Achieving 41% Faster Time to Market and 99.3% Accuracy in Product Data Matching for a Leading Pricing Intelligence Platform

99.3%

Data Accuracy Across SKUs

41%

Faster Time-to-Market

25%

Gross Profit Growth

Service

  • Product Data Matching
  • Product Data Management

Client Overview

A Renowned Competitor Pricing Intelligence Platform

Our client provides a high-tier subscription-based competitor-monitoring solution that extracts and collects real-time data from digital shelves to facilitate market trend monitoring, assortment analysis, and precise price benchmarking. Their software is being used by manufacturers, omnichannel merchants, and online retailers in over 30 countries.

Project Objectives

High-Precision and Scalable Product Data Matching for Proprietary Software

The client sought the expertise of a dedicated team of product data matching specialists to enhance the precision and performance of their competitor price-monitoring tool. While their system could automatically match products with identifiers like UPC and Brand SKU for like-for-like (LFL) or exact matches, manual support was critical for:

  • Manually matching client products to corresponding listings on competitor websites when the automated tool failed due to inconsistent titles, descriptions, or missing standardized identifiers.
  • Verifying the accuracy of automated product matches using Last Searched Quality Assurance (LSQA) validation before intelligence is shared with the end user.
  • Mapping product variations such as color, finish, size, and quantity using Dynamic Data (DD) values to ensure accurate like-for-like (LFL) comparisons.
  • Matching products across hundreds of competitor retail platforms and product search engines—including Google Shopping, PriceGrabber, Shopzilla, and others—to ensure broad multi-source coverage.
  • Handling large SKU volumes under quick turnaround times to ensure timely delivery of competitive insights.

Project Challenges

Managing 25K+ SKUs Monthly with Accuracy and Speed

The project scope was well-defined, but many factors made execution challenging, including rapid, accurate product matching at scale and the need to adapt to varied data formats, evolving competitor sites, and rigorous quality standards.

Rapid Turnaround

High Volume, Rapid Turnaround

Maintaining real-time intelligence required the team to validate over 25,000 SKUs monthly without compromising on the strict accuracy thresholds.

Variable Product Data

Variable Product Data

Competitor sites often used irregular naming conventions and incomplete descriptions, contained different attribute hierarchies and brand SKUs, or lacked UPCs. Such issues would limit the reliability of automated matching.

Variant Complexity

Variant Complexity

Products with multiple options (color, size, finish, etc.)—particularly in electronics, apparel, and home goods—needed detailed attribute-level data validation for precise matching.

Website Structures

Changing Website Structures

Frequent updates to competitor site UI (layouts, product pages, URLs) required ongoing adjustments to matching and QA procedures.

Multi-Criteria

Multi-Criteria Matching

Searching and validating required strong cross-referencing and analytical thinking skills as products needed to be matched on multiple criteria, such as brand SKUs, brand name, UPC, partial titles, product titles, descriptions, and features.

Quality Control

Large-Scale Quality Control

Thousands of automated matches were validated through the LSQA workflow, and even small errors could affect the quality of intelligence for end users, requiring the QC team to have specialized training and to pay close attention to details.

The Implemented Solution

Human-Verified Product Data Matching with Comprehensive Quality Assurance

We implemented a dual-layer validation process led by a specialized team of 9 eCommerce catalog management experts. To further safeguard the integrity of sensitive competitor data throughout the engagement, robust security protocols and strict compliance measures were also put in place.

Manual Matcher (MM) Operations

Our eCommerce specialists searched for client products on competitor websites using the client’s MM software. Steps included:

  • Data validation by leveraging multiple search parameters such as brand, brand SKU, UPC, product titles, and partial titles to find potential matches, then performing a detailed product titles, features, descriptions, specifications, and images comparison to confirm a 100% match.
  • Dynamic Data (DD) values were applied to map particular attributes for products with variants (finish, color, size, shade) and ensure the matched competitor product was the precise variant. This step was essential for linking URLs to specific product versions.
  • Confirmed matches were recorded via URL. Products without valid matches were marked as “No URL” to maintain database accuracy.

Last Searched Quality Assurance (LSQA)

This secondary tier focused on auditing automated and existing matches against competitor listings. Steps included:

  • Using Chrome, specialists conducted a side-by-side comparison of client listings and competitor URLs across multiple criteria (brand, size, title, color, quantity) to confirm matching status.
  • As with manual matching, DD values were assigned here too, to keep the SKU database up to date with all variant information.
  • Completed data tagging by approving products that matched “exact match” criteria, while rejecting those without relevant matches, as per client guidelines.

Security Measures

Sensitive Data Protection, Assured

Given the sensitivity of pricing data, stringent security protocols were enforced, ensuring the confidentiality of competitive intelligence:

  • No Data Downloads: There were no data extraction, downloads, or storage on team workstations as the client’s platform operated entirely in a browser-based environment.
  • Data Compliance: All project operations were aligned with ISO/IEC 27001:2022 security standards, including physical safeguards, access logs, and regular security audits.
  • Confidentiality and Access Control: All team members signed comprehensive Non-Disclosure Agreements (NDAs) prior to project onboarding and accessed the client’s system exclusively through VPN-secured connections with multi-factor authentication. Access was tailored to each member, allowing visibility into and interaction with only the data and tasks assigned.

Project Achievements

Our domain expertise, scalable workforce model, and human-assisted data validation workflow delivered measurable value to the client and enhanced their overall product data management capabilities.

41%

Faster time-to-market.

25%

Growth in gross profit for pilot customers.

99.3%

Accuracy rate across all matched products.

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