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Implementation of AI and Data Annotation for E-commerce

We live in an experience economy, where customers want a personalised experience. Instead of the one-size-fits-all strategy used in the past, they would prefer to visit an eCommerce store and have their needs catered to them individually. Businesses all over the world are using AI to provide consumers with unique items that they are most likely to be interested in.Quality data annotation, on the other hand, will be crucial in the development of cutting-edge AI technology.

In this article, we’ll look at how eCommerce companies are integrating AI into their service offerings, as well as the types of data annotation that are needed to do so.

AI for Ecommerce

Artificial intelligence (AI) has drastically changed the world of online shopping. It provides services to customers in many ways from ensuring security to providing assistance and making things in a more proper and easy manner. It helps the Retail/ e-commerce space to provide services to their customers on the next level and create satisfactory online shopping experiences.

Due to intelligent solutions that are helping to transform the e-commerce market, AI is one of the fastest technical achievements. With the support of data annotation and labelling services offered by Data Labeling companies like TagX, which make complicated tasks simpler, AI and Machine Learning are assisting in delivering the best and most stable shopping experience.

Both retailers and consumers benefit from AI, data labelling, and data annotation in online shopping. Many e-commerce companies are already using AI to improve user experience, and many more are in the process of doing so. Improving the quality of the search engine using machine learning is one of the most critical and beneficial tasks in the retail/ecommerce space.

E-commerce Use-Cases

Product Recommendation

A user’s search for goods and services on an e-commerce platform can be made simpler with AI. Customers search for convenience as a differentiating factor in an online store much of the time. By training algorithms to connect products with keywords, AI will recommend products to us and assist us in doing so.It turns the buying process efficient for the customer and drives sales for the business.

Visual Search

E-commerce platforms that use Computer Vision to incorporate a visual search feature allow customers to take a picture or upload an image of an object of interest. AI analyses the item’s characteristics and may suggest related items in their online and offline stores. Options to narrow down search results by personal preference can be added to this recommendation engine.AI ensures customers find exactly what they are looking for each time they visit the platform, greatly increasing sales revenue and opportunities to provide further product recommendations.

Product Review Moderation

Customers may use product review pages on ecommerce sites to interact with the website and provide feedback. The website has no influence about how consumers react to good or poor goods, but it does have control over which reviews appear on the site. It is important to ensure that the product review page does not contain any offensive language or content. This can be ensured and screened by the Natural Language Processing capabilities of AI.

Image-Product Tagging

The most effective way to draw a customer’s attention is through visual representation. Particularly in e-commerce, where physical stores have the benefit of touch and feel. To prevent consumer misunderstanding, the images and product description should be identical, and the image quality should meet the website’s expectations.You can train the system through ML to tag specific descriptions and keywords with images and to verify the quality of those images simultaneously.

Data Annotation Services for Ecommerce

Content Moderation

Our team helps Ecommerce-oriented clients keep content secure and reliable on marketplaces and aggregated data pages. Not-safe-for-work tagging and platform moderation are examples of work.

Categorization

To boost search relevance and customer experience for online customers, teams of content experts quickly and reliably categorise e-commerce content by multiple attributes.

Deduplication

By enhancing product discovery, you can reduce consumer dissatisfaction and speed up the checkout process. Deduplication and the removal of obsolete listings are crucial.

Data Matching

Our Tagging work, which is applicable to product data matching, keeps e-commerce listings up to date in real time and gives retailers a competitive edge in terms of product pricing.

Conclusion

If you’re an eCommerce company that hasn’t embraced machine learning, you’ll be left behind. After all, the advantages of technology to your industry are numerous. Machine learning can help you improve your efficiency in a variety of areas, including customer service and inventory management.

It’s also simpler than you would think to take advantage of solutions in the field. By learning more about the fundamentals of e-commerce machine learning, you’ve already taken the first step. All that’s left now is to figure out what you want the technology to do for you and get to work.