The competitive edge, delivered. This Customer Intelligence Engine simultaneously analyzes the web, Reddit, and X/Twitt…
The competitive edge, delivered. This Customer Intelligence Engine simultaneously analyzes the web, Reddit, and X/Twitter to generate a professional, actionable executive briefing. 🎯 Problem Statement Traditional market research for Customer Intelligence (CI) is manual, slow, and often relies on surface-level social media scraping or expensive external reports. Service companies, like HVAC providers, struggle to efficiently synthesize vast volumes of online feedback (Reddit discussions, real-time tweets, web articles) to accurately diagnose systemic service gaps (e.g., scheduling friction, poor automated systems). This inefficiency leads to delayed strategic responses and missed opportunities to invest in high-impact solutions like AI voice agents. ✨ Solution This workflow deploys a sophisticated Multisource Intelligence Pipeline that runs on a scheduled or ad-hoc basis. It uses parallel processing to ingest data from three distinct source types (SERP API, Reddit, and X/Twitter), employs a zero-cost Hybrid Categorization method to semantically identify operational bottlenecks, and uses the Anthropic LLM to synthesize the findings into a clear, executive-ready strategic brief. The data is logged for historical analysis while the brief is dispatched for immediate action. ⚙️ How It Works (Multi-Step Execution) 1. Ingestion and Parallel Processing (The Data Fabric) Trigger:** The workflow is initiated either on an ad-hoc basis via an n8n Form Trigger or on a schedule (Time Trigger). Parallel Ingestion:** The workflow immediately splits into three parallel branches to fetch data simultaneously: SERP API: Captures authoritative content and industry commentary (Strategic Context). Reddit (Looping Structure): Fetches posts from multiple subreddits via an Aggregate Node workaround to get authentic user experiences (Qualitative Signal). X/Twitter (HTTP Request): Bypasses standard rate limits to capture real-time social complaints (Sentiment Signal). 2. Analysis and Fusion (The Intelligence Layer) Cleanup and Labeling (Function Nodes):** Each branch uses dedicated Function Nodes to filter noise (e.g., low-score posts) and normalize the data by adding a source tag (e.g., 'Reddit'). Merge:** A Merge Node (Append Mode) fuses all three parallel streams into a single, unified dataset. Hybrid Categorization (Function Node):** A single Function Node applies the Hybrid Categorization Logic. This cost-free step semantically assigns a pain_point category (e.g., 'Call Hold/Availability') and a sentiment_score to every item, transforming raw text into labeled metrics. 3. Dispatch and Reporting (The Executive Output) Aggregation and Split (Function Node):** The final Function Node calculates the total counts, deduplicates the final results, and generates the comprehensive summaryString. Data Logging:* The aggregated counts and metrics are appended to *Google Sheets** for historical logging. LLM Input Retrieval (Function Node):** A final Function Node retrieves the summary data using the $items() helper (the serial route workaround). AI Briefing:* The *Message a model (Anthropic) Node receives the summaryString and uses a strict HTML System Prompt to synthesize the strategic brief, identifying the top pain points and suggesting AI features. Delivery:* The *Gmail Node** sends the final, professional HTML brief to the executive team. 🛠️ Setup Steps Credentials Anthropic:** Configure credentials for the Language Model (Claude) used in the Message a model node. SERP API, Reddit, and X/Twitter:** Configure API keys/credentials for the data ingestion nodes. Google Services:** Set up OAuth2 credentials for Google Sheets (for logging data) and Gmail (for email dispatch). Configuration Form Configuration:** If using the Form Trigger, ensure the Target Keywords and Target Subreddits are mapped correctly to the ingestion nodes. Data Integrity:** Due to the serial route, ensure the Function (Get LLM Summary) node is correctly retrieving the LLM_SUMMARY_HOLDER field from the preceding node's output memory. ✅ Benefits Proactive CI & Strategy:** Shifts market research from manual, reactive browsing to proactive, scheduled data diagnostic. Cost Efficiency:** Utilizes a zero-cost Hybrid Categorization method (Function Node) for intent analysis, avoiding expensive per-item LLM token costs. Actionable Output:** Delivers a fully synthesized, HTML-formatted executive brief, ready for immediate presentation and strategic sales positioning. High Reliability:** Employs parallel ingestion, API workarounds, and serial routing to ensure the complex workflow runs consistently and without failure. An n8n automation workflow template by Bhuvanesh R.
Bhuvanesh R
web
free
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