The Client
Our client offers subscription-based pricing intelligence software used by omnichannel sellers, online retailers, and manufacturers in 30+ countries. Their software collects competitor product data from digital shelves and uses it to support assortment analysis, market trend monitoring, and price benchmarking.
Project Requirements
The client's software could automatically match products using standardized identifiers, such as UPCs and brand SKUs. However, a significant portion of their catalog was outside the scope of reliable automation, requiring a dedicated team of product data matching experts to handle manual matching and validation at high volumes.
The specific requirements were:
Project Challenges
Executing this project at the required scale introduced several challenges, including:
Maintaining real-time competitive intelligence required matching and validating 25,000+ SKUs monthly without compromising accuracy. Speed and precision had to be sustained simultaneously, as any processing backlog would delay data delivery, while rushed validation would undermine data reliability.
Categories such as electronics, home décor, and apparel required attribute validation across dimensions, pack configurations, and model numbers, as these categories have multiple variant products (e.g., finishes, colors, shades, and sizes). Ensuring exact comparison was crucial, as matching a product to the wrong variant—even within the same product family—would produce inaccurate pricing intelligence.
Product titles, descriptions, and attribute structures varied widely across websites, making automation unreliable for non-standardized items. Some products lacked consistent brand SKUs or UPC codes, further increasing reliance on a manual matching workflow.
Each product required searching and validating across several data points, such as brand SKU, brand, UPC, partial titles, and product features, demanding analytical judgment rather than procedure-based execution.
Competitor websites regularly change page layouts, URL structures, and product pages. Product data matching methodologies and validation protocols had to be revised regularly to remain aligned with these changes.
Errors in LSQA validation—even at a low rate—would cascade into flawed pricing intelligence delivered to the client's customers, making precision at scale a non-negotiable.
Our Solution
To support the engagement, 10 product data matching specialists were trained on the client’s primary workflows, ensuring full alignment with established matching procedures and validation standards.
Our specialists used the Manual Matcher software provided by the client to search and verify product matches across competitor websites. The workflow involved:
LSQA involved a side-by-side review of automated or previously matched products against current competitor listings. The process included:
Security Measures
As the project involved sensitive pricing data, the engagement was executed under strict security protocols, including:
By integrating a structured matching framework with a rigorous two-tier quality assurance protocol, our domain-expert team drove significant operational improvements. This performance established a foundation for a long-term partnership managing the client’s end-to-end product data requirements. Key outcomes of our engagement include:
| Metric | Result |
|---|---|
| Data Matching Accuracy | 99.3% across all matched products. |
| Gross Profit Uplift | 25% potential growth in gross profit for the major selected customers using validated pricing data. |
| Operational Efficiency | 40% improvement in data processing efficiency and time-to-market. |
Our eCommerce product data management specialists clean, validate, and structure SKU-level data at scale—so your operations run on reliable, decision-ready information.
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