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Customer Pain Analysis & AI Briefing with Anthropic, Reddit, X, and SerpAPI

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Created by: Bhuvanesh R || bhuvanesh

Bhuvanesh R

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Last update 4 hours ago

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🎯 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.