AI is reshaping how businesses carry out their digital marketing activities. One of the most helpful ways that AI is transforming business is in managing digital assets for e-commerce businesses.
The online retail industry has exploded and promises to keep growing. It is estimated that by 2040, 95% of all purchases will be online.
An e-commerce business has the potential for tremendous growth if you build one, but it’s also challenging to stand out from the competition.
One important factor that affects the success of an e-commerce site is the presence of images. Managing images for online retail websites is no easy task. There are several concerns that businesses need to keep in mind as they list products with their corresponding images.
- Uploaded images for each product need to be of high quality. You need to automatically detect whether your images are optimized for the internet.
- It’s essential to quickly flag inappropriate images and remove them before they become visible to the public.
- Businesses need to ensure that their sellers upload visuals that meet certain standards, such as specific dimensions, pixel values, and the absence of graphics like watermarks.
- Duplicate images must be removed.
- Images with copyright issues need to be found and moderated before it leads to a significant issue.
- The image descriptions and the content of the images must match the product.
When an e-commerce website has several hundred listings and has to manage images uploaded by multiple users, manual intervention is ineffectual. The concerns mentioned above are magnified when there are large numbers of sellers and products involved.
Images play a significant role in driving conversions for online stores. Since users cannot feel or interact with a product, they depend on images to convey accurate and helpful information. This is why businesses need to adopt AI and computer-vision technology in particular to manage their digital assets.
Computer vision powered by machine learning makes it possible to process images quickly and accurately. It can moderate and label large quantities of images in short timespans, outdoing human capabilities.
It’s helpful to understand some computer-vision technologies and how they process images. You can make informed decisions about the kind of technology and tools you can adopt to manage your digital marketing. You’ll also be able to provide more accurate information to your customers, which will boost conversions.
Let’s look at some key computer-vision techniques that can manage your online store’s images. Here’s a breakdown of object detection, image classification and other technologies that can help e-commerce businesses. By understanding how AI can boost your e-commerce business, you’ll better understand the kind of tools and platforms that will help you.
1. Object detection
Computer-vision tools can detect specific objects within an image and draw a bounding box around them. E-commerce websites can use object detection tools to find products within an image. This makes it easy to automatically detect products and process these images as needed. There are several applications of object detection:
- Identifying and flagging inappropriate images
- Helping users shop for objects or products within the image
- Ensuring the right image appears for the right products
Object detection removes the need for human intervention, which also brings down the error rate in processing images. You’ll be able to ensure that you’re helping customers find products they are looking for. Object detection also prevents negative experiences that can arise from wrong or inappropriate images appearing on your site.
2. Image classification
As the name suggests, image classification is where an AI model classifies images into different labels and categories. This is extremely important, as e-commerce platforms depend on clear categorizations of products. Categories help users navigate a complex site and easily find what they are looking for.
You need to ensure that the right products are placed under the right categories. If your website has content uploaded by multiple sellers, then relying on them to accurately categorize products may not be practical. Here are a few examples of ways that image classification can help an e-commerce business.
- Sellers’ products are automatically assigned to the right category by just processing the product images.
- It’s possible to filter low-resolution images. This is especially useful for digital asset businesses with subscription-based membership sites, such as a stock photography site that needs to host high-quality photos.
- Image classification will allow your business to automatically recommend products related to what a user is currently looking at. Producing recommended images and related products is an essential activity to boost sales in e-commerce. Computer-vision algorithms will help you to automatically generate the right product suggestions for your customers and improve conversion rates.
3. Multilabel classification
Multilabel classification makes it possible to assign images to more than one category or class. This is very relevant in e-commerce where products fit several categories.
For example, notepads can be assigned to the sections for office supplies as well as stationery goods. Toys are often classified under multiple categories, like learning or entertainment, or by age group and gender.
Multilabel classification is especially useful for online furniture and clothing stores. Such businesses need to apply different labels to single products, such as styles, textures, colors and materials.
Extracting attributes from product images, such as the material of a dress or the color of a piece of furniture, is important. This enables a business to provide users with accurate search results when they look for specific products.
Relying on manual effort to apply the right labels and tags to a product image is time-consuming. It’s also highly prone to errors. Instead, you can use computer-vision technology to simply process an image and automatically generate labels. This will boost your search capabilities and help you categorize products correctly.
4. Image similarity
Image similarity makes it possible to detect how similar or dissimilar two images are. An image similarity model will compare two images and score them between 0 and 1. Completely identical images will be rated 0, and dissimilar images will score closer to 1.
Image similarity has many applications in e-commerce. It’s especially useful for shopping on social media platforms and business apps. Platforms like Pinterest use computer-vision technology that allows users to find images similar to ones they like. This helps customers find products they can buy based on a picture they find on social media.
Today, many clothing companies enable buyers to click a picture of an item and then upload it to an app or a website. Image similarity makes it possible for a buyer to find the same item or a similar one on a retail site.
This technology helps users find products they like easily and quickly. This doesn’t just boost sales; it also increases engagement with your customers.
Another helpful application of image similarity is the detection of duplicate images and products. You’ll be able to moderate these duplicates, remove them from your database and save storage space.
Boosting your e-commerce business with computer vision
Online stores rely on images to provide information about products. These images need to be of high quality and accurately represent the product.
When businesses deal with a large number of products and images, managing them becomes difficult. Computer vision can substantially improve the quality and accuracy of the images that appear on your site.
In e-commerce businesses, where even the smallest signal of inaccuracy will lead to abandonment, it’s essential to use AI. There are many AI tools and platforms available that enable you to train a computer-vision model that will support your e-commerce business. Understanding how AI and computer vision can assist you will help you make better decisions to grow your site.
Read for more articles.