Integrate Cordial Data with Snowflake for Useful Analytics and Activation

A Step-by-Step Guide

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Make the Most of Your Cordial Data in Snowflake

Cordial and Snowflake are a natural fit for data modeling, business intelligence and data activation – especially for retail brands interested in optimizing their overall business performance, acquisition and retention marketing programs, and merchandising and fulfillment operations decisions.

Cordial is the only true marketing platform that empowers brands to fully automate their marketing strategies. Future-thinking brands such as Revolve, Eddie Bauer, and Adore Me choose Cordial to deliver high-conversion messages—across email, SMS, mobile app, social media, direct mail, and more—that drive record-breaking customer engagement and revenue growth. Snowflake enables every organization to mobilize their data with Snowflake’s Data Cloud. Customers use the Data Cloud to discover and securely share data, power data applications, and execute diverse AI/ML and analytic workloads. Together, the software application and cloud data tooling provide business and data practitioners with an opportunity to analyze and optimize email, SMS and campaign automation to drive profitable growth.

Connect to Cordial

The first step toward useful, modeled Cordial data in Snowflake is to connect the source and destination systems. There are many legacy tools available in market that handle the ETL or ELT transfer of Cordial data to Snowflake, and there are emerging tools that accomplish this transfer while providing value-added services like local data logging, and semantic data labeling and mapping along the way – making Cordial data modeling, analytics and activation easier once the data is landed in Snowflake.

To connect to Cordial, follow these easy steps

  • Open SoundCommerce in any browser. Open the “Intelligent Pipeline” application from the top right navigation menu. Select “Sources” from the left navigation menu. Choose “Add New Source” from within the Sources pane to open the data source library.

  • Search or browse to find “Cordial” within the data source library.

  • Complete the “Connection Setup” form with your credentials and token to securely connect to Cordial and begin collecting source data.

Log Cordial Data for Flexible Modeling in Snowflake

There are a few more considerations to address along the way. First, what happens if Cordial is unavailable for some reason, or the data you’re expecting has been purged by Cordial? What happens when Cordial changes their API schemas and data scope? What happens if you need to reinterpret your Cordial data for a new use case in the future?

You’ll want your Cordial data immutably logged locally, just upstream of Snowflake to ensure you have the data and data flow flexibility you need to future-proof your Cordial data and models. SoundCommerce provides permanent logging of Cordial data upstream of Snowflake to ensure failover and future-proofing. Regardless of how you connect your Cordial and Snowflake data, you’ll want a data lake or event log in the middle to ensure data integrity and modeling flexibility.

Define and Label Cordial Data for Snowflake

As new technologies arise and best practices evolve, traditional integration tools like ETL and ELT data pipelines are giving way to intelligent pipelines that help prep data for Snowflake starting at ingest. Simply moving JSON from Cordial to Snowflake leaves all the work for your data team in Snowflake.

As you onboard your Cordial data into Snowflake, you’ll want to create semantic labels and metadata that describe the Cordial data for easier unification and modeling across other systems and data in Snowflake.

There are third-party solutions that will catalog your Cordial data and generate semantic labels and mappings after you’ve landed it in Snowflake. With SoundCommerce, the Cordial data is defined and labeled on its way into Snowflake instead, to avoid this costly rework later. You’ll end up with business-ready entities like orders, customers, products and campaigns, making it much easier to model your Cordial data in Snowflake.

Map Cordial Entities to Snowflake

Once the raw Cordial data has been organized into useful entities, it’s time to map the Cordial data into useful tables in Snowflake.

Why do defined and labeled entities from Cordial matter so much? The main reason is that Cordial data needs to be combined with data from other SaaS and on-premise software systems in useful ways. Landing raw Cordial data in Snowflake without this semantic understanding means data engineering and analyst teams must do all of the heavy lifting regarding the meaning of the Cordial data and the standardization of the meaning of that Cordial data from scratch in Snowflake.

Defining, labeling and mapping the Cordial data on the way in means much less effort once the data is landed in Snowflake.

Materialize Cordial Data in Snowflake

Next, you’ll establish a secure connection to Snowflake:

  • Select “Destinations” from the left navigation menu. Choose “Add New Destination” from within the Destinations pane to open the data destination library.

  • Complete the “Connection Setup” form to securely connect to Snowflake to establish a secure destination for your labeled, mapped and modeled data.

That’s it! You now have logged, labeled and mapped data from Cordial flowing securely to Snowflake.

Model Cordial Data in Snowflake

Once you have well-formed entities from Cordial onboarded to Snowflake, it’s time to build useful analytical and behavioral models on the Cordial data – and combine the Cordial data with data sets from other systems in Snowflake for more advanced, cross-dimensional analysis.

You can build your own analytical models on the Cordial data in Snowflake using languages like SQL and Python, organized into model libraries in tools like DBT or Coalesce. With SoundCommerce, you get prebuilt analytical models for Cordial running in Snowflake, with ready access to the model source code in DBT.

Host the Modeled Cordial Data in Snowflake for Analytics

Snowflake supports reporting and visualization through a wide variety of analytics tools including Sigma, Tableau, Looker, Power BI and Microstrategy to name a few. You can build your own dashboards, tabular views and graphs in any of these tools to reveal insights about Cordial in your Snowflake models. SoundCommerce provides pre-built embedded reports in Sigma to reduce the time, cost and risk of BI reporting of Cordial data out of Snowflake – so you can start making better decisions and taking better action as soon as you’ve connected Cordial to Snowflake.

Host the Modeled Cordial Data in Snowflake for Campaign and Customer Activation

Whether your marketing team uses Cordial for activation – or uses other tools and channels or both to take action on the data – you’ll want to be able to easily move your modeled Cordial data in Snowflake to your most important marketing applications.

If you’ve followed the steps above to properly onboard and model your Cordial data in Snowflake, it’s easy to use reverse ETL (rETL) tools like Census or Hightouch to orchestrate the data from there, or use SoundCommerce native orchestrations to push data into common channels and applications like Facebook, Instagram, TikTok, Braze, Klaviyo, Insider or Dynamic Yield to put the Cordial data in Snowflake to use!

Getting Your Cordial Data Defined, Labeled, Mapped and Modeled in Snowflake is Easy!

SoundCommerce can automate the steps necessary to bring Cordial data into Snowflake, addressing the key functions of raw Cordial data logging, Cordial semantic definitions and mappings, and pre-built Cordial data models that are analytics- and activation ready in Snowflake.

Contact us today to get started with Cordial in Snowflake!

Technical Resources for Integrating
Cordial Data with Snowflake

More information and technical specifications for data collection from Cordial is available at:

Cordial API Documentation

More information and technical specifications for data ingest into Snowflake is available at:

Snowflake API Documentation

Integrate and Model Cordial Data in Snowflake