Connect funnelytics and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for funnelytics
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Integrate funnelytics with your favorite apps
The app you are looking for is in beta, and we are awaiting verification from the app builders
before making it accessible to end users.
This process may take 15 to 30 days.
If you can’t wait, we can add the beta version to your viaSocket workspace within 24 hours.
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- text is aiReplytoCommentSend a Private ReplyAdd Step
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- Send for Approval acceptCreate Entry of expensesAdd Step
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Automate Instagram DM Replies, Reactions, and Team Alerts
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Frequently Asked Questions
To start, connect both your funnelytics and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in funnelytics triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how funnelytics data is recorded in TimescaleDB. This includes choosing which data fields go into which fields of TimescaleDB, setting up custom formats, and filtering out unwanted information.
The data sync between funnelytics and TimescaleDB typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.
Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.
Yes, you can set conditional logic to control the flow of data between funnelytics and TimescaleDB. For instance, you can specify that data should only be sent if certain conditions are met, or you can create if/else statements to manage different outcomes.
About funnelytics
Funnelytics is a visual customer journey analytics tool that lets you map your journey on a whiteboard and overlay data to see how it's actually performing.
Learn MoreAbout TimescaleDB
TimescaleDB is a powerful time-series database designed for fast ingest and complex queries, making it ideal for handling time-series data efficiently. It extends PostgreSQL, providing scalability and performance enhancements specifically for time-series workloads.
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