Client Success Story

High-Accuracy Product Data Matching for a Leading Competitor Pricing Intelligence Platform

40%

Faster
Time-to-Market

20-25%

Uplift in
Gross Profit

99.2%

Data Accuracy Achieved

Service

  • Product Data Matching
  • Data Validation
  • Competitor Price Monitoring

Platforms

  • Proprietary Price Intelligence Software
  • Manual Matcher (MM)
  • LSQA Quality System
The client

A Leading Competitor Pricing Intelligence Platform

Our client has a subscription-based competitor pricing monitoring software that serves online retailers, omnichannel merchants, and manufacturers across 30+ countries. Their proprietary pricing intelligence platform aggregates competitor data directly from digital shelves to power price benchmarking, assortment analysis, and market trend monitoring.

PROJECT REQUIREMENTS

Achieving Accurate and Scalable Product Data Matching

The client required a dedicated team of eCommerce product data matching specialists to improve the accuracy and performance of their proprietary tool. The pricing intelligence software could automatically match products with standardized identifiers (UPC, Brand SKU) as exact matches or like-for-like (LFL) comparisons. However, manual intervention was essential for:

  • Manual Matcher (MM) Operations: Searching and matching client products on competitor websites when automated systems couldn't establish reliable matches due to variations in product titles, descriptions, or missing standardized identifiers
  • Quality Assurance Validation: Verifying automated matches through Last Searched Quality Assurance (LSQA) reviews to ensure data accuracy before feeding intelligence to end-users
  • Variant Attribute Matching: Accurately mapping product variations (color, finish, size, quantity) using Dynamic Data (DD) values to ensure precise like-for-like comparisons
  • Multi-Source Coverage: Matching products across several hundred competing retail websites and product search engines (including Google Shopping, PriceGrabber, and others)
  • Scalability: Processing large volumes of SKUs within tight turnaround times to maintain the timeliness of competitive intelligence data
PROJECT CHALLENGES

Accurately Matching Over 25,000 SKUs Per Month Under Strict Guidelines and Timelines

While the project was well-defined, several aspects made execution complex:

  • High Volume and Tight Deadlines: The project demanded not just accuracy but speed. Manual product data matching and validation processes needed to be efficient enough to maintain real-time intelligence while ensuring accuracy across 25,000+ SKUs.
  • Inconsistent Product Information: Competitor websites used varying naming conventions, incomplete product descriptions, and different attribute hierarchies, making automated matching unreliable for non-standardized products. Many products lacked UPC codes or consistent brand SKUs.
  • Product Variation Complexity: Products with multiple variants (colors, finishes, sizes, shades) required granular matching to ensure exact comparisons. Categories like electronics, apparel, and home décor required detailed attribute-level validation for model numbers, dimensions, and pack configurations.
  • Dynamic Website Changes: Competitor websites frequently updated with new layouts, product pages, and URL structures, requiring constant adaptation in matching methodologies and quality assurance protocols.
  • Multi-Criteria Matching Logic: Products needed to be searched and validated using multiple criteria—brand, brand SKU, UPC, product title, partial titles, features, and descriptions—requiring analytical thinking and cross-referencing skills.
  • Quality Control at Scale: Validating thousands of automated matches through LSQA workflows required specialized training and attention to detail. Even minor errors in match validation cascaded into flawed intelligence for end-users.
OUR SOLUTION

Human-in-the-Loop Driven Product Data Matching with Multi-Tier Quality Assurance

We assigned a team of 10 eCommerce catalog management specialists trained in product matching methodologies to complete this project in the desired timeframe. To maintain data integrity, the team was trained specifically in the client’s two core validation workflows:

Manual Matcher (MM) Operations

Using the client’s MM software, our eCommerce data specialists systematically searched for client products on competitor websites. The process involved:

  1. Data Validation: We compared product information on competitor websites using multiple search criteria—brand, brand SKU, UPC, product titles, and partial titles. Once potential matches were identified, analysts performed detailed comparisons of titles, features, descriptions, specifications, and images to confirm exact matches.
  2. Variant Matching Using DD Values: For products with variations, specialists inserted Dynamic Data (DD) values to map specific attributes (finish, color, size, shade), ensuring the competitor product matched the exact variant of the client product. This was a non-negotiable step to link the captured URL to the precise product variant.
  3. Data Capturing and Tagging: Validated matches were captured via URL, while products with no suitable matches were flagged as "No URL" to maintain database integrity.

Last Searched Quality Assurance (LSQA)

It involved reviewing automated and previously matched products side-by-side against competitor listings. Our eCommerce data specialists performed:

  1. Data Validation: The team opened the provided URLs in Google Chrome and compared competitors’ and the client listings side-by-side using multiple search criteria: brand, product title, item finish/color, size, and quantity, to verify whether they are an exact match or not.
  2. Variant Matching Using DD Values: Like Manual Matching, LSQA also involved assigning DD values for products with variations to maintain an up-to-date database of SKU variations.
  3. Data Tagging: For products falling under the “exact match” category as per the client’s guidelines, we clicked on the “Approve” button, and for products with no relevant matches, we clicked on the “Reject” button.

Through this structured, two-tier approach, we ensured every SKU’s pricing data was human-verified and correctly standardized for accurate competitor price monitoring.

Assured Data Security for Sensitive Competitive Data

Given the highly sensitive nature of competitive pricing data, a robust security framework was maintained throughout the engagement:

  • Access Control: All team members accessed the client's system through VPN-secured connections with multi-factor authentication. Individual credentials were assigned with role-based access, ensuring team members could only access assigned tasks and data.
  • Data Compliance: The entire operation adhered to the ISO/IEC 27001:2022 data security guidelines. This includes physical security controls, access logging, and regular security audits to ensure compliance with international standards.
  • Confidentiality Agreements: Every team member signed comprehensive Non-Disclosure Agreements (NDAs) before project onboarding.
  • No Data Downloads: The client's platform operated entirely in a browser-based environment with download restrictions. No competitive intelligence data, product information, or pricing data could be extracted, downloaded, or stored locally on team workstations.

Project Outcomes

Our human-in-the-loop data validation approach, scalable workforce model, and domain knowledge delivered exceptional results, establishing SunTec India as a strategic partner for the client’s long-term product data management requirements. Some measurable benefits achieved through our product data matching services are:

40%

improved operational efficiency and faster time-to-market achieved

20-25%

potential uplift in gross profit for the client’s key pilot customers with verified pricing data

99.2%

accuracy rate maintained across all matched products

CONTACT US

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