Intercom to Postgres

This page provides you with instructions on how to extract data from Intercom and load it into PostgreSQL. (If this manual process sounds onerous, 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 PostgreSQL?

PostgreSQL, known by most simply as Postgres, is a hugely popular object-relational database management system (ORDBMS). It labels itself as "the world's most advanced open source database," and for good reason. The platform, despite being available for free via an open source license, offers enterprise-grade features including a strong emphasis on extensibility and standards compliance.

It runs on all major operating systems, including Linux, Unix, and Windows. It is fully ACID-compliant, has full support for foreign keys, joins, views, triggers, and stored procedures (in multiple languages). Postgres is often the best tool for the job as a back-end database for web systems and software tools, and cloud-based deployments are offered by most major cloud vendors. Its syntax also forms the basis for querying Amazon Redshift, which makes migration between the two systems relatively painless and makes Postgres a good "first step" for developers who may later expand into Redshift's data warehouse platform.

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].

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 Postgres

Once you have identified all of the columns you will want to insert, you can use the CREATE TABLE statement in Postgres to create a table that can receive all of this data. Then, Postgres offers a number of methods for loading in data, and the best method varies depending on the quantity of data you have and the regularity with which you plan to load it.

For simple, day-to-day data insertion, running INSERT queries against the database directly are the standard SQL method for getting data added. Documentation on INSERT queries and their bretheren can be found in the Postgres documentation here.

For bulk insertions of data, which you will likely want to conduct if you have a high volume of data to load, other tools exist as well. This is where the COPY command becomes quite useful, as it allows you to load large sets of data into Postgres without needing to run a series of INSERT statements. Documentation can be found here.

The Postgres documentation also provides a helpful overall guide for conducting fast data inserts, populating your database, and avoiding common pitfalls in the process. You can find it here.

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.

Other data warehouse options

PostgreSQL is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, or Snowflake, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Snowflake, and To Panoply.

Easier and faster alternatives

If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.

Thankfully, products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your Intercom data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your PostgreSQL data warehouse.