Client Success Story
Standardizing A 1.2M-SKU Catalog For A Consumer Electronics Retailer
- Lift in Marketing Campaign Performance
- 40%
- Lift in Marketing Campaign Performance
- Increase in On-Site Search Conversions
- 38%
- Increase in On-Site Search Conversions
- Data Accuracy Across the Full Catalog
- 99%
- Data Accuracy Across the Full Catalog
Client Profile
An Established Consumer Electronics Retailer with 1 Million+ Products
Our client is a US-based consumer electronics merchant that sells through both a physical storefront and an Adobe Commerce website. The business has been online for more than half a decade, during which it has grown a product library exceeding 1.2 million SKUs.
The assortment covers home entertainment systems, audio equipment, smart kitchen devices, security cameras, landline and mobile accessories, and a broad range of batteries and power products. The retailer caters primarily to domestic shoppers looking for value and variety and continues to diversify its product catalog year over year.
Project Scope
End-to-End Data Standardization & Management Across a Large and Unorganized Product Catalog
The retailer approached us when their internal teams could no longer support the pace and scale of catalog growth. Inaccurate attributes, duplicate records, inconsistent naming conventions, and incomplete specifications were limiting effective merchandising across the storefront.
The client needed a partner that could take on the operational workload, apply standardized rules across categories, and restore the catalog to a structure that supported execution of marketing, pricing, and promotional activities at scale.
The brief specifically covered:
- Reclassifying SKUs into the correct parent, child, and subcategory structure across the storefront.
- Standardizing product attributes, ensuring filters, facets, and on-site product comparisons worked reliably.
- Auditing and correcting product descriptions, pricing, discount flags, and promotional fields.
- Converting catalog records into a standardized template to maintain consistency at scale.
Project Challenges
Managing Classification, Attribute, and Accuracy Issues Across a Large Catalog
Handling a catalog of roughly 1.2 million products brought several operational challenges into focus. Data inaccuracies across the storefront affected marketing execution, reduced consistency, and slowed new product launches.
The most significant issues our team addressed included:
- Correcting inaccurate product categorization and missing attribute visibility: A large number of products in the client’s catalog had been placed under a broad “General” product type instead of being assigned to the appropriate category. In many cases, the related attribute fields were also not enabled, which prevented meaningful product information from appearing across the catalog.
- Merchandising tools rendered ineffective due to data discrepency: Incorrect categorization, missing data, and inconsistent taxonomy terms, color values, and other product information limited the client’s merchandising capabilities. As a result, key functions such as guided navigation and faceted filtering by attributes like color and price range could not perform reliably.
- Sustaining subject-matter accuracy across hundreds of brands: The client’s catalog spanned a broad range of consumer electronics products, each with its technical data requirements. Identifying product data gaps and assigning correct attributes, including connector type, wattage, resolution, and battery rating, called for dedicated experts familiar with the nuances of each category.
Our Approach
Providing a Comprehensive, Multi-Step Product Data Management
We assigned a 10-member team of product data specialists to the project. The client trained the team on its Adobe Commerce data structure, product terminology, data entry guidelines, and category-specific attribution requirements.
Once training was complete, the team executed the project in four stages:
- 01
Data Cleansing
We began by analyzing catalog discrepancies and cleansing the database to improve the product attribution process. Our team used product data matching techniques to identify duplicate records and correct inaccurate information, spelling errors, and variation-related inconsistencies across the catalog.
- 02
Data Standardization
We consolidated product listing data within the client’s system and standardized key data elements across the catalog, including descriptions, titles, measurement units, spelling, terminology, numerical values, and abbreviations. This brought the catalog into a more consistent format and improved data quality across product records. For example, inconsistent variants such as “colour,” “color,” and “clr” were standardized to “color” across the website and catalog.
- 03
Data Enrichment
To address missing and incomplete product information, we assigned dedicated product data enrichment resources to gather relevant details from trusted sources such as product catalogs and manufacturer websites. The extracted information was then added in the required format so website listings reflected more complete and accurate product data.
- 04
Sorting and Categorization
We created a structured taxonomy with clear parent, child, and subcategory levels to organize the catalog more effectively. Our team accurately classified data, standardized Universal Product Codes (UPCs), and assigned the correct attributes to each item. This improved data consistency across retailer, supplier, and manufacturer workflows while also strengthening product discoverability on the website.
Results Delivered
Tangible Gains in Discoverability, Marketing Performance, and Data Quality
- Increase in Site Search Conversions
- 38%
- Increase in Site Search Conversions
- A more accurate taxonomy and cleaner attribute structure improved product discoverability across the storefront, helping shoppers find relevant SKUs more easily through on-site search.
- Lift in Marketing Campaign Performance
- 40%
- Lift in Marketing Campaign Performance
- Email, paid media, and on-site promotional campaigns benefited from reliable product data, clean imagery, and accurate pricing, which improved click-through and conversion rates.
- Catalog Data Accuracy Across 1.2M SKUs
- 99%+
- Catalog Data Accuracy Across 1.2M SKUs
- Our team delivered cleansed, matched, and standardized records, followed by internal QA audits. The client received data in a merchandising-ready state.