The Client
Our client is a trusted eCommerce Seller dealing in consumer electronics. With over 25 years of experience in the industry, they now manage a portfolio of over 7,000 brands across two dedicated platforms, delivering a wide range of electronics and accessories to a growing network of vendors.
The Requirement
With a product catalog exceeding 2 million SKUs and an expanding brand portfolio, the client was facing significant challenges in managing product data. Their in-house processes were struggling to keep pace with the increasing operational demands of product onboarding and enrichment across multiple eCommerce platforms.
To streamline operations and address these issues before they escalated, the client was actively seeking a scalable product information management services that could:
Project Complexities
While the requirements were clear, the project presented several challenges in executing large-scale product data scraping and enrichment.
Our Solution
We proposed a holistic solution to address their PIM inefficiencies. It included large-scale data scraping using Python scripts, data consolidation for cleansing and standardization, taxonomy development, and AI-powered enrichment. This approach was designed to optimize product data acquisition, standardization, and enrichment at scale.
Owing to the scale of the project, we assembled a dedicated team with the required expertise. It included data scraping experts, prompt engineers, and QA professionals, all experienced working with similar clients, assuring maximum alignment with the client’s needs.
Scalable Python scripts were developed to automate and optimize data scraping for electronic product sellers like them. These scripts were designed to extract both structured data (e.g., product titles, prices, SKUs) and unstructured content (e.g., product descriptions, user reviews, warranty information) across various websites, ensuring comprehensive data coverage at scale.
Each script was manually reviewed to verify accuracy and compliance with ethical scraping practices.
To overcome anti-bot mechanisms and ensure reliable extraction, we employed a combination of techniques, including:
Once the data was scraped, we compiled it into a centralized repository for cleaning and preparation. Our team removed special characters, corrected formatting inconsistencies, and eliminated duplicate entries to ensure clean input for the subsequent stages. We then applied initial standardization across data fields to establish consistency and bring uniformity.
To support SKU enrichment for the consumer electronics product seller, we used ChatGPT-4 to generate missing attributes:
We then reviewed and validated all enriched data to ensure maximum consistency and factual accuracy. Each data point, including product descriptions, specifications, pricing, and images, was enriched by cross-referencing with similar products from trusted sources. This was done to ensure the enriched data not only met the highest standards of quality but also accurately reflected the client’s product offerings.
To address the client's need for a structured product categorization system, we created a custom taxonomy based on Google’s framework. We utilized ChatGPT to analyze their product line and identify the most relevant categories. Then, referring to the UNSPSC website, our experts assigned the appropriate UNSPSC codes to each product. Our team meticulously reviewed all assigned codes and categories, eliminating any errors and ensuring all products were correctly categorized.
The Irreplaceable Role of Human Experts in our eCommerce Data Scraping, Enrichment, and Categorization Process
Throughout this process, human expertise played a pivotal role in addressing areas where AI automation alone couldn't provide the necessary context.
1
Client Onboarding and Project Initialization
2
Data Scraping
3
Data Cleansing and Standardization
4
Custom Taxonomy Development and UNSPSC Categorization
Improved Data Accuracy, Structure, and Information Management for Over 2 Million SKUs
Our holistic approach, which combined AI-driven data enhancements with human expertise in enriching data, creating custom taxonomies, and validating data points, delivered measurable results. It led to noticeable improvements in data quality, more precise categorization, and better operational efficiency. As a result, the client experienced streamlined SKU management at scale, boosting overall performance and helping them manage their inventory more effectively.
99.8%
Error-free data through precise product categorization
78%
Increase in efficiency through strategic task automation
Reach out to us and get complete support with our end-to-end product information management services, covering everything from data extraction, cleansing, and enrichment to categorization and more. Write to us at info@data4ecom.com
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