How atronous.ai is revolutionizing Product Taxonomy with ML and Embeddings
- Jayashree Dutta
- Apr 8
- 3 min read
In the constantly changing realm of Ecommerce, properly organizing and categorizing products is crucial for smooth navigation and discovery, as well as for boosting conversion rates. The significance of an effective product taxonomy cannot be overstated, as it directly influences the customer experience. When users can easily find what they are looking for, they are more likely to complete their purchases, leading to higher sales and customer satisfaction. Recent studies have shown that well-structured product categories can significantly reduce bounce rates and improve the overall user experience on Ecommerce platforms.
Recently, atronous.ai launched an advanced machine learning pipeline that automates, enhances, and accurately handles product taxonomy assignments, minimizing manual work and inconsistencies. This innovative solution leverages cutting-edge algorithms to analyze vast amounts of product data, ensuring that items are categorized in a way that reflects both their attributes and customer search behavior. By utilizing natural language processing and deep learning techniques, the system not only streamlines the categorization process but also adapts to emerging trends and user preferences in real-time.
🧠 The Challenge: Automatic Classification
Retailers often receive product data in various formats: images, URLs, spreadsheets, or PDFs
from a wide range of suppliers. These items lack standardized taxonomies or come with misaligned categories that don’t fit the retailer's existing classification system. This leads to a fragmented shopping experience and lost revenue opportunities.
🔍 Step 1: Identifying the Object from an Image URL
Using advanced computer vision models, the platform ingests product image URLs and identifies the object type in the photo. For example, in a recent engagement with a leading retailer , the platform processed entries such as:
Using a combination of convolutional neural networks (CNNs) and pre-trained models (like CLIP or BLIP), the system outputs a probable object class like Armchair, Sofa, or Barstool, without any manual labeling.
🧬 Step 2: Training on the Retailer's Current Taxonomy
What makes atronous truly adaptive is its ability to learn from the retailer’s own classification structure. By training on the existing taxonomy tree, whether pulled from a Shopify store, ERP system, or internal database, the ML engine understands how categories are organized and how products typically flow through them.For this retailer’s use case, atronous automatically mapped these detected objects to specific categories such as:
Furniture → Seating → Armchairs
Furniture → Living Room → Sofas
Furniture → Dining → Barstools
🧩 Step 3: Assigning the Best Taxonomy Match
Once trained, the system uses similarity scoring between image embeddings and taxonomy embeddings to assign each product to the most appropriate category.For instance:- An image classified as Barstool with mid-century aesthetics was assigned to Furniture > Dining > Barstools with over 95% confidence.- A decorative stool misclassified in the source data was automatically reclassified as an Accent Piece based on visual and semantic similarity.This not only improves classification accuracy but allows the taxonomy to adapt dynamically to the retailer’s style and structure.
🧾 Bonus Step: Taxonomy Auditing & Alternative Suggestions
In a powerful fourth step, atronous audited the current taxonomy assignments and flagged inconsistencies or misclassifications. It also suggested potential improvements.For example:- Products categorized under Chairs that visually match Recliners were highlighted.- Items split across Living Room Decor and Accent Chairs were recommended for consolidation or reclassification.These suggestions are provided in an easy-to-review spreadsheet or directly synced into PIM systems.
🚀 The Impact
The implications of this technology are profound. For Ecommerce businesses, the automation of product taxonomy assignments means that they can allocate their resources more efficiently, allowing teams to focus on strategic initiatives rather than being bogged down by tedious manual categorization tasks. Furthermore, the accuracy of the machine learning model helps to eliminate inconsistencies that often arise from human error, leading to a more reliable and user-friendly shopping experience.
This AI-first approach has transformed how retailers handle product onboarding:
90% reduction in manual taxonomy assignment time
30% increase in accuracy of product categorization
Improved shopper UX via cleaner navigation and product grouping
As product catalogs grow and become more complex, automation like this isn't just helpful, it becomes mission critical.
Ready to Streamline Your Taxonomy?
atronous.ai is bringing intelligence to the very core of digital merchandising. If your team is struggling with inconsistent categorization, long onboarding cycles, or messy product feeds, we would love to help.Let’s talk. 💬
تعليقات