E-Commerce Pipeline & Market Analytics

Project Summary

This project is a decision-ready analytics system built inside my sustainable fashion resale business. I designed the reporting pipeline, forecasting workflow, and dashboard layer so operational decisions around inventory, pricing, and demand could be made with cleaner signal and less manual effort.

💡 The Solution: Automated Reporting

I engineered and optimized SQL and Python data pipelines to analyze market trends and inventory, which directly reduced reporting time by 40%.

Key Technical Actions:

• Pipeline Engineering: Built automated scripts to ensure 100% data accuracy for inventory tracking.
• Predictive Dashboarding: Designed Tableau dashboards that improved sales forecasting accuracy by 25%.
• Strategic Communication: Translated technical metrics into growth strategies for the business.

🛠️ Technical Deep Dive

The workflow started with transactional sales exports, inventory movement logs, product-level cost data, and marketplace pricing snapshots collected across the resale operation. I used SQL to structure the raw tables into repeatable reporting layers for sell-through, margin, inventory aging, and category performance, then used Python and Pandas to automate the cleaning steps that usually slowed reporting down: standardizing SKU naming, deduplicating records, aligning timestamps, flagging missing cost fields, and reconciling listing activity with actual sales.

Once the base tables were reliable, I built a lightweight reporting pipeline that refreshed key business views without manual spreadsheet cleanup each cycle. That pipeline automated category rollups, weekly inventory health checks, demand snapshots for fast-moving products, and export-ready tables for Tableau, which I used to monitor product performance, pricing opportunities, and forecast demand at the merchandising level.

📈 Key Metrics & Outcomes

The 40% reporting time reduction came from comparing the old manual reporting workflow against the automated refresh process. Tasks that previously required hand-merging marketplace exports, checking inventory inconsistencies, and rebuilding summary tables were reduced to a shorter review-and-publish cycle because the transformation logic was already embedded in SQL and Python scripts.

The 25% forecasting accuracy improvement was observed after moving from intuition-led inventory planning to dashboard-driven demand monitoring. By tracking category-level sales velocity, historical turnover, and pricing patterns in Tableau, I was able to make more consistent purchasing and stocking decisions, which improved how closely projected demand matched actual sell-through over subsequent reporting periods.

🔧 Tools Used

SQL | Python | Tableau | Pandas | Pipeline Automation