Case Study: Automated Price Aggregation for Competitive Intelligence in E-Commerce
Client
Global Retail Inc.
Industry
eCommerce & Retail
Region
North America & Europes
Challenges
In the fast-paced world of e-commerce, pricing is one of the most influential factors that drive consumer choice. For major retail businesses, staying competitive requires real-time awareness of market prices across thousands of SKUs. Our client, a leading player in the e-commerce retail sector, was relying on manual tracking and fragmented tools to monitor competitors’ pricing, which was inefficient, slow, and error-prone.
Given the scale of operations and the volume of products they managed, it became increasingly difficult to make informed pricing decisions quickly. There was a clear need for a robust, automated solution that could consistently track competitor pricing and empower data-driven decisions.
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Goals & Objectives
The client was struggling to stay competitive in a rapidly changing market where competitors adjusted pricing thousands of times daily. Their manual monitoring process was slow, inconsistent, and covered only a fraction of their catalog. They needed real-time visibility into competitor pricing across their entire product range to optimize their pricing strategy.
To develop an automated solution for monitoring competitor prices across multiple e-commerce websites.
To improve pricing strategies and stay competitive in real-time without manual intervention.
To enable better decision-making by aggregating product-level pricing insights across SKUs and platforms.
To enhance customer acquisition and retention through competitive pricing models.
Requirements
A scalable web crawling infrastructure capable of handling thousands of pages per crawl cycle from multiple domains.
Intelligent mapping of client SKUs with competitor product listings, even if naming conventions varied.
Data normalization and enrichment pipelines to clean, structure, and standardize extracted information.
A dashboard or exportable format to view and act upon pricing deltas across categories.
Alert mechanisms for major price drops or changes in best-selling competitor products.
Ability to schedule automated crawls at regular intervals for near real-time insights.
Solution
We built a custom price aggregation system tailored to the client’s operational and strategic needs:
Automated Web Crawlers:
Developed using .NET and headless browser automation frameworks to extract product prices, SKUs, and metadata from a large set of e-commerce competitors.
Intelligent Matching Engine:
Integrated fuzzy matching and rule-based logic to accurately pair client SKUs with similar competitor listings, overcoming inconsistencies in product naming.
Centralized Data Processing:
Built a backend pipeline to clean, deduplicate, and normalize data before pushing it to the client’s business intelligence system.
Custom Dashboards and Exports:
Delivered data in both real-time dashboards (via internal tools) and structured exports for integration into the client’s pricing systems.
Monitoring and Failover Systems:
Added logging, retry, and fallback systems to ensure reliability across changing website structures and anti-bot mechanisms.
Results
The client now receives structured pricing data across competitors with minimal latency, enabling faster and smarter decision-making.
Allowed the business to undercut competitors tactically, resulting in increased conversion rates and reduced customer churn.
The system scaled from a few hundred SKUs to tens of thousands with negligible additional overhead.
Since implementation in 2012, the client has seen a manifold increase in profit margins, enhanced customer retention, and a consistent rise in new user acquisition.
Technologies Used
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Lora Smith
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