Enable Integrations or automations with these events of Paddle and kazm
Detects and returns customers newly created in Paddle since the last check (defaults to the past 15 minutes), ordered newest first.
Trigger when a new address is added for the selected Paddle customer. Returns addresses created since the last check.
Creates a new customer in Paddle.
Update an existing customer using their ID.
Generate an authentication token for a customer.
Create an address for a customer.
Update an address of the customer.
Create a business for a customer.
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Workflow automation is the process of using technology to execute repetitive tasks with minimal human intervention, creating a seamless flow of activities.
To start, connect both your Paddle and kazm accounts to viaSocket. Once connected, you can set up a workflow where an event in Paddle triggers actions in kazm (or vice versa).
Absolutely. You can customize how Paddle 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 Paddle 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 Paddle 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.
Paddle is a comprehensive commerce platform designed to help software companies manage their billing, subscription, and payment processes. It offers a range of tools to streamline revenue operations, including payment processing, tax compliance, and customer management, making it easier for businesses to scale globally.
Learn MoreKazm 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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