# 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

{% tabs %}
{% tab title="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.
{% endtab %}

{% tab title="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.
{% endtab %}

{% tab title="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.
{% endtab %}

{% tab title="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."
{% endtab %}
{% endtabs %}

## 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.