In today’s fast-paced global marketplace, high-quality product content is the lifeblood of online shopping and effective sales. It informs customers, builds trust, and ultimately drives conversions. But what happens when the very source of that vital content becomes a major roadblock?
That’s precisely the challenge a leading global distributor recently faced. Their product catalog boasted thousands of SKUs, each requiring detailed descriptions, compelling visuals, and essential documentation. The problem was, a lot of this information was hidden away in complex data sheets and PDF manuals on the manufacturer’s website.
Imagine the sheer manual effort involved. The distributor’s initial plan was daunting: hire dedicated resources to painstakingly browse the manufacturer’s site, copy-paste product details, download images and PDFs, and manually input everything into an Excel file. For over 2,000 products! This approach was not only time-consuming and prone to errors, but also a significant drain on resources and a major bottleneck in their go-to-market strategy.
Enter atronous: Turning Obstacles into Opportunities
Recognizing the inefficiency and scalability issues inherent in this manual process, the distributor turned to atronous for a smarter solution. Our team specializes in streamlining product content management, and we were eager to tackle this challenge head-on.
Here’s how atronous helped this global distributor break free from their content bottleneck and unlock significant productivity gains:
1. Intelligent Web Data Extraction and Parsing
Instead of a generic website crawl, we employed targeted web ingestion techniques using libraries and frameworks optimized for navigating and extracting data from complex website structures. This involved:
- Comprehending the semantic content of the page: We employed computer vision models capable of interpreting the page content to extract pertinent details like UPC codes, manufacturer part numbers, and other attributes
- Using Content Selectors as fallback: We utilized browser developer tools and techniques like XPath and CSS selectors to pinpoint specific HTML elements containing product titles, descriptions, image URLs, PDF links, and attribute tables.
- Robust Error Handling: We built mechanisms to gracefully handle website changes, broken links, and inconsistent data structures, minimizing disruptions and ensuring data integrity.
- Data Parsing and Structuring: Once extracted, the raw HTML data was parsed and structured into a usable format. This involved:
- Text Extraction and Cleaning: Eliminating unnecessary HTML tags, scripts, and styles to focus on the essential product details. Utilizing text cleansing methods to address formatting inconsistencies, spacing issues, and special characters.
- Attribute Identification and Extraction: Developing logic to identify and extract key product attributes often presented in tables or structured lists. This involved pattern recognition and data normalization to ensure consistency across different product pages.
2. Parsing Data and Images from PDF Files
When the manufacturer’s website hosted product manuals or specifications as PDF files, our ingestion process was extended to incorporate specialized PDF parsing techniques. Here’s a breakdown of the technical approaches involved:
- Link Identification and Downloading: Our web ingestion engine would identify links to PDF files within the manufacturer’s website, typically within product pages or dedicated resources sections. These links would be extracted, and the corresponding PDF files would be downloaded and stored securely, within their own cloud locations.
- PDF Content Extraction – Choosing the Right Libraries and Tools: Depending on the complexity and structure of the PDFs, atronous utilized various open-source and commercial libraries and tools specifically designed for PDF processing in languages like Python . For more details on this step, check out this technical blog
- Table Data Extraction:
- Heuristic-Based Table Detection: Algorithms analyze the layout of the PDF to identify potential table structures based on the presence of lines, spacing, and repeated patterns.
- Structured Data Extraction: Once a table is identified, more sophisticated parsing techniques are used to extract the data within the rows and columns, often requiring handling of merged cells and complex table layouts.
- Conversion to Structured Formats: The extracted table data is then typically converted into structured formats like lists of dictionaries or CSV-like structures for easier mapping and integration.
3. Automated Data Transformation and Template Mapping:
Data Normalization and Cleansing
- Before mapping to the distributor’s templates, the extracted data underwent a normalization and cleansing process. This included:
- Data Type Conversion: Ensuring data fields matched the expected data types in the target templates (e.g., converting text-based numbers to numerical formats).
- Units of Measure Standardization: If necessary, standardizing units of measure (e.g., converting inches to centimeters).
- Data Validation: Implementing rules to identify and flag potentially incorrect or missing data based on predefined criteria.
Template Mapping Engine
- We developed a flexible mapping engine that allowed us to connect the extracted and normalized data fields to the specific columns and structures of the distributor’s upload templates (likely in formats such as CSV or Excel). This involved:
- Configurable Mapping Rules: Providing an interface or configuration files to define the relationships between source data fields and target template fields.
- Data Transformation Logic: Implementing custom logic within the mapping engine to perform more complex data transformations if required (e.g., combining multiple source fields into a single target field).
- Batch Processing and Automation: Automating the process of applying the mapping rules to the extracted data in batches, generating the correctly formatted output files ready for upload into the distributor’s systems (e.g., PIM, ERP, e-commerce platform)
4. Multilingual Translation Integration:
- API-Driven Translation Services: To handle the translation of product titles and descriptions into Spanish and French, we integrated with reputable cloud-based translation APIs (e.g., Google Translate API, Amazon Translate).
- Automated Translation Workflow: The extracted titles and descriptions were automatically sent to the chosen translation API. The translated text was then received and incorporated back into the processed data
- Quality Considerations: While fully automated, we often recommended and implemented options for human review of translations, especially for critical marketing copy, to ensure accuracy and cultural relevance.
Technical Benefits and Impact
By leveraging these technical capabilities, atronous provided a solution that was:
- Scalable: Capable of handling large volumes of product data and adapting to future growth in the product catalog.
- Efficient: Automating the data acquisition and preparation processes, significantly reducing manual effort and time.
- Accurate: Minimizing the risk of human error associated with manual data entry.
- Integrated: Seamlessly fitting into the distributor’s existing systems and workflows through template-based output.
- Global-Ready: Our automated translation capabilities setup the stage for expansion into new markets.
Conclusion
The 80% productivity boost wasn’t just a headline: it was the tangible outcome of a powerful, automated technical infrastructure. By replacing a manual, resource-heavy process with an efficient, scalable catalog creation system, the distributor’s teams were freed from the grunt work and empowered to focus on high-value tasks like content approval.