Connect Userback and kazm to Build Intelligent Automations
Choose a Trigger
When this happens...
New Feedback
Choose an Action
Automatically do this!
- Request a new Action for kazm
Enable Integrations or automations with these events of Userback and kazm
Enable Integrations or automations with these events of Userback and kazm
Triggers
New Feedback
Triggered when a new feedback is submitted in Userback.
Actions
List Feedback
List of feedback
List Projects
List Available Projects
Create a Feedback
Creates a new feedback entry in Userback.
List Comments
List comments from a feedback
List Members
Lists available Members
List SessionRecordings
Lists available SessionRecordings
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Frequently Asked Questions
To start, connect both your Userback and kazm accounts to viaSocket. Once connected, you can set up a workflow where an event in Userback triggers actions in kazm (or vice versa).
Absolutely. You can customize how Userback data is recorded in kazm. This includes choosing which data fields go into which fields of kazm, setting up custom formats, and filtering out unwanted information.
The data sync between Userback and kazm 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 Userback and kazm. 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 Userback
Userback is a visual feedback and bug tracking tool designed to streamline the process of collecting feedback from users and managing it efficiently. It allows teams to gather annotated screenshots, video recordings, and comments directly from users, making it easier to identify and resolve issues quickly.
Learn MoreAbout kazm
Kazm is the data-driven CRM for Web3. We plug into every community touchpoint to create unified customer profiles, then apply machine learning and other methods to label, segment, and help personalize experiences for users.
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