Batch Management and Synchronization of Discord Members by Role

This workflow automates the collection of basic user information from randomly generated users via an API, and it real-time appends the name and country fields to a Google Sheets spreadsheet. It also supports exporting to CSV files. With a fully automated process that requires no manual intervention, it ensures secure data transmission and greatly enhances the efficiency of data collection and management. This workflow is suitable for various scenarios, including market research, software testing, and educational training.

This workflow automatically extracts business information based on specific keywords from Google Maps using Dumpling AI, including name, address, phone number, website, and more. The structured data is then saved to Google Sheets. Users can manually or schedule triggers for this process, eliminating the hassle of manual searches, quickly obtaining potential customer leads, and improving data collection efficiency. It is suitable for various scenarios such as marketing, data analysis, and business development, helping users conduct in-depth market research and customer expansion.

This workflow automatically retrieves checkout session data from the Stripe API for the past 20 days, supporting pagination to obtain complete information. It splits the returned data, focusing on the extraction and filtering of custom fields, allowing users to filter valid session data based on criteria such as nickname and job title. This process significantly enhances the efficiency of data acquisition and analysis, making it suitable for operational and analytical needs in industries such as e-commerce and SaaS, aiding in precise marketing and customer segmentation.

This workflow automates the scraping of company information from Glassdoor and utilizes advanced language models to generate intelligent summaries, providing concise company profile reports. It integrates functions such as data scraping, status polling, and text processing, enabling efficient and accurate extraction and summarization of web information. This addresses the cumbersome issues of traditional manual collection and analysis processes, making it suitable for fields such as human resources, recruitment, and market research. It enhances information processing efficiency and helps users make more informed decisions.

This workflow aims to automate the acquisition of visitor data from the Matomo web analytics tool, focusing on user behavior for those who have visited more than three times. Through advanced AI models, in-depth analysis is conducted to generate SEO optimization recommendations, and the results are systematically stored in a Baserow database. This process not only enhances the efficiency of data analysis but also provides professional insights and optimization strategies for website operations teams, SEO experts, and small businesses, helping to accurately identify areas for website improvement and enhance traffic quality and conversion effectiveness.

This workflow automatically retrieves all order data by calling Squarespace's Commerce API and accurately synchronizes it to Google Sheets. Users can set filtering criteria such as query time range and order status to achieve personalized data synchronization, supporting both manual triggers and scheduled tasks for real-time order information updates. This process simplifies the order data export procedure, prevents data omissions, and enhances the data management efficiency of the e-commerce operations team, facilitating subsequent analysis and decision-making.

This workflow automates the collection and reporting of Google Analytics data, covering dimensions such as website page engagement, search performance, and country visit data. By comparing this week's data with last week's, users can quickly gain insights into traffic changes and user behavior trends, enhancing the efficiency of data-driven decision-making. The final report is sent in a formatted HTML email, making it concise and intuitive, easy to share and archive, and suitable for users such as marketing teams, data analysts, and corporate management.

This workflow can automatically retrieve data from Phantombuster and organize key information such as names, emails, and companies before storing it in an Airtable database. Users only need to manually trigger the execution, which simplifies the data extraction and storage process, enhancing the automation and efficiency of data management. It addresses the cumbersome nature of traditional manual data organization and uploading, improving data accuracy and making it suitable for various scenarios such as market research and recruitment, helping teams efficiently manage external data.