Sync data from
SQL Server to LinkedIn Ads

Model your SQL Server data using any of these methods

• dbt Model Selector

  

  Sync directly with your dbt models saved in a git.

• SQL Editor

  

  Create and Edit SQL from your browser. Hightouch supports SQL native to SQL Server.

• Table Selector

  

  Select available tables and sheets from SQL Server and sync using existing views without having to write SQL.

• Customer Studio

  

  For less technical users, pass traits and audiences from SQL Server using our visual segmentation builder.

Why is it valuable to sync SQL Server data to LinkedIn Ads?

Thanks to SQL Server, 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 LinkedIn Ads, 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 SQL Server. You then need to be able to package this data to send to LinkedIn Ads in the format expected. Plus, you want to do this in a real-time and automated way.

LinkedIn Ads 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 SQL Server 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 SQL Server and LinkedIn Ads data?

In the past, uploading customer data to LinkedIn Ads 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 LinkedIn Ads 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.