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Add your source and destination
Connect to 15+ data sources, like ClickHouse, and 125+ destinations, like Twitter 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 Twitter Ads, and start syncing.
Model your ClickHouse data using any of these methods
Create and Edit SQL from your browser. Hightouch supports SQL native to ClickHouse.
Why is it valuable to sync ClickHouse data to Twitter Ads?
Thanks to ClickHouse, 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 Twitter 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 ClickHouse. You then need to be able to package this data to send to Twitter Ads in the format expected. Plus, you want to do this in a real-time and automated way.
Twitter 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 ClickHouse 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 ClickHouse and Twitter Ads data?
In the past, uploading customer data to Twitter 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 Twitter 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 Twitter to run ads based on certain attributes within your database, such as people who have visited your site
Create lookalike audiences on Twitter using subsets of your users rather than all of them
Continuously fuel your Twitter custom audiences with live data so that data never goes stale
ClickHouse is a fast open-source column-oriented database management system that allows generating analytical data reports in real-time using SQL queries.Learn more about ClickHouse
About Twitter Ads
Twitter is a microblogging and social networking service on which users post and interact with messages known as "tweets". Registered users can post, like, and retweet tweets, but unregistered users can only read those that are publicly available.Learn more about Twitter Ads
Other ClickHouse Integrations
Other Twitter 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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