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Add your source and destination
Connect to 15+ data sources, like Firebolt, and 125+ destinations, like Amazon 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 Amazon Ads, and start syncing.
Model your Firebolt data using any of these methods
dbt Model Selector
Sync directly with your dbt models saved in a git.
Create and Edit SQL from your browser. Hightouch supports SQL native to Firebolt.
Select available tables and sheets from Firebolt and sync using existing views without having to write SQL.
Why is it valuable to sync Firebolt data to Amazon Ads?
Thanks to Firebolt, 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 Amazon 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 Firebolt. You then need to be able to package this data to send to Amazon Ads in the format expected. Plus, you want to do this in a real-time and automated way.
Amazon 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 Firebolt 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 Firebolt and Amazon Ads data?
In the past, uploading customer data to Amazon 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 Amazon 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 audiences to Amazon Ads to run ads based on certain attributes within your database, such as people who have visited your site
Continuously fuel your Amazon Ads custom audiences with live data so that data never goes stale
Deliver production grade data applications & analytics with Firebolt - the cloud data warehouse for modern data engineering & dev teamsLearn more about Firebolt
About Amazon Ads
Advertising with Amazon Ads can help you reach customers at every stage of their journey. Amazon Ads offerings help you achieve your advertising goals while helping customers find the products they are looking for.Learn more about Amazon Ads
Other Firebolt Integrations
Other Amazon Ads 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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increase in return on ad spend
improvement in email engagement
lift in customer acquisition