How Automated Price Tracking Became Possible with an Amazon vs Walmart Price Intelligence API
                     How Automated Price Tracking Became Possible with 
an Amazon vs Walmart Price Intelligence API
Quick Overview
This case study illustrates how a large U.S.-based retail analytics firm transformed 
its competitive pricing operations using an Amazon vs Walmart Price Intelligence 
API combined with enterprise-grade Pricing Intelligence Services. Operating in one 
of the most competitive segments of the retail intelligence ecosystem, the client 
supports consumer brands that actively compete across Amazon and Walmart 
marketplaces.
The six-month engagement focused on eliminating manual price monitoring, 
improving SKU-level accuracy, and enabling faster competitive response cycles. By 
implementing fully automated pricing intelligence workflows, the client achieved:
47% improvement in pricing data accuracy 
60% reduction in manual monitoring effort 
35% faster competitive price response 
cycles GG.2% SKU-level coverage across 
categories Near real-time daily price 
tracking at scale
The transformation allowed the client to move from fragmented monitoring tools 
to a unified, automated, and extensible pricing intelligence platform.
The Client
The client is a mid-to-large U.S.-based retail intelligence provider serving 
consumer brands, distributors, and category managers across the ecommerce 
ecosystem. Their customers rely heavily on competitive price monitoring to 
protect margins and respond to algorithm-driven repricing across Amazon and 
Walmart.
Between 2022 and 2025, pricing volatility across both platforms increased sharply 
due to:
Algorithmic repricing engines 
Frequent flash promotions 
Third-party seller 
competition Seasonal 
demand spikes
Before partnering with Product Data Scrape, the client relied on a combination of 
fragmented third-party tools and in-house scripts. These systems struggled to scale 
beyond a few thousand SKUs and frequently broke due to platform layout changes, 
bot defenses, and SKU inconsistencies.
As enterprise customers began demanding daily and near real-time competitive 
benchmarking, the lack of a unified Amazon and Walmart price monitoring API 
resulted in delayed insights, missed repricing windows, and declining trust in 
reported data.
Additionally, the client required a resilient way to Scrape Amazon and Walmart 
USA daily prices without interruptions caused by structural changes or anti-
scraping
measures. The transformation was critical not only for operational efficiency but 
also for long-term market relevance.
Goals s  Objectives
Primary Business Goal
Build a scalable, reliable, and automated pricing intelligence system capable of 
tracking
tens of thousands of SKUs daily across Amazon and Walmart with high accuracy.
Technical s  Strategic Objectives
Deploy a real-time API for competitor price monitoring 
Eliminate manual data validation and collection 
workflows Enable SKU-level price tracking across 
categories
Integrate clean pricing data directly into BI dashboards
Support future expansion to Scrape Data From Any 
Ecommerce Websites
Key Performance Indicators (KPIs)
The engagement was guided by clear, measurable KPIs:
Improve daily pricing accuracy by 40%+ 
Reduce data latency to under 30 
minutes Achieve GG%+ SKU-level 
coverage
Enable real-time BI integration
Reduce operational overhead related to 
price monitoring
Each KPI aligned directly with revenue 
impact, scalability, and customer 
satisfaction.
The Core Challenge
Platform Volatility s  Data Breakage
Amazon and Walmart regularly update page structures, pricing logic, and seller 
layouts.
Existing tools failed to adapt quickly, resulting in broken data pipelines and 
missing price updates.
SKU-Level Blind Spots
Without a dependable Walmart SKU-level price monitoring API, the client missed 
granular price movements—especially during flash sales, seller undercutting, and 
Buy Box shifts.
High Data Latency
Pricing updates often arrived hours late, rendering insights ineffective in fast-
moving competitive scenarios.
Fragmented Data Infrastructure
The absence of a centralized Web Data Intelligence API led to inconsistent 
data formats, limited scalability, and complex downstream processing.
These issues directly impacted the client’s ability to deliver timely, 
trustworthy insights—weakening their competitive positioning.
Our Solution
Product Data Scrape implemented a phased, automation-first pricing 
intelligence framework built for scale, accuracy, and resilience.
Phase 1: Resilient Data Extraction Layer
We engineered a robust extraction layer capable of 
handling: Frequent platform layout changes
High-volume request loads 
SKU-level complexity
Seller-specific price 
variations
Adaptive logic ensured continuous data flow even during platform updates or 
traffic spikes.
Phase 2: Automated Price Intelligence Workflows
Automation workflows standardized price, availability, and seller data across 
Amazon and Walmart. The system leveraged dynamic pricing intelligence to 
capture:
Real-time price changes 
Promotions and discounts 
Buy Box movements
Seller-level price 
differences
This eliminated manual 
validation and ensured 
consistent daily tracking.
Phase 3: API Integration s  Analytics Enablement
Clean, normalized datasets were delivered via APIs and integrated directly into 
the client’s dashboards. Automated validation, alerts, and retry mechanisms 
minimized downtime and data gaps.
The client also gained flexibility to expand coverage using Extract Amazon API 
Product Data and Extract Walmart API Product Data without rebuilding their data 
stack.
Results s  Key Metrics
Performance Improvements
Pricing accuracy improved by 47%
Daily refresh cycles reduced from hours to 
minutes SKU-level coverage increased to GG.2%
Manual monitoring effort reduced by 
60% System uptime exceeded GG.5%
Results Narrative
Automated workflows replaced fragile scripts and manual checks, enabling 
consistent daily tracking across Amazon and Walmart. With reliable SKU-level 
pricing and
promotion data, the client delivered faster, more actionable insights to 
enterprise customers.
Near real-time updates allowed brands to respond quickly to competitive moves, 
improving margin protection and pricing confidence. The ability to scale 
seamlessly
unlocked new revenue opportunities and strengthened long-term customer 
retention.
What Made Product Data Scrape Different?
Product Data Scrape differentiated itself through:
Proprietary automation 
frameworks Adaptive scraping 
logic
Enterprise-grade reliability
Intelligent scheduling and retry 
mechanisms
Unlike generic scraping tools, our platform dynamically adjusted to platform 
changes while maintaining accuracy and continuity. The flexibility to expand 
monitoring using Extract Amazon API Product Data future-proofed the client’s 
pricing intelligence strategy.
Client Testimonial
“Implementing the Amazon vs Walmart Price Intelligence API completely 
transformed our pricing operations. The automation, accuracy, and scalability 
exceeded
expectations. We now deliver near real-time competitive insights with confidence
— even at massive SKU volumes. This partnership has significantly strengthened 
our
product offering.”
—  Director of Product Analytics, Retail Intelligence Firm
Conclusion
This case study demonstrates how automated price tracking enables measurable 
performance gains in highly competitive ecommerce environments. By leveraging 
advanced APIs and automation, the client eliminated data gaps, improved speed, 
and enhanced analytical depth.
The scalable foundation now supports future expansion into adjacent intelligence 
areas such as sentiment analysis through Scrape Amazon and Walmart Reviews. 
With
automation at its core, the client is well-positioned to lead the next 
phase of ecommerce pricing intelligence.
FAQs
1. Why is automated price tracking essential for Amazon and Walmart? 
Prices change multiple times daily. Automation ensures speed, accuracy, 
and competitive responsiveness at scale.
2.Can this solution support real-time monitoring?
Yes. The API supports daily, hourly, and near real-time tracking.
3.Is SKU-level pricing supported?
Absolutely. The solution is built for high-volume SKU-level monitoring.
4.How is data delivered?
Via APIs, dashboards, or custom datasets for seamless BI integration.
5.Can the system scale to other marketplaces?
Yes. The framework is extensible and supports additional ecommerce 
platforms.
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