Annotation For Retail And eCommerce
In the era of the experience economy, every customer wants tailor-made products that fit their needs. eCommerce and online retail platforms are now using Al-based solutions to intrigue customers. AI helps these companies deliver refined search results and suggest relevant products to online shoppers based on their preferences.
Data annotation is imperative to all Al/ML-based operations. The data annotation tools market is expected to reach over 10 billion USD by 2028. It refers to the categorization and labeling of data based on its specific use. High-quality and accurate data annotation requires human effort where users label and classify information.
Based on this data, AI/ML models then apprehend essential attributes from the database. With the help of data annotation, Al also analyzes different product attributes and recommends related products in online and offline shops.
Data Annotation - Use Cases And Benefits To The Retail And eCommerce Industries
Following are the ways in which data annotation can take retail and eCommerce businesses to the next level. By powering the AI-ML engines with the right quality data, retailers and eCommerce businesses can create a tremendous competitive advantage.
Hyper-Personalized Marketing Campaigns
Data annotation in eCommerce can be used to improve the online shopping experience by targeting each customer with specific campaigns picked based on their interests. Since it is impossible to customize marketing campaigns for each customer manually, retailers can leverage Al-based automation to scale such tasks.
Hyper-personalization of marketing strategies provides benefits in terms of improved productivity, efficiency, and revenue generation. According to research by PWC, 12% of online shoppers preferred buying from a retailer that sent them personalized offers.
Visual Search
Data annotation enables visual search by appropriately labeling similar product images. This makes it easy for ML models to recognize the product when a customer searches for them online. To make this happen, a large amount of data needs to be labeled to identify and track various facial attributes to recognize faces.
Demand Forecasting
Most eCommerce businesses use AI-based retail analytics solutions for demand forecasting. Such solutions help businesses analyze and optimize sales performance. Sales leaders can dive deeper into their data using interactive dashboards and slice and dice the data for deeper analytics.
They accumulate all the data to create a complete picture of all transactions. This includes mode of purchase, customer demographics, discount coupons applied, returns, and refund queries. All such analysis is possible only through correctly labeled data.
Trend Identification And Recommendation
In retail, identifying customer trends is crucial for a successful business. Companies analyze huge amounts of data to understand their customers, data generated by creating multiple customer touchpoints.
This helps them interpret what their customers want so that they can push out relevant offers to attract more and more buyers. This process of trend identification can be made easy using annotated data that is created based on a specific use case.
Email Marketing
Data labeling can also be used to design email marketing strategies by analyzing customer patterns. Specific target campaigns, as discussed above, can hence be deployed based on the customer’s preference. This not only helps retailers bag new sales but also ensures active engagement and retention of existing customers.
Semantic Segmentation
Semantic segmentation is a process of assigning labels to images. However, unlike traditional labeling, it treats different objects of a single image as separate entities. It can even be used to provide different labels for every pixel in an image.
This type of data annotation can be leveraged by online retailers to better model a clothing item to produce a better fit and a detailed output image. This also helps in maintaining a consistent quality score across different products.
Market-Basket Analytics
Market-basket analytics refers to the use of data annotation to pair related products. For example, if a customer buys a mobile phone, they most likely would like to invest in a cover and tempered glass as well.
AI engines can predict and recommend such combinations to customers providing them with a convenient purchasing experience. This also improves sales conversion by marketing multiple products at once.
SCM Analytics And Inventory Management
Data annotation in SCM and inventory management plays an important role in developing robust demand forecasts. Why is it important? Let's take the example of the COVID-19 pandemic. There was a major stockout situation for several products like toilet paper.
Although such a rise in demand increases short-term profit, in the long run, it simply causes walkouts. It is the worst situation for any retailer. Such a situation can be avoided by using AI solutions that can perform real-time data analytics across multiple outlets.
It prevents the out-of-stock inventory situation and ensures a seamless experience for its customers. These AI solutions need to be trained based on accurately annotated data.
Image-Product Tagging
In online retailing, it is important to synchronize product images with their descriptions. While images should aim to get customers interested in the product, their descriptions should compel them to make a purchase. AI-based data annotation can automate this process by optimizing image-product tagging.
Conclusion
In online retail and eCommerce, data annotation fuels the AI system to automate product search and purchase. At EnFuse, our team of data annotation experts provides highly intelligent platforms to identify and annotate specific data, so businesses can leverage machine learning and artificial intelligence. Connect with us to know more!
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