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Add your source and destination
Connect to 15+ data sources, like Greenplum Database, and 125+ destinations, like Facebook Custom Audiences.
Define your model
Use SQL or select an existing dbt or Looker model.
Sync your data
Define how fields from your model map to Facebook Custom Audiences, and start syncing.
Model your Greenplum Database data using any of these methods
dbt Model Selector
Sync directly with your dbt models saved in a git.
Query using looks. Hightouch turns your look into SQL and will pull from your source.
Create and Edit SQL from your browser. Hightouch supports SQL native to Greenplum Database.
Select available tables and sheets from Greenplum Database and sync using existing views without having to write SQL.
For less technical users, pass traits and audiences from Greenplum Database using our visual segmentation builder.
Where can you sync your Greenplum Database data in Facebook Custom Audiences
The Marketing API allows you to build target Custom Audiences from customer information. This includes email addresses, phone numbers, names, dates of birth, gender, locations, App User IDs, Page Scoped User IDs, Apple's Advertising Identifier (IDFA), or Android Advertising ID.
Does this integration support in-warehouse planning?
Yes, if you integerate Greenplum Database and Facebook Custom Audiences using Hightouch, in-warehouse planning is supported.
Great, but what is in-warehouse planning?
Between every sync, Hightouch notices any and all changes in your data model. This allows you to only send updated results to your destination (in this case Facebook Custom Audiences). With the baseline setup, Hightouch picks out only the rows that need to be synced by querying every row in your data model before diffing using Hightouch’s infrastructure.
The issue here is this can be slow for large models.
Warehouse Planning allows Hightouch to do this diff directly in your warehouse. Read more on how this works here.
Why is it valuable to sync Greenplum Database data to Facebook Custom Audiences?
Thanks to Greenplum Database, it's easier than ever to access your customer data, run complex queries, and segment your customers/users into various categories or audiences.
However, to target users and send conversion data to Facebook Custom Audiences, you need access to all of the unique behavioral data (e.g., last-login date, items in cart, pages viewed, etc.) and core business metrics like lifetime value, workspaces, subscriptions, annual recurring revenue, etc. that lives in Greenplum Database. You then need to be able to package this data to send to Facebook Custom Audiences in the format expected. Plus, you want to do this in a real-time and automated way.
Facebook Custom Audiences is only as good as the data you give them, and if you truly want to optimize your ad spend, increase your match rates, and drive conversions, you need to provide custom data from Greenplum Database within your warehouse.
Maybe you want to retarget users who abandoned their shopping cart in the last seven days, or upload a list of high-value customers to identify potential lookalike audiences, or perhaps you want to upload offline conversion events to reduce your customer acquisition costs and increase your return on ad spend.
Why should you use reverse ETL to connect Greenplum Database and Facebook Custom Audiences data?
In the past, uploading customer data to Facebook Custom Audiences meant hopping back and forth between your various SaaS applications or asking your data team for CSV files. Neither of these options is preferable because marketing teams want to self-serve, and data teams don't enjoy constantly fulfilling one-off marketing requests.
Even worse, if you truly want to optimize your advertising campaigns, you need to be uploading fresh data consistently. Non-fresh data can be expensive for ads. If you're using CSVs to define who to exclude from paid ads and you're uploading that data weekly, that's potentially one week of irrelevant ads. As a workaround, engineering teams will integrate directly with the Facebook Custom Audiences API, and build and maintain custom in-house pipelines. The problem is that a single API change can break everything, and data engineers don't want to spend their time building and maintaining pipelines.
With Hightouch, you can leverage the existing data models and customer segments your engineering team has defined in your warehouse and sync that data directly to your ad platforms in real time. You can schedule your data syncs to run automatically, on a set cadence, or even for the exact duration of your marketing campaign. Hightouch lets your data teams establish the guardrails for your marketers to self-serve and build custom audiences through a drag-and-drop interface.
Upload lists to Facebook to run ads based on certain attributes within your database, such as people who have visited your site
Create lookalike audiences on Facebook using subsets of your users rather than all of them
Continuously fuel your Facebook custom audiences with live data so that data remains fresh
About Greenplum Database
Greenplum reduces data silos by providing you with a single, scale-out environment for converging analytic and operational workloads, like streaming ingestion.
About Facebook Custom Audiences
A Facebook Custom Audience is a targeted advertising service that allows businesses to import information or “identifiers” about leads and customers (such as email and phone number) to retarget them via ads on Facebook.Learn more about Facebook Custom Audiences
Other Greenplum Database Integrations
Other Facebook Custom Audiences Integrations
Hightouch Playbooks: Best practices to leverage reverse ETL
Read more about Hightouch
What is Operational Analytics & Why You Should Use It
Operational Analytics shifts the focus from simply understanding data to taking action on it in the tools that run business processes. Instead of using dashboards to make decisions, Operational Analytics is focused on turning insights into action – automatically.
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This might be one of the greatest inventions for technical marketers since the advent of legacy CDPs back in 2015.
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increase in return on ad spend
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