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Case Study: Automating Grocery Price Intelligence Across U.S. Metros Using Walmart, Amazon Fresh, and Target APIs

Learn how Actowiz Solutions automated grocery price intelligence across U.S. metros using Walmart, Amazon Fresh & Target APIs with real-time sample data.

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Real-Time Regional Insights with Customizable E-commerce Dashboards

Introduction

In the era of quick commerce and digital grocery shopping, real-time pricing insights can define a brand's competitiveness. Major platforms like Walmart, Amazon Fresh, and Target dominate the U.S. online grocery ecosystem, but regional pricing, availability, and discount fluctuations vary greatly across ZIP codes.

To navigate this complexity, a leading FMCG brand in the United States partnered with Actowiz Solutions to automate grocery price tracking across multiple cities, improve competitor benchmarking, and streamline regional offer deployment.

Client Objectives

  • Extract real-time prices of 250+ products across Walmart, Amazon Fresh, and Target.
  • Compare regional pricing differences across 10 U.S. metro areas.
  • Detect competitor discounts and dynamic price shifts.
  • Integrate pricing data into a BI dashboard for internal pricing strategy alignment.

Challenges Faced

The-Client
  • Inconsistent product listings across platforms and ZIP codes.
  • Dynamic pricing updated multiple times daily.
  • Lack of API access from some platforms, requiring hybrid scraping + API method.
  • Scaling data extraction without being blocked or rate-limited.

Actowiz Solutions' Approach

The-Client
1. API Access & Smart Scraping:

Actowiz integrated available public and private APIs (Walmart & Target) and implemented smart scraping for Amazon Fresh using rotating IPs and headless browsers.

Tools Used:

  • Python (Scrapy, BeautifulSoup, Selenium)
  • AWS Lambda for parallel extraction
  • Proxy rotation via residential IP pools
2. Unified Product Matching Engine:

Created a product normalization layer to match SKUs across different retailers, using NLP for fuzzy matching based on brand, size, and product variant.

3. Real-Time Data Scheduler:
  • Scraped or called APIs every 4 hours (6x per day)
  • Stored data in MongoDB
  • Served via REST API + CSV for dashboard consumption

Sample Data Snapshot

Product Name ZIP Code Platform MRP Price Discount Availability Timestamp
Tide Pods 42ct 10001 Walmart $19.99 $17.49 12.5% In Stock 2025-06-01 08:00 AM
Tide Pods 42ct 10001 Amazon Fresh $19.99 $16.99 15% In Stock 2025-06-01 08:00 AM
Tide Pods 42ct 10001 Target $19.99 $18.49 7.5% Out of Stock 2025-06-01 08:00

Key Results

🔹 98% SKU Price Coverage
  • Covered 250+ SKUs across 10 cities with <2% data gaps.
🔹 Detected Dynamic Discounts
  • Identified over 1,300 price changes in 30 days.
🔹 Hyperlocal Pricing Intelligence
  • Enabled city-level pricing comparisons: e.g., Los Angeles vs. Chicago.
🔹 Time-to-Market for Offers Reduced by 40%
  • Marketing teams deployed promotions based on live competitor pricing.

Visual Dashboards (Delivered to Client)

The-Client
  • Price Heat Map by City & Platform
  • Daily Discount Tracker
  • Out-of-Stock Alerts
  • Price Change Velocity Graph

Technology Stack

  • Backend: Python, Flask
  • Data Storage: MongoDB, AWS S3
  • APIs: Walmart API, Target Public API, Custom Scraper for Amazon Fresh
  • Scheduler: AWS Lambda + CloudWatch
  • Frontend: Power BI and Google Looker Studio

Why Actowiz Solutions?

  • 🌐 Expertise in U.S. retail ecosystem
  • 📊 Real-time scraping + API integration
  • 🚀 Rapid deployment: live in 12 business days
  • ✅ Legal & compliance-checked data practices

Client Testimonial

"Actowiz’s solution helped us uncover pricing opportunities and outperform competitors in key U.S. metros. Their automation saved us hundreds of man-hours."

— Director of Pricing Strategy, FMCG Brand USA

Next Steps

  • Expand coverage to include Shipt, Costco, and Kroger.
  • Integrate review scraping for sentiment-price correlation.
  • Add AI-driven price prediction layer.

Conclusion

Actowiz Solutions demonstrated how smart automation, real-time scraping, and hybrid API models can help brands stay ahead in the dynamic U.S. grocery market. By enabling zip-code level insights into price, stock, and competitor movements, FMCG and retail brands can fine-tune strategies for both national and local campaigns.