Intercom to Metabase

This page provides you with instructions on how to extract data from Intercom and analyze it in Metabase. (If the mechanics of extracting data from Intercom seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Intercom?

Intercom is a powerful platform for communicating with customers and leads. It provides customer messaging apps for a variety of uses, from targeted messaging to customer support. It offers tracking, filtering, and segmentation functionality on all the data it collects to allow users to analyze interactions to derive business insights.

What is Metabase?

Metabase provides a visual query builder that lets users generate simple charts and dashboards, and supports SQL for gathering data for more complex business intelligence visualizations. It runs as a JAR file, and its developers make it available in a Docker container and on Heroku and AWS. Metabase is free of cost and open source, licensed under the AGPL.

Getting data out of Intercom

You get data out of Intercom using the Intercom API, which offers access to endpoints that can provide information on users, tags, segments, conversations, and more. For example, to get data about a conversation, you could call GET /conversations/[id].

Some use cases may be handled with Apps found in the Intercom App Store.

Sample Intercom data

The Intercom API returns JSON data. Here's the kind of response you might see when querying for the details of a conversation:

{
  "type": "conversation",
  "id": "147",
  "created_at": 1400850973,
  "updated_at": 1400857494,
  "conversation_message": {
    "type": "conversation_message",
    "subject": "",
    "body": "

Hi Alice,

\n\n

We noticed you using our product. Do you have any questions?

\n

- Virdiana

", "author": { "type": "admin", "id": "25" }, "attachments": [ { "name": "signature", "url": "http://example.org/signature.jpg" } ] }, "user": { "type": "user", "id": "536e564f316c83104c000020" }, "assignee": { "type": "admin", "id": "25" }, "open": true, "read": true, "conversation_parts": { "type": "conversation_part.list", "conversation_parts": [ //... List of conversation parts ] }, "tags": { "type": 'tag.list', "tags": [] } } }

Preparing Intercom data

Once you've figured out what you want to pull down and how to pull it, you need to map the data that comes out of each Intercom API endpoint into a schema that can be inserted into your database.

This means that for each value in the response, you need to identify a predefined datatype (i.e. INTEGER, DATETIME, etc.) and build a table that can receive them. The Intercom API documentation can give you a good sense of what fields will be provided by each endpoint, along with their corresponding datatypes.

Complicating things is the fact that these records are not always "flat" – in other words, there may be values that are actually lists. This complicates things because it means you'll most likely to create additional tables to be able to capture the unpredictable cardinality in each record. (The "tags" value in the data above is an example of this.)

Loading data into Metabase

Metabase works with data in databases; you can't use it as a front end for a SaaS application without replicating the data to a data warehouse first. Out of the box Metabase supports 15 database sources, and you can download 10 additional third-party database drivers, or write your own. Once you specify the source, you must specify a host name and port, database name, and username and password to get access to the data.

Using data in Metabase

Metabase supports three kinds of queries: simple, custom, and SQL. Users create simple queries entirely through a visual drag-and-drop interface. Custom queries use a notebook-style editor that lets users select, filter, summarize, and otherwise customize the presentation of the data. The SQL editor lets users type or paste in SQL queries.

Keeping Intercom data up to date

At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in Intercom.

And remember, as with any code, once you write it, you have to maintain it. If Intercom modifies its API, or the API sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.

From Intercom to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Intercom data in Metabase is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Intercom to Redshift, Intercom to BigQuery, Intercom to Azure Synapse Analytics, Intercom to PostgreSQL, Intercom to Panoply, and Intercom to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate Intercom with Metabase. With just a few clicks, Stitch starts extracting your Intercom data, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Metabase.