E-commerce SEO Optimizer

A practical tool for e-commerce teams and SEO specialists to audit and improve on-page product listings at scale. Built with Python, Pandas, and Streamlit: upload a product catalog, see every title/description/image/price issue, review keyword-aware suggestions, and export an optimized copy — original columns are always preserved.

Problem it solves

E-commerce stores often have hundreds or thousands of thin product listings: short titles, missing descriptions, no keywords, broken image references. Fixing them manually in a spreadsheet is slow and error-prone. This tool surfaces the exact problems, proposes concrete title/description improvements using the product's own brand/category/keywords, and produces a safe export for review before applying changes upstream.

Key features

• Upload CSV or XLSX product catalogs (title, description, price, category, brand, image columns recognized and normalized)
• Audit engine: empty/short/long titles, empty/short descriptions, missing prices, missing or malformed image URLs, duplicate titles
• Severity grading: critical / warning / info
• Suggestion engine: expands short titles with brand/category keywords, generates description copy, records reasons for every change
• Safe apply: writes optimized_title, optimized_description, optimized columns without overwriting originals
• Download the optimized catalog as CSV

Data expectations

The tool looks for a title column (title, name, product, product title) and optional description, price, category, brand, image columns. Unknown columns are ignored. Missing values are flagged, never guessed.

Technology stack

• Python 3.11
• Pandas (catalog processing)
• Streamlit (web UI)
• openpyxl (XLSX support)

Folder structure

ecommerce-seo-optimizer/
├── app.py                  # Streamlit application
├── optimizer.py            # Validation, suggestion, and export engine
├── tests/
│   └── test_optimizer.py
├── requirements.txt
└── .gitignore

Installation

python -m venv venv
# Windows: venv/Scripts/activate  |  macOS/Linux: source venv/bin/activate
pip install -r requirements.txt

Usage

streamlit run app.py

Upload a catalog or use the bundled sample, review the issues and suggestions, then apply and download the optimized file.

Testing

pytest -q

Tests cover issue detection, suggestion generation, missing-title handling, safe apply behavior, and CSV export.

Limitations and known issues

• Keyword extraction is heuristic (brand, category, top words); it is not a full SEO content service
• Title length guidance uses common best-practice ranges (20-70 title, 50-160 description) — tune the constants in optimizer.py to your vertical if needed
• No integration with platforms (Shopify, WooCommerce, etc.); export and import manually

Future improvements

• Keyword density scoring and SERP snippet preview
• Competitor title analysis (from a comparison file)
• Shopify/WooCommerce export formats
• Batch apply via a reviewed CSV diff

License

MIT (add a LICENSE file before commercial use if required by your use case).

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Developed by Muhammad Hamid

Digital Marketing | SEO | Performance Marketing | E-commerce | Web Development

• Portfolio: https://abouthamid.web.app/
• LinkedIn: https://www.linkedin.com/in/muhammad-hamid-digital-marketing/
• WhatsApp: https://wa.me/923167417135

Have a project, job opportunity, or collaboration in mind? Contact Muhammad Hamid on WhatsApp.