Image recognition, machine learning and visual search have changed the way we interact with the world. This latest development in machine learning has transformed the e-commerce industry. Automated tagging of images play a crucial role in visual search. In an age of instant gratification, consumers follow the snap, find, and shop technique. The user clicks a product picture, finds a similar item on an app, and purchases it. Discovering products becomes much easier using tagged images rather than text-based keywords.
Automated image tagging in e-commerce helps companies to enrich and organize their product catalogs. In this article, we talk about the benefits of automated image tagging for the e-commerce industry.
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What is automated image tagging?
Automated image tagging enables machines to assign relevant keywords to digital pictures. It is also known as automated image annotation. This process is used in image retrieval systems for organizing images in a vast database. It is a part of multi-class machine learning image classification.
Let's take a real-life example. Suppose a wedding album is scanned and uploaded to a computer system. Then this digital album is transferred to a personal website. As every new image gets uploaded a software program filters specific details. If there is an image for the ring exchange ceremony, it's tagged as ‘engagement ring’ or ‘wedding ring’. With the help of Image recognition machine learning, post upload, the user can search for a particular image with the help of keywords.
Automated image tagging using machine learning
As an e-commerce platform, cart abandonment must be your worst nightmare? You would never want visitors to get frustrated and abandon their shopping carts. Suppose your site generates results for men’s activewear when someone looks for men’s formal wear. How will you feel as a customer? Surely disappointed.
We all know that image tagging is important for the success of an e-commerce business. How can we do it using machine learning?
Here is a quick example. Suppose, an e-commerce catalog contains the image of a 'pink party dress'. Image recognition machine learning technology can label it into ‘boat neck’, ‘flared’, ‘sleeveless’, and further subsets. All these tags will be generated automatically and can be approved through a single click.
Let’s take a look at the benefits of using machine learning in tagging images:
● Derive accurate search results
● Capture customer’s attention through relevant products
● Leverage social media for instant fashion updates and spontaneous purchases
● Improve customer service experience through visual search chatbots
How can you benefit from automated image tagging in e-commerce?
Automated tagging of images proves effective in category navigation. It helps e-commerce companies to remain competitive and improve customer engagement. Here, we discuss few benefits associated with automated image tagging in e-commerce:
● Ensures consistency - Manually curating images may include some flaws and end results might be inconsistent. The use of machine learning makes the tagging process quick and accurate. ML algorithms automatically analyze pictures using keywords ensuring consistent image categorization.
● Saves costs - The manual process of sorting and arranging a huge collection of digital photos is cumbersome. Tagging images manually involves a lot of time and money. Automated image tagging in e-commerce makes this process convenient and cost-effective.
● Improves accuracy of search results - Most fashion retailers are including visual search along with text search for efficient results. Amazon Fashion introduced StyleSnap feature that leverages image recognition machine learning technology for an amazing product discovery experience. Users can take a picture of the inspired look and find similar items on the Amazon app. Now shoppers need not juggle through endless options to spot the perfect faux fur coat.
● Better search recommendations - Colors are a trendsetter when it comes to fashion. With advanced computer vision technology, e-commerce websites offer a wide palette of color shades. A user gets automated color-biased search results and customized recommendations. For instance, someone buys an attractive pair of shoes in a shade of brown. He will receive recommendations for other accessories in the exact color shade. This drives an increase in basket size and boosts sales.
Automated image tagging in e-commerce provides all product information, accurately structured, through image recognition machine learning.
E-commerce brands using AI for image tagging
Asos - Prominent fashion e-retailer Asos offers a range of more than 85,000 products. Asos launched a visual search tool to deal with this huge inventory. This tool makes the search process quick and satisfying. With the help of image recognition machine learning, customers can navigate to their choicest products conveniently. This fosters prompt product discovery. It also helps users make most of every fashion inspired idea.
Forever 21 introduced an AI-based visual search tool for its e-commerce business. The tool was named as ‘Discover your style’ and helped in increasing sales conversions. Initially, it was available for the ‘dresses’ and ‘top’ categories. Later the tool was introduced for the remaining categories as well.
eBay introduced two AI-based visual image recognition machine learning features, ‘Image Search’ and ‘Find It On eBay’ for effective customer engagement. eBay took this initiative as image and video are expected to constitute about 50% of searches. Besides, most consumers are inspired to purchase products based on what they view.
AI in e-commerce is the need of the hour. Do you want to fast track your digital transformation journey? Skyl.ai has the right solutions to boost your conversion rates. We use machine learning to cater to the unique requirements of the e-commerce industry, including Image-Product Tagging & Quality Verification.
Get in touch with Skyl.ai team and try out our machine learning image classification API here.