How to Extract Executive Data with TypeScript

Gathering executive data manually from various online platforms is tedious and time-consuming, often resulting in incomplete or inaccurate datasets due to restrictive APIs, unreliable scraping methods, frequent website changes, or stringent login requirements. Technical teams face challenges in obtaining structured, reliable executive information that can meaningfully drive networking, outreach, and competitive intelligence initiatives.

This Airtop automation, utilizing TypeScript, resolves those challenges by leveraging robust real browser sessions to ensure accurate extraction even with authenticated sites. It supports complex login scenarios like OAuth, 2FA, and Captcha, delivering consistently reliable and structured executive data outputs in JSON format. Automated extraction significantly streamlines data collection processes, ensures continuous data quality, and empowers technical teams to focus on analysis and strategic initiatives rather than data entry tasks.

Who is this Automation for?

• Automation engineers seeking scalable data extraction solutions

• Developers integrating executive data into business intelligence systems

• Data analysts conducting competitive analysis or market research

• Technical sales teams building targeted lead generation campaigns

Key Benefits

• Real browser sessions for accurate and reliable data scraping

• Structured JSON output for easy data manipulation and integrations

• Built-in authentication support (OAuth, 2FA, and Captcha)

• TypeScript compatibility for modern, maintainable automation workflows

Use Cases

• Extracting structured information from LinkedIn executive profiles

• Collecting executive data for targeted outbound networking efforts

• Automating competitive intelligence data gathering for business strategy

• Building comprehensive, continuously updated executive databases for lead generation

Getting Started with the Extract Executive Data Automation

Quickly automate your executive data extraction workflows using Airtop and TypeScript.

How the Extract Executive Data Automation Works

This automation initiates secure, authenticated browser sessions to access executive profiles on platforms like LinkedIn or corporate directories. Utilizing TypeScript scripts with Airtop's real browser automation, it reliably logs in (including handling OAuth flows, two-factor authentication, or Captchas), navigates to relevant profile pages, extracts structured executive details like name, title, company, education, and contact information, and outputs clean, consistent JSON data ready for downstream integration and analysis.

What You'll Need

• An Airtop account for browser automation

• Node.js environment configured with TypeScript

• Valid login credentials for targeted platforms

• Target URLs or profiles for data extraction

Setting Up the Automation

1. Log into your Airtop dashboard and create a new executive data automation project.

2. Configure authentication methods within Airtop (OAuth, 2FA credentials, Captcha settings).

3. Install Airtop's TypeScript client library using npm within your project environment.

4. Write (or modify existing) structured TypeScript automation scripts targeting executive profiles.

5. Run the automation script to begin generating structured executive data output.

Customize the Automation

Tailor your Airtop Executive Data Automation to precisely match your business objectives by leveraging the clean and configurable TypeScript environment. Examples include:

• Modifying scraped data points like executive tenure or educational background details.

• Adding new target platforms or websites into your TypeScript script for broader data extraction.

• Adapting data extraction frequency for continuous monitoring or periodic reporting.

• Integrating additional data processing steps within TypeScript to enrich and categorize extracted executive information.

Automation Best Practices

• Regularly update authentication credentials and browser automation scripts to accommodate any platform changes.

• Implement robust error-handling in TypeScript scripts to avoid automation disruptions.

• Define clear JSON schemas for extracted data, enabling consistent integration across tools.

• Maintain compliance by respecting target platform terms of use and scraping guidelines.