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AI-Powered Auto Tagging for Fashion Retail

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The Problem






Unsatisfactory Shopping Experience

A principle part of retail catalog management is consistent taxonomy because shoppers use text search on e-commerce websites to find products. Inconsistent and inaccurate tagging affects search results, impedes product discoverability, and leads to a frustrating online shopping experience. As a result, the customer abandons the site.

01

High Operating Costs

Manual tagging costs retailers time, money, consistency, and accuracy. Human errors lead to discrepancies in tags and products incorrectly labeled products. For example, cultural and geographical gaps result in the same images being tagged differently and minimize the searchability of products.

02

Loss of Revenue

Customers leave websites if they cannot find what they need. As a result, lousy e-commerce catalog management is the easiest way to lose a customer and revenue.

03

Poor SEO

Lack of accuracy in tagging also affects SEO—poor descriptions, titles, and tags impact product searchability on the web.

04






The Solution


Machine learning and image recognition automatically detect visual attributes, analyze and label product images in an e-commerce catalog. AI-powered product tagging saves time and money needed for manual tagging (up to 90% of operational costs) and boosts revenue.
Automatic image tagging software helps the products appear in relevant search results. It assigns them to specific categories and attribute labels to improve product recommendations. For example, if a product catalog has an image of a red dress, image recognition and machine learning extract color, category, and subcategory. Such as "ankle-length, sleeveless, U-neckline, red maxi dress". The retailer can review and approve these tags with one click.
Our powerful image auto-tagging solution breaks down images of fashion styles into the smallest detail, including the type of collar, silhouette, heels, accessories, sleeve length, and many more.
By achieving automation, online retail businesses can focus on expanding their product portfolio, tapping into new markets, and maximizing conversions on their websites.






How does SalesVision's auto-tagging work?



SalesVision's automatic fashion tagging uses deep learning and advanced image recognition algorithms to tag categories and subcategories from any image. It performs product scans, detects clothing in pictures, and breaks down the style and color details in minutes.




We achieve up to 98% accuracy

Using years of experience in Artificial Intelligence and deep learning, our data scientists continuously improve precision rates. We achieve high accuracy even for the images that are challenging for the human brain to process.







Why is our auto-tagging solution different?



Powerful Technology

At SalesVision, we complement the object detection model with object segmentation. Object segmentation accurately extracts each style element's pixels, finds its distinctive attributes, and precisely determines colors.

Precision

Many companies require product images to have a specific aspect ratio, resolution, and background to tag them correctly. Our software's precision is not affected by the background, aspect ratio, or the quality of the photo. We use deep learning methods and Convolutional neural networks that are the most advanced tools for automatic image analysis.

Improved SERP Ranking

As a result of precise auto-tagging, you get accurate and SEO-ready titles, descriptions, and tags. They boost your product discoverability and enrich the metadata of the catalog. Taking your product to the very top of the search results increases website traffic and boosts sales.

Enhanced Personalization

You can choose whether to use our cloud platform or opt for a fully independent solution and take full control of data. Our data science and AI experts will guide you through the entire process, from demo to implementation.






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