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Add your source and destination
Connect to 15+ data sources, like ClickHouse, and 125+ destinations, like Bloomreach.
Define your model
Use SQL or select an existing dbt or Looker model.
Sync your data
Define how fields from your model map to Bloomreach, 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 Bloomreach?
Lifecycle marketing platforms like Bloomreach are built on omnichannel experiences and multiple touch points. They allow marketers to experiment and iterate at a moment's notice. However, to build personalized experiences for your customers, you first need access to the rich behavioral data in your data warehouse.
This might include data models built around unique objects in your business like workspaces, subscriptions, playlists, or even custom audiences your data team has defined based on core metrics like items in cart, pages viewed, average order value, time spent in-app, etc.
To truly convert new users and retain existing customers, you need to build customized experiences that feel unique to each user. Usually, this includes sending custom emails, SMS messages, push notifications, or even web notifications.
For example, if you want to encourage users who abandoned their shopping cart in the last seven days to complete their purchase, you might want to send an email letting them know your stock is running low. Maybe you want to target new users with a special offer through an in-app/on-browser notification, or perhaps you simply want to send an SMS notifying your customers that their order has shipped.
Either way, the only complete 360-degree view of your customer lives in your warehouse. Your lifecycle marketing tool is only as good as the data you give it. If you want to offer the best possible experience to your customers, you need to take advantage of the data in your warehouse.
Why should you use reverse ETL to connect ClickHouse and Bloomreach data?
Whenever your marketing team wants to launch a new campaign, the typical process is to request a specific data set from your data team (e.g., can you give a list of every user who's visited our pricing page over the last seven days?) One-off requests like these are frequent, and each one pulls away from high-value work your data team could be doing.
To facilitate this, your data engineers must download ad-hoc CSV files so your marketing team can upload that data into Bloomreach. Alternatively, your data team might build and maintain custom pipelines to consistently ingest that data into Bloomreach. The problem is CSV files aren't fresh, and data pipelines are prone to failure because integrating with third-party APIs is hard.
Ultimately, your marketers want to self-serve, and that's only possible with Reverse ETL. With Hightouch, you can leverage your existing data models (e.g., lifetime value, last-login date, workspaces, annual recurring revenue, etc.) in your warehouse and sync that data directly to fields in Bloomreach. You can schedule your syncs to run sequentially or define a set cadence (e.g., running your syncs while your marketing campaign is in progress.)
Hightouch has a visual audience builder so your marketers can self-serve and build custom audiences using the parameters your data team has set. If any of your rows fail, Hightouch will automatically retry them later in the next sync. You can easily view all of your API payload requests/responses in a live debugger, and your sync logs can be written back directly to Snowflake. Ultimately your lifecycle marketing tool is only as good as the data you give it.
Upload customer attributes to build attribute-based audiences
Send custom events associated to a user with custom properties
Other ClickHouse Integrations
Other Bloomreach 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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