
When this happens...

Automatically do this!
Enable Integrations or automations with these events of IP2Location.io and kazm
Obtain domain information by domain name
Retrieves geolocation data for an IP address like country, region, city and more.
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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 IP2Location.io and kazm accounts to viaSocket. Once connected, you can set up a workflow where an event in IP2Location.io triggers actions in kazm (or vice versa).
Absolutely. You can customize how IP2Location.io 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 IP2Location.io 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 IP2Location.io 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.
IP2Location.io is a geolocation service that provides information about the geographical location of an IP address. It offers a range of services and APIs that enable users to identify the location, network, and other relevant data associated with an IP address. This information can be valuable for various purposes, such as targeting advertisements, preventing fraud, analyzing website traffic, and enhancing cybersecurity measures. IP2Location.io utilizes a vast database of IP address data, continually updated to ensure accuracy and reliability in its geolocation services.
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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