Wayfair Rugs Market Intelligence: AI Agent Demo

Name: Pornimaa Eswari Ganesan

Email Id: pornimaaeg.raj@gmail.com

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Executive Summary

Overview of Market Intelligence Agents

🎯Objective:

Help Wayfair’s Rugs category team automate trend discovery, monitor competitors, and generate content ideas.

🧠My Solution

Built 3 connected AI agents that feed into one Market Intelligence Dashboard.

⚙️ Core Outputs

✓ Trend Discovery Agent - Link� ✓ Competitor Monitoring Agent - Link� ✓ AI Insights & Content Agent - Link� ✓ Integrated Dashboard - Link

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🔍 Key Insights

• Outdoor rugs are converging with indoor design: washable, non-slip, low-pile styles.
• Wayfair is the premium option (about $115 average vs. $63 Amazon, $52 Walmart) with the top ratings.
• The entry-level tier is thin on Wayfair; competitors lead on washable rugs.

🧠Future Improvements

Add more data sources (Amazon, Instagram), connect SKU sales data, run agents on a schedule to track change over time, and add a human review step before content is published.

Agent 1: Market Trend Discovery

From Rug Category + Product Links → Structured Trend Report

🎯 Objective:

Automates the manual research behind a trend report: which styles are trending, which colors, materials and features recur, which price bands dominate, and what risks come with investing.

🧠 Input Prompt Example

A rug category, plus optional product links.

Sample Input Prompt: Outdoor rug

⚠️ Keep in Mind / Input Notes

• Use a specific category (e.g., “outdoor rug”), not a broad term like “rugs.”
• Quality depends on source coverage: the sample run used Pinterest and industry articles; Amazon and Instagram returned no data.
• Treat results as a directional snapshot and validate market figures before planning.

📄View JSON Workflow

🖼️ View Sample Output

💡 Improvements / Expansion Opportunities

• Restore Amazon and Instagram feeds for fuller coverage
• Connect Wayfair SKU sales data to tie trends to performance
• Track trends across runs to show direction over time
• Schedule weekly runs with alerts when a segment shifts

Agent 2: Competitor Monitoring

From Rug Category → Wayfair vs. Amazon vs. Walmart Benchmark Report

🎯 Objective:

Automates competitive benchmarking: pulls product data from Wayfair, Amazon and Walmart and reports on price, ratings, discounts, assortment gaps and sourcing opportunities.

🧠 Input Prompt Example

A single chat message naming a supported rug category.

Sample Input Prompt: Outdoor Rug

⚠️ Keep in Mind / Input Notes

• Choose one of the supported rug categories; other entries return no data.
• Analyzes about 10 products per retailer, so findings are directional, not full-market. Figures vary slightly between runs.
• Review AI recommendations and supplier suggestions before acting.

📄View JSON Workflow

🖼️ View Sample Output

💡 Improvements / Expansion Opportunities

• Validate the category input before the run starts
• Expand sample sizes and add review text and imagery comparison
• Schedule weekly runs to track price and discount shifts
• Feed gaps into Trend Discovery so both agents inform each other

Agent 3: AI Insights & Content

From Trend + Competitor Reports → Ready-to-Use Content Strategy

🎯 Objective:

Turns research into action: reads both reports and produces a data-backed plan for a chosen audience, season and campaign goal, including content ideas, social captions, campaigns and a 3-email welcome sequence.

🧠 Input Prompt Example

Product category, the two reports (HTML), and optional audience, season and goal.

Sample Input Prompt: Outdoor Rug · Summer · Product Launch

⚠️ Keep in Mind / Input Notes

• Both reports are required; optional fields default to general shoppers, year-round and Awareness.
• Copy uses only facts from the reports: no invented statistics or discounts.
• Treat output as a first draft for human review before publishing.

📄View JSON Workflow

🖼️ View Sample Output

💡 Improvements / Expansion Opportunities

• Load Wayfair brand guidelines so copy matches voice
• Add creative briefs and image direction
• Add a human approval step before export
• Use performance data to learn what works

Market Intelligence Dashboard

Bringing All Agents Together → One-Page Insights for Wayfair’s Rugs Team

🎯 Objective:

Bring the trend and competitor findings onto one page so the Rugs team can see market size, price positioning, risks and next actions without reading two long reports.

⚙️ How This Works

Upload a category plus the Trend and Competitor reports. The workflow reads both without AI, so figures match the source reports, and fills a dashboard template.

⚠️ Keep in Mind / Implementation Notes

• Run both agents first; the dashboard is built entirely from their reports.
• Report layouts must stay the same, or fields show “N/A.” Data gaps in a report carry through.

📄View JSON Workflow

🖼️ View Dashboard

💡 Improvements / Expansion Opportunities

• Refresh automatically as new reports are generated
• Add trend lines across runs for prices, discounts and segments
• Connect Wayfair sales and traffic data
• Add filters by retailer, price tier or segment

Reflections & Future Improvements

What I Learned and How I Can Make It Better

🧭 Key Learnings

• “ I learned how AI can turn market signals into actionable insights, with each agent feeding the next. ”

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• “ I learned that small details break workflows: one uppercase letter returned zero products and was my hardest bug to find. ”

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• “ I gained hands-on experience building connected AI agents and dashboards. ”�

🚀 Future Improvements / Next Steps

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• “ I plan to integrate SKU performance metrics to connect trends with real sales data. ”

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• “ I’d like to restore Amazon and add social media sources for better trend discovery. ”

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• “ I want to refine AI prompts for more brand-aligned and creative content suggestions. ”

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Thankyou Image

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