n8n Workflow: Social Media Ad Performance Monitor & Alert

This n8n workflow demonstrates a basic structure for monitoring and alerting on social media ad performance. While the provided JSON defines a foundational setup, it currently serves as a template, showcasing how to integrate AI agents, conditional logic, and communication channels.

The workflow is designed to be highly extensible, allowing users to integrate real-time ad performance data from platforms like Bright Data's Marketing Campaign Performance (MCP) and trigger alerts based on defined thresholds.

What it does

This workflow outlines the following key steps:

1. Scheduled Trigger: The workflow is initiated on a regular schedule (e.g., daily, hourly) to check ad performance.
2. AI Agent (Planned Integration): An AI Agent is included, suggesting the future integration of advanced logic for analyzing ad performance data, identifying trends, or predicting potential issues.
3. Code (Data Transformation): A Code node is present, likely for processing or transforming raw ad performance data into a format suitable for analysis or alerting.
4. Conditional Logic (If): An "If" node allows for conditional routing based on specific criteria (e.g., if ad spend exceeds a threshold, if CTR drops below a target).
   • True Branch (Alerting): If the condition is met (e.g., a critical performance issue is detected), the workflow proceeds to send an email alert via Gmail.
   • False Branch (No Action): If the condition is not met, a "No Operation" node signifies that no immediate action is required.
5. Data Manipulation (Edit Fields): A "Set" node is included for editing or setting specific fields within the data, potentially for formatting alerts or preparing data for subsequent steps.
6. AI Language Models & Parsers:
   • OpenAI Chat Model: An OpenAI Chat Model is included, indicating the intention to use a large language model for tasks like summarizing performance, generating alert messages, or providing recommendations.
   • Auto-fixing Output Parser & Structured Output Parser: These nodes suggest that the workflow intends to parse and validate the output from the AI models, ensuring data is in a structured and usable format, even attempting to self-correct if the initial output is malformed.
7. Sticky Note: A sticky note is present, likely for documentation or temporary notes within the workflow design.

Prerequisites/Requirements

To fully utilize and extend this workflow, you would typically need:

• n8n Instance: A running n8n instance.
• Bright Data MCP (or similar Ad Platform): (Implied by the directory name, but not directly in JSON) Access to an ad performance monitoring tool or API to retrieve actual ad data.
• Gmail Account: For sending email alerts. This requires a configured Gmail credential in n8n.
• OpenAI API Key: For the OpenAI Chat Model and potentially the output parsers, requiring an OpenAI credential in n8n.

Setup/Usage

1. Import the Workflow:
   • Copy the provided JSON code.
   • In your n8n instance, go to "Workflows" and click "New".
   • Click the "Import" button (usually a cloud icon with an arrow pointing down) and paste the JSON.
   • Click "Import".
2. Configure Credentials:
   • Locate the "Gmail" node and configure your Gmail account credentials.
   • Locate the "OpenAI Chat Model" node and configure your OpenAI API Key credentials.
3. Customize Nodes:
   • Schedule Trigger: Adjust the schedule in the "Schedule Trigger" node to your desired frequency (e.g., every 1 hour, once a day).
   • AI Agent: Configure the AI Agent with specific instructions for analyzing ad performance data. This would involve defining tools the agent can use (e.g., to query ad platforms) and its reasoning process.
   • Code: Modify the "Code" node to process the specific ad performance data you will be ingesting.
   • If Node: Define the conditions in the "If" node that determine when an alert should be triggered (e.g., {{ $json.ctr < 0.01 }} for a Click-Through Rate below 1%).
   • Gmail: Customize the subject and body of the email in the "Gmail" node to include relevant ad performance metrics and alert messages. You can use expressions to dynamically insert data from previous nodes.
   • OpenAI Chat Model: Configure the prompt for the OpenAI model to generate insightful summaries or alert messages based on your ad data.
4. Connect Data Source (Extensible Step):
   • To make this workflow functional, you would need to add a node before the "Code" node to fetch actual ad performance data from your chosen platform (e.g., HTTP Request node to an API, a dedicated ad platform node if available in n8n). The output of this data fetching node would then feed into the "Code" node for processing.
5. Activate the Workflow: Once configured, activate the workflow to start monitoring and alerting.