What role will Computer Vision play in the future of eCommerce?

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What role will Computer Vision play in the future of eCommerce?

September 2, 2022 6 min read

Computer Vision applications are transforming eCommerce, with enterprises predicted to deploy computer vision technologies in order to drive seamless omnichannel shopping experiences, bringing clear benefits to both customers and retailers.

What is Computer Vision?

Computer vision refers to a subset of AI that collects information from images or multi-dimensional data through artificial systems. In simpler terms, computer vision deals with enabling computers to recognize the contents of an image the same way humans do, using complex algorithms like Deep CNN (Convolutional Neural Network).

Computer vision is at the forefront of various industries, reshaping medical imaging, predictive maintenance, aerial survey and imaging, autonomous vehicle technology, and eCommerce. eCommerce, in particular, stands to benefit greatly from computer vision applications, with enterprises predicted to need computer vision to remain competitive in the eCommerce landscape.

Events like the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) helped propel computer vision to the forefront of advances in AI, showcasing how deep learning algorithms trained on high-quality data sets could surpass human ability in classifying objects.

Computer Vision in eCommerce

Computer vision applications are crucial to enabling AI adoption in eCommerce, finding use in optimizing inventories, monitoring product catalogs, curating recommendations, enabling seamless visual search, and understanding customer intent, bringing clear benefits to both the customer and the retailer.

Computer Vision-driven omnichannel shopping experiences

Computer vision applications are set to provide superior customer experiences in eCommerce with the potential to drive seamless omnichannel shopping experiences. 88% of consumers surveyed report that product images are a key element of a great shopping experience; however, the sheer volume of eCommerce catalogs that contain millions of products makes visual algorithms central to filtering product images in line with the company’s guidelines, underlining the potential for computer vision applications in eCommerce.

Computer Vision use cases in eCommerce

Visual search allows users to perform online searches using real-world images. It empowers retailers to overcome the limitations of text-based search (where customers are unable to describe a product using words) by harnessing the ubiquity of smartphones. That 62% of millennials prefer visual search to other technologies points to the potential of visual search in eCommerce.

Overcome the limitations of text-based search

Customers can perform visual searches to discover products that are more suited to being found through image-based attributes making product discovery more convenient. By integrating visual and text-based attributes, retailers can also take a multimodal approach to identify products.

Early adopters who incorporate visual search on their platforms could see revenues increase by 30%, with the global visual search market projected to reach $15 Billion by 2023. Visual search has already been adopted by major retailers like eBay, ASOS, and Amazon as part of their online shopping experience. Now, with increasing AI adoption, even smaller retailers will be able to make the most of this computer vision-driven change.

Personalized Product Recommendations

The key to curating personalized product recommendations in eCommerce is to offer customers products that precisely match the search terms that they enter. According to one study, half of all online shoppers admit to making a purchase they didn’t intend to make until they were shown personalized product recommendations.

Enrich catalogs and improve personalized recommendations

According to another study, personalized product recommendations dramatically increase the AOV (Average Order Value). The same study also revealed that product recommendations constitute about 31% of online product sales.

Using computer vision-driven visual attribute extraction, retailers can enrich product catalogs and improve personalized product recommendations by showing customers products that have exactly the same visual attributes as the products they are searching for. Customers can also be shown products that complement the ones they are seeking to buy.

eCommerce enterprises can also make use of deep learning recommendation techniques such as collaborative filtering and content-based recommender systems to drive intelligent recommendations. Fashion websites that deploy collaborative filtering algorithms can enable product discovery by figuring out latent features; options not available in faceted navigation.

Smart Warehousing and Inventory management

With preprogrammed robots no longer able to keep up with the demands of present-day warehouses, autonomous item handling robots powered by computer vision can automate stocking and retrieval by detecting the exact location of a particular product and retrieve it with exactly the right amount of force and transport it to the designated location.

Warehouse automation can eliminate tedious and tiresome human labor and make inventory movements faster and cost-efficient. Computer vision applications can also be deployed to perform quality checks and inspect products during shipping and upon returns.

1 in every 3 shoppers experiences an out-of-stock situation, costing businesses almost $1 billion in annual sales. Using computer vision applications, retailers can keep an eye on current inventory levels and inform the replenishment department if stocks go below a certain level, making real-time updates to the inventory to develop an omnichannel retail experience.

Attribute Extraction and Catalog Curation

Deep learning algorithms driven by computer vision help retailers move beyond manual labeling by regularly scanning product catalogs to infer and extract attributes from images that rectify errors in product descriptions and provide more concrete visual attributes to existing products. Products can also be automatically detected, categorized, and labeled, enabling retailers to list their products more quickly and accurately than their competitors.

Move beyond manual labeling

Retailers with large catalogs can extract precise attributes from products and use them to generate accurate labels. Low-resolution images and irrelevant listings can be eliminated, while explicit content, counterfeit items, and fake brands can be detected and flagged based on the product image and subsequently removed.

Augmented Reality – Try Before You Buy

AR (Augmented Reality) combines aspects of the real world with a computer-generated likeness of the product the customer intends to purchase. AR uses computer vision to precisely position the digitized product, rotate it and make it appear authentic.

AR grants customers the convenience of previewing a product before purchasing it, providing a more immersive and interactive online shopping experience. Customers can try new styles of clothing without cumbersome trials using Virtual Try-On (VTO) with multiple sensory modalities beyond visual, such as auditory and haptics, carrying the potential to enhance the AR experience.

Autonomous Checkouts

Computer vision applications are also making their presence felt by the transformations they are enabling in brick-and-mortar stores which include ‘Just Walk Out’ autonomous checkout technology driving cashierless experiences. Customers can now walk into a store, pick up the products they like, and walk out, completely eliminating the need for the checkout process.

Just Walk Out technologies like Amazon Go, Alibaba’s Hema Supermarket, and Standard Cognition’s Standard store make use of multiple cameras powered by computer vision applications to track customer selections and detect when products are taken from or returned to shelves while keeping track of them in a virtual cart for which the customer will automatically be charged as they leave the store; advances made possible only through computer vision, deep learning algorithms, and sensor fusion.

The Taskmonk Advantage

At Taskmonk, we power data labeling operations for enterprises enabling them to create differentiated data-centric AI at scale.

Create data-centric AI at scale

Using Taskmonk, AI teams can manage multiple labeling partners on a single platform and optimize labeling costs using our proprietary allocation algorithms and AI models. Taskmonk’s purpose-built no code framework also allows AI teams to create customized labeling solutions and QC workflows across data types and labeling processes.

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