Sync data from
Snowflake to LinkedIn Ads
Connect your data from Snowflake to LinkedIn Ads with Hightouch. No APIs, no months-long implementations, and no CSV files. Just your data synced forever.
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Integrate your data in 3 easy steps
Add your source and destination
Connect to 15+ data sources, like Snowflake, and 140+ destinations, like LinkedIn Ads.
Define your model
Use SQL or select an existing dbt or Looker model.
Sync your data
Define how fields from your model map to LinkedIn Ads, and start syncing.
Model your Snowflake 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 Snowflake.
Select available tables and sheets from Snowflake and sync using existing views without having to write SQL.
For less technical users, pass traits and audiences from Snowflake using our visual segmentation builder.
Where can you sync your Snowflake data in LinkedIn Ads
LinkedIn Audiences is a set of targeting capabilities to combine LinkedIn’s data with your own data to reach the right audiences on LinkedIn.
Does this integration support in-warehouse planning?
Yes, if you integerate Snowflake and LinkedIn Ads 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 LinkedIn Ads). 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 Snowflake data to LinkedIn Ads?
Thanks to Snowflake, 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 Snowflake. 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 Snowflake 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 Snowflake 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.
Upload lists to LinkedIn to run ads based on certain attributes within your database, such as people who have visited your site
Create lookalike audiences on LinkedIn using subsets of your users rather than all of them
Continuously fuel your LinkedIn custom audiences with live data so that data never goes stale
Snowflake is a managed cloud data warehouse that helps you consolidate and aggregate your data into a single, centralized platform to tackle analytics use cases. These workloads include data warehousing, data lakes, data engineering, application development, data sharing, and business intelligence.Learn more about Snowflake
About LinkedIn Ads
LinkedIn is a social media network focused on professional relationship, business listings, and employment opportunities. The site offers marketers and recruiters the ability to advertise on the platform through paid social posts, in-mail and more.Learn more about LinkedIn Ads
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