A Complete Guide on Implementing Visual AI in eCommerce Business
Are you looking to take your eCommerce business to the next level? Chances are Visual AI, or computer vision, is the best solution for you. A sub-domain of artificial intelligence, visual AI carries in it the maximum potential to automate multiple operations and improve customer experience with greater accuracy. In fact, the many benefits of Visual AI in the eCommerce business, which we’ll discuss later in the blog, will lead to the market’s CAGR growing up to 46.9%, making it worth over 95 billion USD by 2027.
- The visual AI eCommerce market is growing at a CAGR of 46.9%, which will make it worth over 95 billion USD by 2027.
- Visual AI is the sub-domain of AI that can interpret visual data, such as images and videos.
- You can build visual eCommerce by using applications like visual search engines, virtual merchandisers, description generators, and recommendation engines.
- While high cost, data security concerns, and algorithm biases are intimidating challenges associated with visual AI, you can overcome all these with the right development partner.
What is Visual AI?
Visual AI is the sub-domain of artificial intelligence that uses computer algorithms to interpret and understand visual data, such as images and videos. Some major applications of visual AI include facial recognition, object detection, and image classification. All these applications, together and separately, can be used to automate various processes, such as product sorting and inventory management for an eCommerce business. By automating such tasks, the business can achieve optimal optimization in its resource utilization and workflows.
Other than process automation, visual eCommerce applications can also assist eCommerce businesses in enhancing their customer experience. It can be used to create product recommendations, detect product defects, automatically tag products with keywords, identify customer preferences, and suggest related items.
Overall, Visual AI has the potential to significantly improve net revenues for eCommerce stores. By automating time-consuming and labor-intensive tasks, eCommerce stores can reduce costs and provide customers with faster and smoother shopping experiences. They can then easily gain a competitive edge in the ever-changing and increasingly competitive online retail landscape.
How You Can Implement Visual AI in Your eCommerce Store?
To successfully implement visual AI in your eCommerce business, it is important to understand what you want to achieve with it. Visual eCommerce applications can be used to help identify customer preferences, predict customer behavior, personalize their experience, and improve on-site product and category management. Since there are so many use cases for visual AI, it is important to clearly define your objectives and the use cases you would want to invest in. This decision should depend on core factors like the types of data you need to collect, the budget required, and prospective ROI. Here are some more details on varied visual AI applications that you can use-
Visual search is among the most exciting retail AI solutions that enable customers to search for products using an image instead of words. Such applications of visual search in eCommerce stores can be accomplished by uploading an image of a product on the visual search engines, which can recognize the product and provide relevant results from its non-structured database of product images. This allows customers to quickly find the product they are looking for without having to type in a long search query. The application of visual search in eCommerce can also be used to detect objects in images, allowing customers to find products they may not have known the exact name of or could not have described accurately with words.
Writing an accurate technical description of a product can quickly become a laborious and time-consuming task. By using visual AI, eCommerce businesses can bring more accuracy and efficiency in the process. It can quickly and accurately extract product information like product name, color, size, and price from the images present. This can save significant time and resources for eCommerce sites, as they no longer need to manually enter product information into their website. Using AI to check to automate tagging also helps in identifying any potential problems or discrepancies that could cause customer dissatisfaction
The automatic tagging functionality of visual eCommerce stores can help retail businesses in more than one way. One is by writing technical descriptions, and the second is by implementing artificial intelligence in eCommerce visual merchandising. In general, visual merchandising is the practice of promoting the sale of goods through the use of exciting visual displays. Merchants use aspects like the product and shelf design, color, lighting, and other techniques to create an appealing and eye-catching environment for shoppers.
It also involves organizing product displays and utilizing window displays to draw customers in and create a positive shopping experience. By implementing eCommerce visual merchandising through visual AI and automatic tagging, online stores can automate the creation of memorable and personalized shopping experiences. Based on users shopping history and preferences, the algorithm can redesign the landing page, homepage, and category page, bringing changes that directly affect engagement and conversion rates.
Product Description Generation
The information a visual AI application gathers through automatic tagging can then be used to generate rich product descriptions that accurately describe the product, increasing the chance of successful sales. Additionally, visual AI can be used to analyze customer images to generate custom product descriptions tailored to the individual customer, creating a more personalized shopping experience. In any case, it is a well-known fact that by providing an optimized copy for a product description, eCommerce stores can improve their conversion rates. Therefore, by automating product description generation through visual AI, companies will not only save time and resources but can also easily increase their sales as well.
Other than using the automatic tagging data to generate product descriptions, you can also take AI development services for your eCommerce store to make intelligent product recommendations for customers. By analyzing the visual characteristics of products that a customer has previously purchased, Visual AI can identify similar items that may be of interest to them. This helps to create a more personalized shopping experience, which can increase customer loyalty and engagement.
For example, if a customer has previously purchased a certain brand of dress, Visual AI can suggest similar dresses from the same brand or other brands with similar features that may appeal to the customer. This helps to ensure that customers find products that they like quickly and easily, which can encourage them to keep coming back to the online store.
AR-based shopping is the most exciting application of visual AI that creates an immersive shopping experience for customers. Through it, users can easily visualize how different items on a visual eCommerce store would look in their homes or on their bodies. It helps in making the online shopping experience more realistic by allowing users to virtually try on clothes, see how the furniture fits in their homes, and compare different products against each other. Another way visual AI combines with AR technology to make eCommerce businesses more advanced is by utilizing object recognition models. This allows users to pan the camera over a real-life object and then instantly check for similar products online through visual search engines.
The object recognition application of visual AI doesn’t only help in creating an immersive virtual shopping experience. Instead, it also helps in creating a more personalized shopping experience as well. You can easily hire AI/ML developers to build such an application of visual AI in eCommerce that can identify similar items in the inventory database. It can compare the features of a product a customer is interested in with other similar products in the database. This allows businesses to quickly identify products that are similar and then utilize them to cross-sell more items on their website.
Image Quality Control
Visual AI can help eCommerce sites ensure that product images are of high quality and resolution, as this can have a significant impact on customer satisfaction and conversion rates. It can analyze product images for any defects or flaws by carefully evaluating factors like lighting, shadows, color, texture, and size. It can also detect any discrepancies between images and their respective product descriptions.
In the end, visual AI can be used to maintain quality assurance in terms of images used on the website. It can automatically highlight images that do not meet the website’s standards in terms of clarity, size, and orientation. This can help to reduce the time and cost associated with manual inspection and quality control of images.
Benefits of Using Visual AI in eCommerce
All the above-mentioned use cases of visual AI in eCommerce have a wide range of benefits. It can help businesses become more efficient, profitable, and scalable, all through minimal effort. Here are some of the other major benefits of implementing visual AI in eCommerce.
Enhanced Customer Experience
The foremost benefit of visual eCommerce is how it enhances customer experience with the brand. It can easily curate a more personalized and tailored shopping experience. It can automatically detect what type of product a customer is looking for and recommend similar products that they would be interested in. For example, if a person has shown interest in a red dress, the website’s landing page for the product can automatically be customized to show other red dresses on the website.
This will help customers find the exact product they are looking for in a much more efficient manner. This personalized result can then be stored with the customer’s profile to understand their overall shopping preferences. So, the next time the customer visits the website, they will find products and services they are most interested in. This enhanced personalization will help improve customer experience and, eventually, loyalty to the brand.
Improved Product Discovery
Through visual search in eCommerce and other applications like product matching and similar product descriptions, eCommerce website owners can easily enhance product discovery on their websites. Let’s consider the example mentioned in the point above again; if someone searches for a red dress, the website can then showcase to the user some of the less discoverable red dresses on the website. They can also use the bought-in-combination section to showcase matching boots and other accessories to improve their visibility. This way, customers can quickly find the products they would be interested in but might not be actively looking for.
Better Product Images
The automation of image quality control and assurance will improve the website’s graphical content effortlessly. Visual AI can automatically detect low-quality images and flag them to the admin. This will help maintain a better quality of the website, eventually helping with the customer experience as well. Visual AI can also be used to enhance product photos and make them look more appealing and professional. This will help eCommerce businesses attract more customers and improve conversion rates.
Reduce Product Returns
Visual AI can be used to automatically tag products into categories. Like with any other process, automation will help in improving the accuracy of product descriptions and technical details. Moreover, it can also help in producing and selecting more descriptive images of the product in use. This will eventually help customers get better and more accurate visual detail of the product, reducing the return rates based on the product mismatch reason.
Challenges Associated with Using Visual AI in eCommerce
While there are many benefits of creating a visual eCommerce store, its implementation is not without challenges. Different applications of visual AI come with their own range of implementation challenges. For example, to facilitate automatic tagging, the eCommerce company will need to train the machine learning model on a huge dataset.
Therefore, whenever they implement a new range of products, their model will become outdated, requiring them to retrain the model, collect new data, and test the same. There are some other major difficulties that come along with visual AI implementation in eCommerce, which we have briefly discussed below-
Let’s face it. AI is not the cheapest of technologies. Especially applications like visual AI in eCommerce, which require specialized hardware and software, can bear huge costs to businesses. Apart from these tangibles, the cost of training and maintaining the system to scale as the requirement grows also add to the project cost. Lastly, the cost of data storage and processing can be expensive, as large amounts of unstructured data would be required for a visual AI system to be effective. All these costs can become overwhelming for the project cost, leading small businesses to opt for the technology completely.
As with any technology involving the use of customer data, there may be risks associated with data security when implementing visual AI in an eCommerce store. Visual AI relies on collecting, storing, and analyzing large amounts of customer data, which can be vulnerable to cyber-attacks if not properly secured. Companies must ensure that sensitive customer information is encrypted and stored securely to protect customer privacy. They must also implement robust authentication protocols to prevent unauthorized access to customer data.
If a visual AI system in eCommerce is trained on a biased dataset, then it may inaccurately identify and categorize products and make incorrect decisions regarding prices. This can result in incorrect or unfair product recommendations or pricing strategies, which could negatively impact customer experience as well as sales.
Misinterpretation of Visual Content
Misinterpreting visual content can be a major challenge while using visual AI in eCommerce. AI-driven systems, though highly effective, are not always accurate. For example, AI-based systems may struggle to accurately interpret the color of an object or the size of an item. This can lead to errors in pricing and product descriptions, resulting in customer dissatisfaction and lost sales. Additionally, AI systems may not recognize nuances in visual content, such as the texture of the fabric or the style of clothing. This can lead to poor product recommendations and poor customer experiences.
Unlock the Potential of Visual AI for Your eCommerce Store with Matellio
While the above-mentioned implementation challenges of visual AI in eCommerce seem intimidating, let them not deter you from using the technology to unlock your eCommerce store’s complete potential. With the right eCommerce app development company like Matellio, with expertise in AI, you can easily overcome these challenges and make the best use of the technology as per your requirements. To begin with, understand the various use cases of visual AI for your particular eCommerce business.
Thereafter, you can take a free consultation call to choose the right visual AI platform for your use case(s). Then we can easily set up your AI system to regularly train the model and make more accurate and effective decisions so long it functions on your website. Where should you begin, you ask? Well, simply fill up this form with your requirements, ideas, and references, and have our experts schedule a call with you. Thereafter, they will be responsible for sharing with you a free quote and development plan for your project. And just like that, you’re already on the path to beginning your eCommerce upgrade journey.
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