This article provides a complete, practical walkthrough of the Wait Filter Create Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   Automated Competitor Review Analysis with n8n, Bright Data & OpenAI: From Trustpilot to Ad Copy in Minutes
   
   Meta Description:  
   Discover how to use this powerful n8n workflow to automatically collect negative competitor reviews from Trustpilot, analyze them with Bright Data and OpenAI, and generate Facebook ad copy and insights for your marketing team—all sent directly to your inbox.
   
   Keywords:  
   n8n workflow, Trustpilot scraper, Bright Data, OpenAI GPT-4o-mini, competitor analysis, Facebook ads copy, marketing automation, review mining, Google Sheets, AI marketing, GPT ad generator
   
   Third-Party APIs Used:
   
   - Bright Data API (https://api.brightdata.com): Used to scrape and retrieve Trustpilot reviews based on competitor URL and timeframe.
   - OpenAI GPT-4o-mini via LangChain: Used to generate Facebook ad copy by analyzing aggregated negative competitor reviews.
   - Google Sheets API: Used to log all collected review data for record-keeping and reference.
   - Gmail API (via Gmail OAuth2): Sends an email summary with insights and ad copy suggestions to the marketing team.
   
   Article:
   
   How to Automate Competitor Review Analysis and Generate Ad Copy Using n8n, Bright Data & OpenAI
   
   Staying ahead of competitors is a never-ending battle in digital marketing. One of the most revealing sources of customer sentiment is public reviews—and few platforms are as rich in qualitative feedback as Trustpilot. But manually analyzing 1-star and 2-star reviews? That’s where automation becomes a game-changer.
   
   This article explores a fully automated workflow powered by n8n, Bright Data, and OpenAI, designed to seamlessly convert negative customer reviews about your competitors into actionable marketing insights and compelling Facebook ad copy.
   
   Let’s break down how it works and why it’s a no-fluff solution for performance marketers and growth hackers alike.
   
   🛠️ Step 1: Collect Competitor Info via a Form Trigger
   
   The workflow begins with an n8n Form Trigger step labeled “Please Paste the URL of Your Trustpilot Competitor.” Here, the user inputs the Trustpilot URL of a competing brand and selects a desired timeframe for scraping reviews: last 30 days, 3 months, 6 months, or 12 months.
   
   This component makes the workflow flexible and fully user-driven, meaning it doesn’t need to be edited every time a new competitor is being researched.
   
   🔎 Step 2: Fetch Reviews with Bright Data
   
   Once the form is submitted, the workflow uses Bright Data's Dataset API to initiate a data collection job based on the provided URL and selected date range. The API request includes:
   
   - dataset_id for Trustpilot reviews
   - competitor’s Trustpilot URL
   - selected date range
   
   The workflow then polls Bright Data every 2 minutes using a Wait node until the data snapshot is ready. Once confirmed, the full set of reviews is retrieved via another Bright Data API call in JSON format.
   
   🧾 Step 3: Store Data in Google Sheets
   
   As soon as the dataset is ready, all reviews—both positive and negative—are automatically appended to a pre-formatted Google Sheet. This acts as a historical log and source of record for the collected data.
   
   The provided Google Sheet template facilitates quick launching of the workflow. You just make a copy and plug its ID into the relevant n8n node.
   
   🧼 Step 4: Filter and Aggregate the Bad Stuff
   
   The real marketing gold lies in unhappy customers. The next segment of the workflow filters for poor reviews—specifically those with 1-star or 2-star ratings. These reviews are aggregated into a single text block, forming a structured snapshot of customer pain points toward a specific competitor.
   
   This is the nugget that fuels your ad copy.
   
   🧠 Step 5: Generate Facebook Ad Copy Using OpenAI
   
   This is where AI meets actionable insight. The aggregated negative reviews are passed to OpenAI’s GPT-4o-mini model via Langchain’s LLM chain inside n8n.
   
   The prompt is designed to:
   
   - Summarize competitor weaknesses
   - Avoid ever mentioning the competitor brand
   - Generate three unique Facebook ad variants that tactfully address the frustrations users experienced with your competitors
   
   You can modify this prompt to generate UGC scripts, email flows, product FAQ ideas, or even landing page copy—making the workflow highly adaptable.
   
   📧 Step 6: Email the Summary to Your Marketing Team
   
   In the final step, the workflow composes and sends a summary email using the Gmail API (OAuth2 authenticated). The email includes:
   
   - The original competitor Trustpilot URL
   - A summary of consumer pain points
   - Three suggested Facebook ad copies
   - The full block of aggregated negative reviews for reference
   
   This ensures that both strategic decision-makers and creative teams have immediate, actionable information.
   
   Why This Workflow Matters
   
   ✅ Empowers lean marketing teams  
   ✅ Monitors competitor sentiment at scale  
   ✅ Saves hours of manual research and analysis  
   ✅ Uses real customer language for more emotive ad content  
   ✅ Fully automates delivery to your inbox or CRM
   
   Final Thoughts
   
   This n8n workflow is more than a scraper—it’s an insight engine and ad copy generator wrapped in one elegant automation. By connecting third-party APIs like Bright Data, OpenAI, Google Sheets, and Gmail, it transforms raw customer complaints into a source of marketing brilliance.
   
   Whether you’re launching a direct-to-consumer brand or optimizing ad spend for a storefront, this automated review-to-ad system streamlines one of the most labor-intensive aspects of competitive marketing research.
   
   Try it, tweak it, and make your next ad campaign not just data-informed—but data-inspired.
   
   For workflow assistance, reach out to Yaron at yaron@nofluff.online and explore tutorials via his YouTube channel or LinkedIn.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.