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How AI Image Recognition Is Transforming eCommerce Marketplaces

Image recognition is transforming the way online users shop for products. In the past, you had to physically go and look for products that you wanted to buy that looked similar to something you...

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Image recognition is transforming the way online users shop for products. In the past, you had to physically go and look for products that you wanted to buy that looked similar to something you wanted. Machine learning and data science have turned this model on its head. 

For example, in the fashion space, users can snap a picture of their favorite look and run it through a search engine. The engine then spits out hundreds of products that look similar to yours, based on various data tags and labels. This is especially popular among millennials and generation Z users who value speed and the ability to shop using their smartphones.

The global visual search market is on track to hit $14 billion by 2023, and it's going to become the standard for all eCommerce brands and marketplaces. The challenge is in building the machine learning algorithm correctly. The fact that remains is this: machine learning models need human intervention, and human data labeling plays a vital role in the ability of eCommerce brands to go live with AI image recognition.

What is AI Image Recognition? 

AI image recognition it's a technology used in visual search that allows the user to view search results in visual form. The search uses real-world images instead of text and works by having a database of image tags. The software identifies objects, places, people, and text in an image and then stores it in a database which allows users to search for similar-looking products using images. 

Examples of AI Image Recognition in eCommerce

The first example of AI image recognition came from Pinterest, the social media platform. They were the first to launch an image search that allowed users to search for similar-looking images. It completely transformed the shopping experience. Today, its users conduct 600 million visual searches per month, with a 15% increase every year.

Think about it. Instead of clicking on different product pages to compare them visually, users can click on a button and see all the similar images of hundreds of products at once. Users can shop from their phones by uploading photos and finding similar outfits to what they were wearing on their birthday or at a party. 

Several marketplaces have already launched AI image recognition features that dramatically transform the shopping experience. For example, Facebook Marketplace has an AI assistant that generates metadata and product data labels from images and offers users personalized shopping recommendations. eBay has a similar feature that helps users list items faster, and Amazon launched an AI-powered fashion search that uses machine learning to find similar clothes and styles.

Ecommerce brands are also using visual search, and there are many examples of this. ASOS launched a visual search on their mobile app called StyleMatch, which lets users upload an image and find the closest brand and style to it. 

IKEA launched a visual search feature by integrating its entire catalog with the visual search engine on Pinterest. Since then, the world's most famous home decor brand has launched an augmented reality app called Place, where users can use visual search to shop for products and see them displayed in their space before they decide to buy.

Bringing Digital Customer Experiences to Life with Data Labeling

Ecommerce brands need human data labeling to train AI models to deliver AI image recognition features at scale. Datasets and machine learning algorithms have to be updated and improved regularly if a brand wants to get accurate results. 

What is Human Data Labeling? 

Human data labeling is when a human labels images and helps train your machine model. There are several approaches that exist. One is known as human-in-the-loop data labeling, which uses aggregation techniques to produce large datasets that are resistant to the mistakes of an individual. Other approaches include the machine doing most of the data and a human correcting it from time to time and tweaking the model to improve its accuracy.

The Value of Human Data Labeling

Machine learning has its limits. First, a machine is trained on a subset of your raw data, which has been labeled by humans. And then, the machine goes out to replicate the same process for other parts of your data. Where it will have more confidence in its accuracy, it will do on its own. While in the parts where it's less confident, it will require a human being to go in and label the data. This produces a much more accurate system, and over time the machine algorithm learns the right way to label the data. 

However, to make this system efficient, a business needs an industry expert that can interpret the data and label it correctly. Most companies don't have the time or resources to train a team of experts for this task, and that's why so many brands outsource their data labeling operations to companies like Helpware. 

How Helpware can help eCommerce Brands with Human Data Labeling

Helpware has a team of dedicated experts with varied expertise. We are able to provide eCommerce brands and marketplaces with the right experts to be able to interpret the data and maximize the efficiency of the image recognition algorithm. This makes the adoption of AI technology much easier and more streamlined for eCommerce brands. We can handle various tasks like image processing, data labeling, natural language processing (NLP), data tagging, data digitization, and much more.

Getting Started with Human Data Labeling 

Human data labeling is a critical component of any AI image recognition feature. Without it, brands can spend weeks and months creating machine learning models that aren't accurate and don't ultimately help the user find what they are looking for using visual search. Helpware is a micro-tasking platform that helps brands create and streamline their AI operations with the right team of experts.

Want to learn how we do that? Get started today

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