Data Annotation’s Role in Visual Search & Stock Management

Retailers lose revenue due to poor inventory tracking and search issues. Discover how high-quality data annotation creates efficient AI-driven retail systems.

In recent years, consumer shopping habits have changed majorly, with over 56.6% having preferred to shop online in 2024. As shoppers look for faster, more personalized experiences, retailers are turning to AI-powered solutions to meet these growing demands. A significant development in this area is the integration of AI in retail operations, mainly in visual search and inventory management. 

Your top retail brands, such as Amazon, Walmart, Zara, and ASOS, have already implemented AI-powered systems to avoid overstocking and understocking situations and make product search simple through visual search. But how do brands ensure that these systems deliver precise results every time?

The efficiency of these systems depends on accurate data annotation –- the process of labeling training data so that machine learning models can understand and process it. The more efficiently and accurately these models will perform, the more effectively visual search and intelligent inventory management will work, helping both customers and retailers. 

So, let’s see in detail how data annotation for AI systems in retail improves visual search and inventory management.

Challenges Retailers Face Due to Poorly Annotated Data

Before jumping to how high-quality data annotation helps, it is important to realize what retailers are missing out on without it. Poorly annotated data can severely impact AI-driven visual search and inventory management systems in the following ways: 

• Incorrect Product Classification and Variant Detection

When product images in training datasets have broad or vague labels (such as categorizing all sneakers simply as ‘shoes’), AI-powered visual search systems struggle to recognize specific variations. This can lead to inaccurate product recommendations, displaying a range of items that may not match customer preferences. As a result, shoppers may struggle to find the exact product they need, negatively impacting their experience and reducing conversion rates.

• Stockouts

Misclassified products and unrecognized barcodes can lead to inaccurate inventory records, which prevents retailers from tracking stock levels correctly. When a product is low on stock or runs out, the system fails to generate alerts. As a result, the retailers are unable to replenish inventory on time. The outcome is frustrating for customers who don’t receive their products on time, which leads to negative reviews.

• Overstocking

At times, a product is recorded as two different products due to wrong labeling. For example, a gaming laptop is recorded as a business laptop and a gaming PC. The system may order both categories separately and create an excessive stock of the same laptop. Such overstocking ties up capital, increases storage costs, and risks product obsolescence, especially in industries like electronics, fashion, and fitness & sports, where trends change quite quickly. 

• Misplaced or Lost Products

Automated warehouse management systems depend on annotated data to track product locations through barcode labeling. If the barcode is assigned to the wrong product or a single barcode is linked with multiple products, AI-powered systems may register products under the wrong category, location, or SKU, leading to misplacement. This will delay shipping for retailers as they will have to spend time and resources finding the correct product. 

Thus, poor data annotation can lead to insufficient model performance, increased operational costs for retailers, and reduced customer engagement. 

How Does Data Annotation for Retail Improve AI-Powered Visual Search?

Customers are moving towards image-based shopping as it is faster and more accurate. All they have to do is upload a photo to find exact or visually similar items instantly in the retailer’s database. Reflecting this shift, the visual search market size is expected to hit USD 150.43 billion by 2032, with a CAGR of 17.4% between 2024-2032. However, the effectiveness of visual search heavily depends on high-quality annotated data. Here is how image annotation for retail can enhance the visual search capabilities: 

• Easy Product Detection

Bounding box annotation labels products in images by drawing rectangular boxes around them for easy identification and classification. When such a training dataset is fed into the AI models, it improves object recognition as visual search engines can detect and match products even when the background is cluttered, increasing the chances of conversions.

• Fine-Grained Attribute Recognition

Semantic segmentation labels every pixel in an image, allowing AI to distinguish intricate product details such as fabric textures, patterns, and design elements. This precision helps customers find products that closely match their preferences, even when subtle differences matter—like a specific lace pattern or a unique fabric weave. When an exact item is unavailable, AI-driven attribute recognition suggests visually similar alternatives, keeping customers engaged and increasing conversions for retail brands.

• Facilitates Multimodal Search 

By labeling text on packaging, labels, or product descriptions and annotating images, annotators create structured data for product searches. Multimodal searches then use this data to combine text-based search queries with image-based results. Well-annotated data enables AI to understand intent across different input formats, making searches more intuitive and efficient. This ensures customers find products faster and increases their likelihood of conversion.

How Does Data Annotation Drive Effective Inventory Management for Retailers?

Retailers can satisfy customers only when they are able to deliver the products without any delays at their doorstep. To achieve this, they use AI-powered inventory management systems that help them track, classify, and manage products across warehouses and storefronts. These systems often rely on high-quality data annotation to enable: 

• Real-Time Stock Monitoring

OCR annotation labels product names, SKUs, expiration dates, and serial numbers from scanned images. This trains AI models to recognize these aspects and instantly update inventory databases when products are added, removed, or moved within the warehouse. This way, retailers can detect stock fluctuations and replenish them as and when needed. 

• Faster Processing with Barcode Labeling

Many retailers depend on barcodes to track products. Properly labeled barcodes enable automated product identification, reducing manual work and processing times. This improves the speed of product checkouts, helps in faster inventory audits, and ensures order fulfillment. 

• Forecasts of Stock Requirements

Do you know that Walmart employs AI-based stock forecasting across the globe to control inventory and prevent stockouts? While this is just one example, many brands are using properly labeled SKU data, location-based sales data, and logistics and supplier performance data from the past to train AI models to analyze trends and predict future stock needs. This way, high-demand products are easily restocked by sellers.  

• Improved Warehouse Automation

Accurate bounding box annotation marks the products in the training dataset to locate products on shelves, conveyor belts, and pallets. For retailers who require more precise identification, instance segmentation outlines individual products pixel by pixel, helping AI differentiate between similar-looking items. Such annotated data helps AI-powered retail inventory management systems track product locations and prevent stock misplacement. When the exact location of the product is known, workers or robots can pick the product faster, thereby reducing search time. 

How Can Retailers Achieve High-Quality Data Annotation?

So far, we know that accurate data annotation is essential for the proper functioning of AI-powered systems for visual search and inventory management. But how can retailers actually achieve this? To label training data, retailers can build in-house teams of professionals or outsource data annotation to experienced service providers. Both approaches need careful planning to ensure consistency, accuracy, and scalability. Let’s talk about each approach in detail:

• In-House Data Annotation

The in-house approach gives you more control over the process as you can hire and train annotators according to your needs. However, this makes it time-consuming and costly as well. 

Key Considerations:

• Ensure data security by implementing role-based access control, multi-factor authentication, security protocols, and encryption. 
• Ensure the team understands product taxonomies and SKU structures specific to the retail brand.
• Conduct ongoing skill development programs to keep up with evolving AI and annotation standards.
• Invest in AI-assisted annotation tools that support different annotation techniques for diverse retail needs.
• Ensure that the data complies with GDPR, CCPA, and other rules laid down by the government.

• Outsourcing Data Annotation

Outsourcing, on the other hand, is a more practical and cost-effective approach for large-scale data annotation, as you can get access to a dedicated team, advanced tools, and subject matter expertise to maintain contextual relevance and accuracy across annotations.

Key Considerations:

• Look for providers who have vast experience in providing image, text, and video annotation services, ensuring high accuracy and consistency.
• Make sure they allow businesses to scale annotation efforts quickly, making it easier to handle large volumes of product data.
• Ensure they implement rigorous quality assurance measures and comply with data security regulations.
• Check their pricing models (whether they have pay-as-you-go models or long-term contracts) to find a plan that best fits your budget and business needs.

Conclusion

Data annotation enhances visual search by helping AI accurately recognize products, improving search relevance and customer experience. In inventory management, it enables precise tracking, faster picking, and optimized warehouse space. Thus, leveraging quality annotation ensures both seamless shopping and efficient retail operations. To implement data annotation, choose an approach that aligns with your budget, project needs, and long-term goals, whether it is building an in-house team or outsourcing data annotation services to an experienced provider. Most importantly, remember to regularly update training datasets with new product images to ensure AI systems align with changing retail trends.