Help Scout to Power BI

This page provides you with instructions on how to extract data from Help Scout and analyze it in Power BI. (If the mechanics of extracting data from Help Scout 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 Help Scout?

Help Scout provides a help desk platform with email and live chat support, a knowledge base tool, and an embeddable search/contact widget.

What is Power BI?

Power BI is Microsoft’s business intelligence offering. It's a powerful platform that includes capabilities for data modeling, visualization, dashboarding, and collaboration. Many enterprises that use Microsoft's other products can get easy access to Power BI and choose it for its convenience, security, and power.

With high-value use cases across analysts, IT, business users, and developers, Power BI offers a comprehensive set of functionality that has consistently landed Microsoft in Gartner's "Leaders" quadrant for Business Intelligence.

Getting data out of Help Scout

Help Scout provides a Mailbox API that lets developers retrieve data stored in the platform about customers, mailboxes, conversations, and more. For example, to retrieve information about a customer conversation, you would call GET https://api.helpscout.net/v2/conversations/id.

Sample Help Scout data

Here's an example of the kind of response you might see with a query like the one above.

{
  "id" : 123,
  "number" : 12,
  "threads" : 2,
  "type" : "email",
  "folderId" : 11,
  "status" : "closed",
  "state" : "published",
  "subject" : "Help",
  "preview" : "Preview",
  "mailboxId" : 13,
  "assignee" : {
    "id" : 256,
    "type" : "customer",
    "first" : "Mr",
    "last" : "Robot",
    "email" : "none@nowhere.com"
  },
  "createdBy" : {
    "id" : 12,
    "type" : "customer",
    "email" : "bear@acme.com"
  },
  "createdAt" : "2019-03-15T22:46:22Z",
  "closedBy" : 14,
  "closedAt" : "2019-03-16T14:07:23Z",
  "userUpdatedAt" : "2019-03-16T14:07:23Z",
  "customerWaitingSince" : {
    "time" : "2019-07-24T20:18:33Z",
    "friendly" : "20 hours ago",
    "latestReplyFrom" : "customer"
  },
  "source" : {
    "type" : "email",
    "via" : "customer"
  },
  "tags" : [ {
    "id" : 9150,
    "color" : "#929499",
    "tag" : "vip"
  } ],
  "cc" : [ "bear@normal.com" ],
  "bcc" : [ "bear@secret.com" ],
  "primaryCustomer" : {
    "id" : 238604,
    "type" : "customer",
    "first" : "Rob",
    "last" : "Robertovic",
    "email" : "rob@acme.com"
  },
  "customFields" : [ {
    "id" : 8,
    "name" : "Account Type",
    "value" : "8518",
    "text" : "Free"
  }, {
    "id" : 6688,
    "name" : "Account Status",
    "value" : "33077",
    "text" : "Trial"
  } ],
  "_links" : {
    "assignee" : {
      "href" : "..."
    },
    "closedBy" : {
      "href" : "..."
    },
    "createdByCustomer" : {
      "href" : "..."
    },
    "mailbox" : {
      "href" : "..."
    },
    "primaryCustomer" : {
      "href" : "..."
    },
    "self" : {
      "href" : "..."
    },
    "threads" : {
      "href" : "..."
    },
    "web" : {
      "href" : "..."
    }
  }
}

Preparing Help Scout data

If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. The Help Scout documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.

Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. In these cases you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

Loading data into Power BI

You can analyze any data in Power BI, as long as that data exists in a data warehouse that's connected to your Power BI account. The most common data warehouses include Amazon Redshift, Google BigQuery, and Snowflake. Microsoft also has its own data warehousing platform called Azure SQL Data Warehouse.

Connecting these data warehouses to Power BI is relatively simple. The Get Data menu in the Power BI interface allows you to import data from a number of sources, including static files and data warehouses. You'll find each of the warehouses mentioned above among the options in the Database list. The Power BI documentation provides more details on each.

Analyzing data in Power BI

In Power BI, each table in the data warehouse you connect is known as a dataset, and the analyses conducted on these datasets are known as reports. To create a report, use Power BI’s report editor, a visual interface for building and editing reports.

The report editor guides you through several selections in the course of building a report: the visualization type, fields being used in the report, filters being applied, any formatting you wish to apply, and additional analytics you may wish to layer onto your report, such as trendlines or averages. You can explore all of the features related to analyzing and tracking data in the Power BI documentation.

Once you've created a report, Power BI lets you share it with report "consumers" in your organization.

Keeping Help Scout 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.

The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, Help Scout's API results include fields like created_At that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've taken new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.

From Help Scout to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Help Scout data in Power BI 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 Help Scout to Redshift, Help Scout to BigQuery, Help Scout to Azure SQL Data Warehouse, Help Scout to PostgreSQL, Help Scout to Panoply, and Help Scout to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data from Help Scout to Power BI automatically. With just a few clicks, Stitch starts extracting your Help Scout data via the API, 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 Power BI.