Listrak to Panoply

This page provides you with instructions on how to extract data from Listrak 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 Listrak?

Listrak is a marketing automation platform for online and omnichannel retailers. It combines data from desktop and mobile platforms with online and offline data about purchases, and uses AI and predictive analytics to give businesses insights into their customers' behavior.

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 Listrak

Listrak has three REST APIs – for email, SMS, and data input – that developers can use to get at information stored in the platform. For example, to get information about an email list, you would call GET /v1/List/{listId}.

Sample Listrak data

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

"status": 200,
"data": {
"listId": 0,
"listName": null,
"folderId": 0,
"ipPoolId": 0,
"bounceDomainAlias": null,
"bounceHandling": null,
"bounceUnsubscribeCount": 0,
"createDate": "0001-01-01T00:00:00",
"enableBrowserLink": false,
"enableDoubleOptIn": false,
"enableDynamicContent": false,
"enableGoogleAnalytics": false,
"enableInternationalization": false,
"enableListHygiene": false,
"enableListRemovalHeader": false,
"enableListRemovalLink": false,
"enableListrakAnalytics": false,
"enableSpamScorePersonalization": false,
"enableToNamePersonalization": false,
"fromEmail": null,
"fromName": null,
"googleTrackingDomains": null,
"linkDomainAlias": null,
"mediaDomainAlias": null

Preparing Listrak 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. Listrak'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

Once you've identified all the columns you want to insert, you can use Redshift's CREATE TABLE statement to create a table to receive all of the data.

Once you have a table built, you might think that the easiest way to migrate your data (especially if there isn't much of it) would be to build INSERT statements to add data to your Redshift table row by row. Don't do it! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, we suggest loading the data into Amazon S3 and then using the COPY command to load it into Redshift.

Keeping Listrak 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 Listrak.

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

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 move data from Listrak to Panoply automatically. With just a few clicks, Stitch starts extracting your Listrak data, structuring it in a way that's optimized for analysis, and inserting that data into your Panoply data warehouse.