Sheets, then connect that sheet to Data 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 Data Studio. If that happens, pre-aggregate or summarize your data in Google Sheets before connecting.