Wayfair Rugs Market Intelligence
AI Agent Demo
Final Externship Presentation
A strategic automation system for tracking rug trends, monitoring competitors, generating content ideas, and bringing insights together in one dashboard.
Presenter
Pavel Tsafack
jiofacktsafackpavel@gmail.com
Cohort: [Add cohort]
PT
Wayfair Rugs Market Intelligence • AI Agent Demo
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Executive Summary
A connected AI market-intelligence system for Wayfair’s Rugs Category team.
4
connected workflows
3
core reports
1
integrated dashboard
30
sample products analyzed
At a glance
• Objective: Help the Rugs team identify trends, compare competitors, and turn insights into strategic content and merchandising recommendations.
• Solution: Built Trend Discovery, Competitor Monitoring, AI Insights & Content Strategy, and Market Intelligence Dashboard workflows in n8n.
• Market insight: Warm, textured, nature-inspired and washable rugs show strong potential for customer education and assortment positioning.
• Competitive insight: Wayfair differentiates through design-led assortment; Amazon emphasizes practicality; Walmart competes on price.
• Future improvements: Connect live SKU, sales, inventory, search, and review data; automate refreshes; add alerts and confidence scores.
Core Outputs
Trend Discovery Agent
Competitor Monitoring Agent
AI Insights & Content Agent
Integrated Dashboard
Submission links
Add Google Drive links for JSON workflows and HTML outputs before submitting.
Business value
Turns disconnected reports into a repeatable decision-support process for category, merchandising, and content teams.
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Agent 1: Moodboard Generator
Optional sample agent format — visual inspiration for rug merchandising and creative planning.
🎯 Objective
Generate visual inspiration for rug merchandising and content planning based on style, palette, and room context.
🧠 Input Prompt Example
Create a neutral organic modern moodboard for area rugs using beige, gray, taupe, and natural textures for a living room.
⚠️ Keep in Mind
Best results come from clear style, room type, color palette, and product-use context. Visuals may require brand review.
💡 Improvements
Upgrade the image API, add Wayfair product-image constraints, and generate captions directly from the final visual theme.
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Agent 2: Trend Discovery Agent
Scans real-world signals to identify what is rising in popularity and why.
n8n workflow screenshot
🎯 Objective
Analyze product, social, editorial, and market signals to identify emerging rug trends and translate them into actionable recommendations.
🧠 Input Prompt Example
Area Rug
Optional: focus: washable
⚠️ Keep in Mind / Input Notes
Supported categories: Area Rug, Outdoor Rug, Hallway Runner, and Shag Rug. Results depend on API/source availability and may take several minutes.
🖼 Output Example / Key Findings
• Warm, textured, nature-inspired rugs show strong attention.
• Earthy neutrals, organic textures, and layered styling appear as key directions.
• Opportunities include educational content and assortment focus around practical, livable rugs.
💡 Improvements
Connect Wayfair SKU/search data, add more sources, schedule recurring trend refreshes, and include confidence scoring.
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Agent 3: Competitor Monitoring Agent
Compares Wayfair against competitor listings, pricing, reviews, and assortment strategy.
n8n workflow screenshot
🎯 Objective
Collect and analyze competitor product listings, pricing, reviews, and feature signals to surface whitespace opportunities for Wayfair.
🧠 Input Prompt Example
Area Rug
Optional: focus: washable
⚠️ Keep in Mind / Input Notes
Compares Wayfair, Amazon, and Walmart samples. This is a market snapshot; recurring runs are needed for true price-movement tracking.
🖼 Output Example / Key Findings
• Wayfair is strongest in design-led and premium positioning.
• Amazon emphasizes washable, stain-resistant, non-slip practical rugs.
• Walmart competes aggressively on value and entry-level pricing.
💡 Improvements
Add scheduled monitoring, review sentiment, more retailers, historical price tracking, and automated alerts for competitor changes.
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Agent 4: AI Insights & Content Agent
Turns market intelligence into content strategy, campaigns, captions, and recommendations.
n8n workflow screenshot
🎯 Objective
Convert Trend Discovery and Competitor Monitoring reports into a practical Wayfair content strategy for customer education and engagement.
🧠 Input Prompt Example
Product Category: Area Rug
Focus Area: Washable
Uploads: P2 Trend Report + P3 Competitor Report
⚠️ Keep in Mind / Input Notes
Requires both HTML reports. More specific audience, season, and content-goal inputs produce more targeted recommendations.
🖼 Output Example / Key Findings
• Generated 6 content ideas across blog, video, Pinterest, Instagram, buying guide, and email.
• Produced 3 campaign concepts and platform-specific captions.
• Highlighted care, styling, and buying-guide content gaps.
💡 Improvements
Validate every metric, add source citations, connect to content calendars, and create audience-specific versions.
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Market Intelligence Dashboard
Integrated view that brings trend and competitor intelligence into one HTML dashboard.
🎯 Objective
Give the Rugs team one polished place to review key metrics, trend signals, competitor comparisons, risks, opportunities, and recommended actions.
⚙️ How It Works
• User enters a product category and uploads P2 Market Trend + P3 Competitor reports.
• Workflow fetches a dashboard template and extracts the uploaded HTML files.
• Parser organizes market metrics, competitor prices, risks, suppliers, and opportunities.
• Output is assembled into a responsive, downloadable HTML dashboard.
Dashboard Sections
• Executive Overview
• Market & Trends
• Competitive Intel
• Opportunity Radar
• Risk & Diagnostics
• Action Center
⚠️ Implementation Notes
The dashboard is a snapshot. To refresh insights, run the workflow again with updated reports. Parser accuracy depends on consistent HTML structure.
💡 Improvements
Schedule refreshes, host securely online, add live Wayfair data, track history, and trigger alerts when signals cross thresholds.
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Reflections & Future Improvements
What I learned from building a real business-facing automation system.
🧭 Key Learnings
• End-to-end workflow design: I connected data collection, AI analysis, validation, file parsing, and HTML generation.
• Prompt engineering matters: structured prompts and JSON outputs made the results easier to control and reuse.
• Business value comes from synthesis: data matters most when converted into opportunities and decisions.
• Reliability requires safeguards: missing data, API limits, and AI claims need validation and fallback logic.
🚀 Future Improvements / Next Steps
• Connect to live Wayfair SKU, sales, inventory, search, and customer-review data.
• Schedule automatic refreshes and store historical results over time.
• Add citations, confidence scores, and validation checks for every recommendation.
• Deploy the dashboard online with secure access, alerts, filters, and category comparisons.
💬 Personal Reflection
This externship helped me grow from building individual automations to designing a connected AI market-intelligence system. I strengthened my skills in n8n, prompt engineering, data processing, debugging, and dashboard development while learning to connect technical workflows to real business decisions.
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Thank you
I appreciate the opportunity to present this AI agent system and the business value it can create for Wayfair’s Rugs strategy.
Contact
Pavel Tsafack
jiofacktsafackpavel@gmail.com
Optional Links
Full workflow folder — https://docs.google.com/document/d/1I8Mze9wLwVh2CIU3oyMKHoxpecjaIdk5pTPnXhXhIAc/edit?usp=sharing
Sample HTML reports — https://drive.google.com/file/d/1uBX_maD4e0uw23fYoMZQ6SmhhoCLnPqN/view?usp=sharing
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