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.

Panoply provides a Smart Cloud Data Warehouse platform that lets users set up a new Amazon Redshift instance in just a few clicks. It uses machine learning algorithms to accomplish complex tasks like schema building, data mining, modeling, scaling, performance tuning, security, and backup. Panoply can import data with no schema, no modeling, and no configuration, and you can use analysis, SQL, and visualization tools on data in Panoply just as you would if you were creating a Redshift data warehouse on your own.

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].

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": [] } }
}

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.)

When you've identified all of the columns you want to insert, use the Reshift CREATE TABLE statement to create a table in your data warehouse to receive all the data.

Once you have a table built, it may seem like the easiest way to replicate your data (especially if there isn't much of it) is to build INSERT statements to add data to your Redshift table row by row. If you have any experience with SQL, this probably will be your first inclination. Think again! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, you should load the data into Amazon S3 and then use the COPY command to load it into Redshift.

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.

Panoply 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, PostgreSQL, Snowflake, or Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, To Snowflake, To Azure SQL Data Warehouse, To S3, and To Delta Lake.