Baserow and Slack integration

Product Price Monitor with Mailchimp and Baserow ⚠️ COMMUNITY TEMPLATE DISCLAIMER: This is a community-contributed template that uses ScrapeGraphAI (a community node). Please ensure you have the ScrapeGraphAI community node installed in your n8n instance before using this template. This workflow scrapes multiple e-commerce sites for product pricing data, stores the historical prices in Baserow, analyzes weekly trends, and emails a neatly formatted seasonal report to your Mailchimp audience. It is designed for retailers who need to stay on top of seasonal pricing patterns to make informed inventory and pricing decisions. Pre-conditions/Requirements Prerequisites Running n8n instance (self-hosted or n8n cloud) ScrapeGraphAI community node installed Mailchimp account with at least one audience list Baserow workspace with edit rights Product URLs or SKU list from target e-commerce platforms Required Credentials | Credential | Used By | Scope | |------------|---------|-------| | ScrapeGraphAI API Key | ScrapeGraphAI node | Web scraping | | Mailchimp API Key & Server Prefix | Mailchimp node | Sending emails | | Baserow API Token | Baserow node | Reading & writing records | Baserow Table Setup Create a table named price_tracker with the following fields: | Field Name | Type | Example | |------------|------|---------| | product_name | Text | “Winter Jacket” | | product_url | URL | https://example.com/winter-jacket | | current_price | Number | 59.99 | | scrape_date | DateTime | 2023-11-15T08:21:00Z | How it works This workflow scrapes multiple e-commerce sites for product pricing data, stores the historical prices in Baserow, analyzes weekly trends, and emails a neatly formatted seasonal report to your Mailchimp audience. It is designed for retailers who need to stay on top of seasonal pricing patterns to make informed inventory and pricing decisions. Key Steps: Schedule Trigger**: Fires every week (or custom CRON) to start the monitoring cycle. Code (Prepare URLs)**: Loads or constructs the list of product URLs to monitor. SplitInBatches**: Processes product URLs in manageable batches to avoid rate-limit issues. ScrapeGraphAI**: Scrapes each product page and extracts the current price and name. If (Price Found?)**: Continues only if scraping returns a valid price. Baserow**: Upserts the scraped data into the price_tracker table. Code (Trend Analysis)**: Aggregates weekly data to detect price increases, decreases, or stable trends. Set (Mail Content)**: Formats the trend summary into an HTML email body. Mailchimp**: Sends the seasonal price-trend report to the selected audience segment. Sticky Note**: Documentation node explaining business logic in-workflow. Set up steps Setup Time: 10-15 minutes Clone the template: Import the workflow JSON into your n8n instance. Install ScrapeGraphAI: n8n-nodes-scrapegraphai via the Community Nodes panel. Add credentials: a. ScrapeGraphAI API Key b. Mailchimp API Key & Server Prefix c. Baserow API Token Configure Baserow node: Point it to your price_tracker table. Edit product list: In the “Prepare URLs” Code node, replace the sample URLs with your own. Adjust schedule: Modify the Schedule Trigger CRON expression if weekly isn’t suitable. Test run: Execute the workflow manually once to verify credentials and data flow. Activate: Turn on the workflow for automatic weekly monitoring. Node Descriptions Core Workflow Nodes: Schedule Trigger** – Initiates the workflow on a weekly CRON schedule. Code (Prepare URLs)** – Generates an array of product URLs/SKUs to scrape. SplitInBatches** – Splits the array into chunks of 5 URLs to stay within request limits. ScrapeGraphAI** – Scrapes each URL, using XPath/CSS selectors to pull price & title. If (Price Found?)** – Filters out failed or empty scrape results. Baserow** – Inserts or updates the price record in the database. Code (Trend Analysis)** – Calculates week-over-week price changes and flags anomalies. Set (Mail Content)** – Creates an HTML table with product, current price, and trend arrow. Mailchimp** – Sends or schedules the email campaign. Sticky Note** – Provides inline documentation and edit hints. Data Flow: Schedule Trigger → Code (Prepare URLs) → SplitInBatches SplitInBatches → ScrapeGraphAI → If (Price Found?) → Baserow Baserow → Code (Trend Analysis) → Set (Mail Content) → Mailchimp Customization Examples Change scraping frequency // Schedule Trigger CRON for daily at 07:00 UTC 0 7 * * * Add competitor comparison column // Code (Trend Analysis) item.competitor_price_diff = item.current_price - item.competitor_price; return item; Data Output Format The workflow outputs structured JSON data: { "product_name": "Winter Jacket", "product_url": "https://example.com/winter-jacket", "current_price": 59.99, "scrape_date": "2023-11-15T08:21:00Z", "weekly_trend": "decrease" } Troubleshooting Common Issues Invalid ScrapeGraphAI key – Verify the API key and ensure your subscription is active. Mailchimp “Invalid Audience” error – Double-check the audience ID and that the API key has correct permissions. Baserow “Field mismatch” – Confirm your table fields match the names/types in the workflow. Performance Tips Limit each SplitInBatches run to ≤10 URLs to reduce scraping timeouts. Enable caching in ScrapeGraphAI to avoid repeated requests to the same URL within short intervals. Pro Tips: Use environment variables for all API keys to avoid hard-coding secrets. Add an extra If node to alert you if a product’s price drops below a target threshold. Combine with n8n’s Slack node for real-time alerts in addition to Mailchimp summaries.

Automatically track your Android app’s keyword rankings on Google Play. This workflow checks ranks via SerpApi, updates a Baserow table, and posts a heads-up in Slack so your team can review changes quickly. 💡 Perfect for ASO teams tracking daily keyword positions Growth & marketing standups that want quick rank visibility Lightweight historical logging without a full BI stack 🧠 What it does Runs on a schedule (e.g., weekly) Queries SerpApi for each keyword’s Play Store ranking Saves results to Baserow: Current Rank, Previous Rank, Last modified Sends a Slack alert: “Ranks updated — review in Baserow” ⚡ Requirements SerpApi account & API key Baserow account + API token Slack connection (bot/app or credential in n8n) ⚙️ Setup Instructions 1) Create a Baserow table Create a new table (any name). Add user-field names exactly: Keywords (text) Current Rank (number) Previous Rank (number) Last modified (date/time) Optional fields you can add later: Notes, Locale, Store Country, App Package ID. 2) Connect credentials in n8n Baserow: add your API token and select your Database and Table in the Baserow nodes. Slack: connect your Slack account/workspace in the Slack node. SerpApi: open the HTTP Request node and put your API key under Query Parameters → api_key = YOUR_KEY. 3) Verify field mapping In the Baserow (Update Row) node, map: Row ID → {{$json.id}} Current Rank → {{$json["Current Rank"]}} Previous Rank → your code node should set this (the template copies the old “Current Rank” into “Previous Rank” before writing the new one) Last modified → {{$now}} (or the timestamp you compute) 🛟 Notes & Tips If you prefer a single daily Slack summary instead of multiple pings, add a Code node after updates to aggregate lines and send one message. Treat 0 or missing ranks as “not found” and flag them in Slack if helpful. For multi-country tracking, include hl/gl (locale/country) in your SerpApi query params and store them as columns. 🤝 Need a hand? I’m happy to help you get this running smoothly—or tailor it to your brand. Reach out to me via email: [email protected]

This workflow automates the process of monitoring multiple RSS feeds, intelligently identifying new articles, maintaining a record of processed content, and delivering timely notifications to a designated Slack channel. It leverages AI to ensure only truly new and relevant articles are dispatched, preventing duplicate alerts and information overload. 🚀 Main Use Cases Automated News Aggregation:** Continuously monitor industry news, competitor updates, or specific topics from various RSS feeds. 📈 Content Curation:** Filter and deliver only new, unprocessed articles to a team or personal Slack channel. 🎯 Duplicate Prevention:** Maintain a persistent record of seen articles to avoid redundant notifications. 🛡️ Enhanced Information Delivery:** Provide a streamlined and intelligent way to stay updated without manual checking. 📧 How it works The workflow operates in distinct, interconnected phases to ensure efficient and intelligent article delivery: RSS Feed Data Acquisition 📥 Initiation:** The workflow is manually triggered to begin the process. 🖱️ RSS Link Retrieval:** It connects to a Baserow database to fetch a list of configured RSS feed URLs. 🔗 Individual Feed Processing:** Each RSS feed URL is then processed independently. 🔄 Content Fetching & Parsing:** An HTTP Request node downloads the raw XML content of each RSS feed, which is then parsed into a structured JSON format for easy manipulation. 📄➡️🌳 Historical Data Management 📚 Seen Articles Retrieval:** Concurrently, the workflow queries another Baserow table to retrieve a comprehensive list of article GUIDs or links that have been previously processed and notified. This forms the basis for duplicate detection. 🔍 Intelligent Article Filtering with AI 🧠 Data Structuring for AI:** A Code node prepares the newly fetched articles and the list of already-seen articles into a specific JSON structure required by the AI Agent. 🏗️ AI-Powered Filtering:** An AI Agent, powered by an OpenAI Chat Model and supported by a Simple Memory component, receives this structured data. It is precisely prompted to compare the new articles against the historical "seen" list and return only those articles that are genuinely new and unprocessed. 🤖 Output Validation:** A Structured Output Parser ensures that the AI Agent's response adheres to a predefined JSON schema, guaranteeing data integrity for subsequent steps. ✅ JSON Cleaning:** A final Code node takes the AI's raw JSON string output, parses it, and formats it into individual n8n items, ready for notification and storage. 🧹 Notification & Record Keeping 🔔 Persistent Record:** For each newly identified article, its link is saved to the Baserow "seen products" table, marking it as processed and preventing future duplicate notifications. 💾 Slack Notification:** The details of the new article (title, content, link) are then formatted and sent as a rich message to a specified Slack channel, providing real-time updates. 💬 Summary Flow: Manual Trigger → RSS Link Retrieval (Baserow) → HTTP Request → XML Parsing | Seen Articles Retrieval (Baserow) → Data Structuring (Code) → AI-Powered Filtering (AI Agent, OpenAI, Memory, Parser) → JSON Cleaning (Code) → Save Seen Articles (Baserow) → Slack Notification 🎉 Benefits: Fully Automated:** Eliminates manual checking of RSS feeds and Slack notifications. ⏱️ Intelligent Filtering:** Leverages AI to accurately identify and deliver only new content, avoiding duplicates. 💡 Centralized Data Management:** Utilizes Baserow for robust storage of RSS feed configurations and processed article history. 🗄️ Real-time Alerts:** Delivers timely updates directly to your team or personal Slack channel. ⚡ Scalable & Customizable:** Easily adaptable to monitor various RSS feeds and integrate with different Baserow tables and Slack channels. ⚙️ Setup Requirements: Baserow API Key:** Required for accessing and updating your Baserow databases. 🔑 OpenAI API Key:** Necessary for the AI Agent to function. 🤖 Slack Credentials:** Either a Slack OAuth token (recommended for full features) or a Webhook URL for sending messages. 🗣️ Baserow Table Configuration:** A table with an rssLink column to store your RSS feed URLs. A table with a Nom column to store the links of processed articles. For any questions or further assistance, feel free to connect with me on LinkedIn: https://www.linkedin.com/in/daniel-shashko/