PricePulse
An automated competitor price tracking platform enabling real-time market intelligence for e-commerce businesses with daily scraping, price alerts, and exportable reports.
Client
Retail Insights Co.
Role
Backend
Timeline
2 months
Team
2 developers
Overview
Retail Insights Co. manually tracked 200+ competitor prices weekly, spending 15+ hours per week on research. PricePulse automates this process, scraping Amazon, eBay, Walmart, and other platforms daily, providing instant market insights for pricing strategy.
Process
Built scalable scraping workers using Puppeteer, scheduled jobs with cron, implemented failure recovery, and created a dashboard for visualizing price trends and competitor activity.
Key Features
Challenges & Solutions
Implemented rotating proxies, randomized user agents, added request throttling, and used headless browser (Puppeteer) to mimic human behavior. Success rate improved to 94%.
Built modular scraper architecture with fallback selectors, added automated failure detection, and created alerts for selector failures. Recovery time reduced to <2 hours.
Implemented parallel scraping with 20 concurrent workers, optimized database queries, and added smart scheduling (high-priority products more frequently). Cycle time reduced to 45 minutes.
Added out-of-stock detection, implemented data validation rules, created duplicate detection, and added manual review queue for anomalies. Data completeness improved to 99.2%.
Results
Manual Research
100% elimination
Scraping Success
<2% missing
Product Coverage
real-time updates
Scraping Cycle
for all competitors
Alert Speed
of price changes
Revenue Impact
pricing optimized
Goals
- •Automate competitive price monitoring
- •Provide real-time market intelligence
- •Enable data-driven pricing strategy
- •Scale to monitor 500+ products
Tech Stack
- •Node.js
- •Puppeteer
- •MongoDB
- •Express
Target Users
- •E-commerce managers
- •Pricing analysts
- •Category managers
Key Learnings
- •Anti-bot protections require diverse techniques—no single solution works
- •Modular scraper architecture is essential for maintainability
- •Data validation is as important as data collection
- •Scheduling and parallelization are key to scaling data pipelines
Future Plans
- •Add machine learning for price elasticity prediction
- •Expand to 50+ e-commerce platforms
- •Implement competitor sentiment analysis from reviews
- •Add predictive pricing recommendations