Salesforce Marketing Cloud Email Studio to Panoply

This page provides you with instructions on how to extract data from Salesforce Marketing Cloud Email Studio and load it into Panoply. (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 Salesforce Marketing Cloud?

Salesforce Marketing Cloud is a marketing automation platform for B2B and B2C marketing. Salesforce Marketing Cloud Email Studio (known as ExactTarget before the company that created it was purchased by Salesforce in 2013) lets businesses create scalable data-based email marketing campaigns. The data it tracks (e.g. open and bounce rates) can be accessed by other parts of the Marketing Cloud platform, which also comprises social media marketing, digital advertising, and mobile messaging components.

What is Panoply?

Panoply provides end-to-end data management-as-a-service. It uses machine learning and natural language processing (NLP) to learn, model, and automate standard data management activities from source to analysis. It can import data with no schema, no modeling, and no configuration. Users can quickly spin up an Amazon Redshift instance and run analysis, SQL, and visualization tools just as they would on a Redshift data warehouse they created on their own.

Getting data out of Salesforce Marketing Cloud

Marketing Cloud offers two APIs:

  • A REST API that exposes access to a range of Marketing Cloud capabilities
  • A SOAP API that provides access to most email functionality, including tracking, subscribers and lists, automations, and content

The SOAP API uses SOAP envelopes to pass SOAP data. A call to retrieve all messages sent since the last batch might look like this.

<Envelope xmlns="http://schemas.xmlsoap.org/soap/envelope/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
   <Header>
      <Security xmlns="https://www.marketingcloud.com/">
         <fueloauth>YOUR_ACCESS_TOKEN</fueloauth>
      </Security>
   </Header>
   <Body>
      <RetrieveRequestMsg xmlns="http://exacttarget.com/wsdl/partnerAPI">
         <RetrieveRequest>
            <ObjectType>SentEvent</ObjectType>
            <Properties>SubscriberKey</Properties>
            <Properties>EventDate</Properties>
            <QueryAllAccounts>false</QueryAllAccounts>
            <Filter xsi:type="SimpleFilterPart">
               <Property>SendID</Property>
               <SimpleOperator>equals</SimpleOperator>
               <Value>12345</Value>
            </Filter>
            <RetrieveAllSinceLastBatch>true</RetrieveAllSinceLastBatch>
         </RetrieveRequest>
      </RetrieveRequestMsg>
   </Body>
</Envelope>

Sample Salesforce Marketing Cloud Email Studio data

The Email Studio API returns information in a SOAP envelope. You have to parse all the attributes before loading the data into your data warehouse. Here's an example of what some of the data for that call to retrieve all messages might look like.

<soap:Envelope xmlns:soap="http://schemas.xmlsoap.org/soap/envelope/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:wsa="http://schemas.xmlsoap.org/ws/2004/08/addressing" xmlns:wsse="http://docs.oasis-open.org/wss/2004/01/oasis-200401-wss-wssecurity-secext-1.0.xsd" xmlns:wsu="http://docs.oasis-open.org/wss/2004/01/oasis-200401-wss-wssecurity-utility-1.0.xsd">
   <soap:Header>
      <wsa:Action>RetrieveResponse</wsa:Action>
      <wsa:MessageID>urn:uuid:cdb7b621-8341-4a35-840d-c4ec56fb8a6d</wsa:MessageID>
      <wsa:RelatesTo>urn:uuid:e6a83541-3ae7-412d-9412-d40ee321c4aa</wsa:RelatesTo>
      <wsa:To>http://schemas.xmlsoap.org/ws/2004/08/addressing/role/anonymous</wsa:To>
   </soap:Header>
   <soap:Body>
      <RetrieveResponseMsg xmlns="http://exacttarget.com/wsdl/partnerAPI">
         <OverallStatus>OK</OverallStatus>
         <RequestID>cefcfd29-d44f-4bbd-9188-e2bd51e2de5f</RequestID>
         <Results xsi:type="SentEvent">
            <PartnerKey xsi:nil="true"/>
            <ObjectID xsi:nil="true"/>
            <SubscriberKey>acruz@example.com</SubscriberKey>
            <EventDate>2017-03-26T10:02:01.987</EventDate>
         </Results>
         <Results xsi:type="SentEvent">
            <PartnerKey xsi:nil="true"/>
            <ObjectID xsi:nil="true"/>
            <SubscriberKey>jsmith@example.com</SubscriberKey>
            <EventDate>2014-03-26T10:02:01.987</EventDate>
         </Results>
         <Results xsi:type="SentEvent">
            <PartnerKey xsi:nil="true"/>
            <ObjectID xsi:nil="true"/>
            <SubscriberKey>rc@example.com</SubscriberKey>
            <EventDate>2017-03-26T10:02:01.987</EventDate>
         </Results>
      </RetrieveResponseMsg>
   </soap:Body>
</soap:Envelope>

Preparing Salesforce Marketing Studio 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. Salesforce's 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. This means you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

Loading data into Panoply

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.

Keeping Salesforce Marketing Cloud 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 Email Studio.

And remember, as with any code, once you write it, you have to maintain it. If Salesforce 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

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, or Snowflake, which are RDBMSes that use similar SQL syntax. 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, and To Snowflake.

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 Salesforce Marketing Cloud Email Studio data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Panoply data warehouse.