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Case Study: How a New York Retailer Improved Price Matching on Amazon Using Hourly Scraping

Actowiz Solutions enabled a New York retailer to beat Amazon prices in real time using hourly scraping, boosting conversions and pricing precision.

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Executive Summary

In the hyper-competitive landscape of online retail, matching or beating Amazon’s dynamic pricing is essential for independent sellers. A New York-based retailer turned to Actowiz Solutions to implement an hourly web scraping solution for Amazon price tracking. This system enabled them to update their own e-commerce prices in near real time, resulting in a 22% uplift in conversions, 12% higher price competitiveness, and a marked reduction in inventory overstock.

Client Background

The client, a mid-sized electronics and accessories retailer headquartered in New York City, had been losing market share due to Amazon’s automated repricing mechanisms. With limited internal technical resources and rising advertising costs, the retailer needed a smarter, cost-effective way to stay price-competitive without margin erosion.

Challenges

Key-Challenges
  • 1. Amazon's Dynamic Pricing: Price fluctuations on Amazon occurred multiple times a day, especially during peak shopping hours or sales events.
  • 2. Lagging Response Time: The retailer's manual or once-a-day price updates were insufficient to match Amazon's pricing agility.
  • 3. Loss of Buy Box Position: The client's products often lost visibility on marketplaces due to pricing mismatches.
  • 4. Outdated Market Intelligence: Lack of real-time insights on competitor pricing, discounts, and stock availability.

Solution Delivered by Actowiz Solutions

Key-Solutions

Actowiz Solutions deployed a custom hourly scraping engine tailored for Amazon US listings in the consumer electronics segment. The solution architecture included:

  • Targeted ASIN Monitoring: Tracked over 3,000 Amazon Standard Identification Numbers (ASINs) related to mobile accessories, smart home devices, and small appliances.
  • Hourly Scraping Scheduler: Scraping frequency was set to once per hour, focusing on price, shipping fee, seller rank, and Prime eligibility.
  • Dynamic Pricing API Integration: Delivered real-time data to the client’s pricing engine to adjust product listings instantly.
  • Intelligent Alerts: Custom thresholds alerted the pricing team when Amazon dropped prices below a pre-defined margin.

Key Data Fields Scraped

Field Description
ASIN Amazon product identifier
Title Product title
Brand Manufacturer brand
Price Current selling price
Previous Price Original/strikethrough price
Seller Name Merchant offering the product
Prime Eligibility Whether fulfilled by Amazon
Ratings & Reviews Count Consumer trust indicators
Availability Status Stock in/out status
Timestamp For change tracking

Sample Scraped Data Snapshot

ASIN Title Price Prime Seller Timestamp
B08CFSZLQ4 Echo Dot (4th Gen) Smart Speaker $34.99 Yes Amazon.com 2025-05-08 08:00
B07FZ8S74R Fire TV Stick with Alexa $39.99 Yes TopRetailerNY 2025-05-08 08:00
B09G3HRP18 Smart Plug (2-pack) $18.99 No EZ-HomeTech 2025-05-08 08:00

Results Achieved

Results-Achieved
✅ Faster Response Time
  • Price update time dropped from 12 hours to under 1 hour.
  • The retailer could now respond within 60 minutes of a competitor change.
✅ Revenue Growth
  • 22% increase in conversion rates on own website and partner marketplaces within 3 months.
  • Gained Buy Box on Amazon-affiliated channels 40% more frequently.
✅ Inventory Optimization
  • With real-time pricing insights, the retailer avoided deep discounting on slow-moving inventory.
  • Achieved 15% reduction in unsold stock by adjusting prices based on Amazon’s sales velocity.
✅ Data Intelligence for Marketing
  • Hourly pricing trends helped the marketing team plan flash sales that were aligned with competitor discounts.
  • Improved ROAS (Return on Ad Spend) by targeting products with real-time price competitiveness.

Technical Infrastructure

  • Scraping Stack: Python + Scrapy + Selenium for JavaScript-rendered content
  • Scheduler: Cron + Celery for managing hourly crawl jobs
  • Database: PostgreSQL for structured data storage
  • Dashboard: Custom Power BI dashboard integrated with Actowiz API for real-time visualization

Why Actowiz Solutions?

  • Scalable Scraping Infrastructure with anti-blocking measures (rotating proxies, headless browsing)
  • Compliance-Aware Approach: Ethical scraping adhering to public data norms
  • E-Commerce Expertise: Prebuilt scrapers for Amazon, Walmart, Target, eBay
  • Dedicated Account Management and real-time API support

Platform-Specific Strategy: Amazon US

  • Hourly Trends: Identified pricing dips on Amazon between 2 AM – 5 AM EST. Used this intel to launch short-duration promotions.
  • Geolocation Targeting: Detected slight regional price variations in New York ZIP codes; adjusted shipping discount strategies accordingly.
  • Seller Profiling: Scraped top third-party sellers for product overlap to identify competitive opportunities.

Client Testimonial

“With Actowiz Solutions, we’re no longer playing catch-up with Amazon. The hourly data feed helped us time our price changes perfectly and win customers back. It’s like turning pricing into a science.”

— Head of E-Commerce, New York Retailer

Conclusion

For any retailer aiming to beat Amazon at its own pricing game, timing and data are everything. This New York-based retailer leveraged hourly scraping to turn price intelligence into a competitive advantage. Actowiz Solutions not only delivered real-time data but transformed it into actionable insights that improved profitability and operational efficiency.