How to connect Kintone and LinkedIn Data Scraper
Create a New Scenario to Connect Kintone and LinkedIn Data Scraper
In the workspace, click the “Create New Scenario” button.

Add the First Step
Add the first node – a trigger that will initiate the scenario when it receives the required event. Triggers can be scheduled, called by a Kintone, triggered by another scenario, or executed manually (for testing purposes). In most cases, Kintone or LinkedIn Data Scraper will be your first step. To do this, click "Choose an app," find Kintone or LinkedIn Data Scraper, and select the appropriate trigger to start the scenario.

Add the Kintone Node
Select the Kintone node from the app selection panel on the right.

Kintone
Add the LinkedIn Data Scraper Node
Next, click the plus (+) icon on the Kintone node, select LinkedIn Data Scraper from the list of available apps, and choose the action you need from the list of nodes within LinkedIn Data Scraper.

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Authenticate LinkedIn Data Scraper
Now, click the LinkedIn Data Scraper node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your LinkedIn Data Scraper settings. Authentication allows you to use LinkedIn Data Scraper through Latenode.
Configure the Kintone and LinkedIn Data Scraper Nodes
Next, configure the nodes by filling in the required parameters according to your logic. Fields marked with a red asterisk (*) are mandatory.
Set Up the Kintone and LinkedIn Data Scraper Integration
Use various Latenode nodes to transform data and enhance your integration:
- Branching: Create multiple branches within the scenario to handle complex logic.
- Merging: Combine different node branches into one, passing data through it.
- Plug n Play Nodes: Use nodes that don’t require account credentials.
- Ask AI: Use the GPT-powered option to add AI capabilities to any node.
- Wait: Set waiting times, either for intervals or until specific dates.
- Sub-scenarios (Nodules): Create sub-scenarios that are encapsulated in a single node.
- Iteration: Process arrays of data when needed.
- Code: Write custom code or ask our AI assistant to do it for you.

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AI Anthropic Claude 3
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LinkedIn Data Scraper
Trigger on Webhook
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Webhook response
Save and Activate the Scenario
After configuring Kintone, LinkedIn Data Scraper, and any additional nodes, don’t forget to save the scenario and click "Deploy." Activating the scenario ensures it will run automatically whenever the trigger node receives input or a condition is met. By default, all newly created scenarios are deactivated.
Test the Scenario
Run the scenario by clicking “Run once” and triggering an event to check if the Kintone and LinkedIn Data Scraper integration works as expected. Depending on your setup, data should flow between Kintone and LinkedIn Data Scraper (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.
Most powerful ways to connect Kintone and LinkedIn Data Scraper
LinkedIn Data Scraper + Kintone + Slack: When LinkedIn Data Scraper finds a new profile based on search criteria, the profile information is added as a new record in Kintone. Slack then sends a message to the sales team to notify them of the new potential lead.
LinkedIn Data Scraper + Kintone + Google Sheets: This flow scrapes LinkedIn profiles based on defined search parameters. The scraped data is then logged into a Google Sheet for record-keeping, and relevant lead information is simultaneously used to create a new record in Kintone.
Kintone and LinkedIn Data Scraper integration alternatives
About Kintone
Integrate Kintone into Latenode to automate custom application workflows. Sync data, trigger actions, and manage processes without code. Extend Kintone's capabilities with Latenode's visual builder, JavaScript functions, and AI tools to automate data validation, reporting, and notifications. Scale efficiently and only pay for what you use.
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About LinkedIn Data Scraper
Need LinkedIn data for leads or market insights? Automate scraping profiles and company info inside Latenode workflows. Extract data, enrich it with AI, then push it to your CRM or database. Latenode's visual editor and affordable pricing make data-driven outreach scalable and cost-effective.
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FAQ Kintone and LinkedIn Data Scraper
How can I connect my Kintone account to LinkedIn Data Scraper using Latenode?
To connect your Kintone account to LinkedIn Data Scraper on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Kintone and click on "Connect".
- Authenticate your Kintone and LinkedIn Data Scraper accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I automatically update Kintone records with LinkedIn profile data?
Yes, you can! Latenode's visual editor and JavaScript blocks allow for sophisticated data transformation and real-time Kintone record updates based on scraped LinkedIn Data Scraper profiles, improving data accuracy.
What types of tasks can I perform by integrating Kintone with LinkedIn Data Scraper?
Integrating Kintone with LinkedIn Data Scraper allows you to perform various tasks, including:
- Enriching Kintone lead records with up-to-date LinkedIn profile information.
- Automating outreach to potential clients based on Kintone record updates.
- Building comprehensive contact databases by scraping LinkedIn profiles.
- Monitoring competitor employee movements and updating Kintone records.
- Generating reports on industry trends based on LinkedIn Data Scraper data in Kintone.
How can I trigger Kintone workflows from LinkedIn Data Scraper events?
Latenode lets you trigger Kintone workflows from LinkedIn Data Scraper events using webhooks. This allows for instant automation based on scraped data.
Are there any limitations to the Kintone and LinkedIn Data Scraper integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- LinkedIn Data Scraper's API limits the number of profiles scraped per hour.
- Kintone API call limits may impact high-volume data transfer workflows.
- Complex data transformations might require JavaScript coding expertise.