Client Success Story
Scaling Product Data Scraping And Enrichment For An Multi-Brand eCommerce Distributor
- Increase in Operational Efficiency
- 78%
- Increase in Operational Efficiency
- Accuracy Achieved
- 99.8%
- Accuracy Achieved
- SKUs Processed and Enriched
- 2M+
- SKUs Processed and Enriched
Client Profile
Established Multi-Brand eCommerce Reseller Managing 7,000+ Supplier Brands
An eCommerce reseller founded in the early 1980s has grown into a trusted reseller for industrial machinery, hardware supplies, and household appliances. The business maintains active vendor relationships with more than 7,000 manufacturer brands and operates two dedicated eCommerce storefronts serving a broad range of sectors, including plumbing, electrical, and equipment supply. With a catalog exceeding 2 million products, maintaining consistent and accurate product data at scale has been an operational priority.
Project Scope
Extracting, Categorizing, and Enriching Product Data at Enterprise Scale
The scope of work included three interconnected product data management functions for the client's 2M+ SKU catalog:
- Data Extraction from Brand Sites: The team was tasked with extracting product data from multiple third-party brand websites shared by the client. This process required precision to preserve all available attributes, followed by removing special characters and format standardization to ensure the data could be integrated directly into pre-approved templates.
- Custom Taxonomy Build and Product Classification: The client's platform had no existing category structure or classification framework. The team was responsible for designing a taxonomy from scratch to match the client's product range and applying accurate UNSPSC codes to each item to improve platform navigation and searchability.
- AI-Assisted Product Data Enrichment: A significant volume of scraped records contained incomplete or missing data fields. The client requested the use of AI — specifically ChatGPT — to fill these gaps systematically through customized prompting, ensuring each enriched field met defined quality and consistency standards.
Project Challenges
Handling Multi-Site Structural Differences, Anti-Bot Restrictions, and Large-Scale Scraping Constraints
Each phase of this engagement brought distinct technical and operational challenges that required targeted responses at every stage. The key obstacles included:
- Development of Custom Scripts For Scraping Websites: Each brand website presented its own scraping constraints—unique HTML structures, embedded content restrictions, and varying anti-bot settings. There was no single-script solution; the team had to build and deploy distinct extraction tools per platform without compromising delivery timelines.
- Scaling Manual Scripts Across High Data Volumes: Scripts built for targeted, single-site extraction lacked the throughput needed for simultaneous, high-volume collection across multiple brand websites. As batch sizes grew, the performance constraints of manually developed scripts became a limiting factor that required proactive management.
- Standardizing Inconsistent Raw Data Output: Much of the scraped data arrived without proper categorization or consistent formatting. Extensive post-processing was required to remove non-standard characters, reformat fields, and align all records with the client's templates—steps that added time and complexity to an already demanding pipeline.
- Ensuring Reliability of AI-Generated Categorizations: ChatGPT occasionally returned outdated UNSPSC codes or produced category assignments that were inaccurate. Managing AI output quality required precise and specific prompts and systematic human review at each enrichment and categorization step to ensure results met acceptable accuracy standards.
Our Approach
Custom Scraping Scripts, Taxonomy Design, and AI-Assisted Enrichment
A six-person team was assembled to manage the engagement. The team included a prompt engineer, a data scraping specialist, and a dedicated QA resource.
- 01
Custom Python Script Development for Data Scraping
Brand websites for scraping were delivered to the team in batches. Custom Python scripts and extraction tools were developed for each site to collect product attributes—including descriptions, pricing, taxonomy, reviews, and product categories—at scale and with high accuracy.
Post-product data scraping, any special characters were removed, and data were organized according to the client's structural requirements. Three approaches applied across scraping operations:
- Curl requests were used to query web resources directly, enabling structured data retrieval and testing while bypassing front-end restrictions.
- Python requests automated HTTP requests to target URLs, producing error-free, consistent data downloads.
- BeautifulSoup objects were used to parse HTML content, enabling precise extraction and cleaning of specific data points for further processing.
The client established clear guidelines on permissible data scraping targets, ensuring all scraping activity remained compliant with ethical and legal standards while minimizing the risk of website blocking.
- 02
Taxonomy Development & UNSPSC Code Assignment
To establish a consistent product classification structure, we developed a custom taxonomy aligned with the client’s product range, drawing from Google’s category framework. We then used ChatGPT to suggest the closest category matches and cross-referenced each product against the UNSPSC directory to assign the correct codes.
Our team manually reviewed every category and code to remove inaccuracies and ensure the final classification remained precise and reliable.
- 03
AI-Powered Data Enrichment with ChatGPT-4
A custom GPT-4 integration was developed after purchasing API tokens to automate the enrichment of incomplete product data. Prompt engineers developed highly specific instructions to guide the model in filling missing fields — including weights, descriptions, and category assignments — with contextually accurate outputs.
We also used our master database to enrich the records further, adding relevant information that improved overall data completeness and quality.
- 04
Human-Supervised Automation Across All Workflows
Automation handled most of the work across scraping, enrichment, categorization, and cleansing processes. Human oversight was applied at each stage to validate outputs, identify errors, and ensure every record met the client’s quality standards before delivery.
- Data Scraping: Python scripts and scraping tools automated the retrieval of product data across all brand websites. | Human Intervention: Manual review was conducted to verify accuracy, remove unwanted special characters, and confirm the data matched client formatting standards.
- Data Enrichment: ChatGPT was used to identify and fill missing product information, including descriptions, weights, and categories. | Human Intervention: Team members validated AI-generated entries and cross-referenced the in-house master database to close any remaining data gaps.
- Product Categorization: ChatGPT generated initial category suggestions and proposed UNSPSC code assignments for all products. | Human Intervention: Resources mapped every code using the UNSPSC reference portal and corrected any outdated or ambiguous classifications.
- 05
Product Data Extraction, Enrichment, Categorization, and Quality Review
The following outlines how each stage of the engagement was executed, from initial onboarding through final delivery:
Client Project Initiation
- Onboarded an eCommerce reseller needing full data pipeline automation.
- Key focus: Scraping and enrichment of 2M+ SKUs' product data.
Data Scraping
- Developed custom Python scripts per brand website.
- Addressed unique site architectures and anti-bot measures.
- Designed scripts for scalability across high-volume, multi-site extraction.
- Conducted manual reviews to verify data accuracy at the extraction stage.
Data Cleansing, Standardization & Enrichment
- Standardized extracted data—removed special characters and applied consistent formatting.
- Used ChatGPT to fill missing product attributes (descriptions, weights, categories).
- Validated AI-generated entries through human review and in-house database enrichment.
Custom Taxonomy & Product Categorization
- Built custom taxonomy framework using ChatGPT for classification.
- Assigned UNSPSC codes to all products.
- Human oversight applied to review and correct AI-generated assignments.
Project Completion
- Delivered enriched, categorized product data with 99.8% accuracy.
- Achieved a 78% increase in operational efficiency through strategic task automation.
Results Delivered
Operational Gains Across Taxonomy, Data Quality, and Workflow Efficiency
- Products Categorized
- 2M+
- Products Categorized
- A fully structured product taxonomy was built and deployed. Team4eCom eliminated categorization gaps that previously affected product discovery and made browsing less intuitive.
- Data Accuracy
- 99.8%
- Data Accuracy
- Error-free product data was delivered through a combination of automated cleansing workflows and strict manual quality checks at every stage.
- Efficiency Increase
- 78%
- Efficiency Increase
- Operational efficiency improved through automation across data scraping, enrichment, categorization, and standardization, reducing manual effort and improving delivery timelines.