AI-driven Conversions: Computer Vision Applications for eCommerce

Large retailers across the country are shifting their focus purely on eCommerce, thanks to lockdown 2.0. For the eCommerce sector to reap the benefits of this booming phase, it must employ advanced technologies such as artificial intelligence (AI). Today, AI-powered computer vision services are beginning to optimize eCommerce sales and enhance customer’s shopping journeys by deploying machine learning algorithms. From object detection to personalized recommendations, computer vision applications for eCommerce is making giant leaps to scale the digital presence for brands across verticals.

Let’s explore some effective computer vision applications that are poised to contribute to achieving the vision of $4.2 trillion USD eCommerce sales in 2020.

Accelerating eCommerce Conversions with Computer Vision Applications

1) Automated Product Categorization

Visually appealing and organized product images together constitute the most significant layer of any eCommerce portal. With millions of product images pouring into an eCommerce database every day, it becomes laborious to structure the data efficiently.

Here’s when artificial intelligence augments eCommerce capabilities with computer vision technologies. Powered by machine learning development services, computer vision streamlines visual classification of commercial products with the following techniques-

a) Object Detection

In contrast to manual validation and uploading, machine learning offers cost and time-effective solutions for accelerating object detection of real-world images. By training models with variable correct and incorrect image inputs, eCommerce businesses can automate-

(i) Validation of inappropriate images

(ii) Matching the right images with respective products

(iii) Extraction of features from product images

A Clothing Attribute detection model prepared by the students of Stanford University.

b) Image Classification

Image classification is a step ahead in processing eCommerce images and enhancing the overall efficiency of businesses. The high classification accuracy of machine learning models enables businesses to extract the following attributes from product images-

(i) Logo size and type

(ii) Variable color palettes

(iii) Prints and clothing patterns

(iv) Shapes of different parts like necks, sleeves, and more.

By harnessing ML models for image classification, eCommerce businesses can improve marketing campaigns, categorize products on sites, enhance customer experience significantly.

Also read- Building and Deploying an AI-powered Image Caption Generator

2) Seamless Visual Search

In the age of smart IoT devices, consumer expectations are pegged high for a seamless and enhanced online shopping experience. Computer vision applications for eCommerce are beginning to match dynamic customer needs by searching for their desired products with a picture of the item.

eCommerce giants, including eBay, are eliminating keywords to offer visual search for optimizing the customer experience.

The process is called “visual search”, wherein object detection, image classification, and other inputs work together to find the visually similar result of the uploaded image.

The underlying deep learning technology enables customers to look for products at eCommerce portals by uploading images. The AI system can accurately match inputs received from object detection and image classification processes to find the right product results.

Also read- Deploying Image Classification with TensorFlow Lite on Android

3) Personalized Recommendations

While product recommendations are an age-long online sales phenomenon, the customer engagement technique has got a competitive advantage with AI. eCommerce giants are now exploring and proactively investing in deep learning to improve their recommendation engines and increase RPV. Though there are several kinds of recommendation techniques, deep learning is currently making strides in-

a) Collaborative filtering

The neural network under collaborative filtering works on a simple logic of matching the preferences of a similar user base. It functions on the grounds that users buying similar items in the past will most likely buy certain items in the future.

eCommerce giant, Amazon, is constantly improving its item-to-item collaborative filtering using machine learning algorithms.

b) Content-based recommender system

This technique pivots on the “keywords” or “textual content” of the products bought by individual users to provide more personalized product recommendations. The deep learning algorithms track and analyze users’ purchase history, reviews, and interests to suggest relevant items.

As customers seek more personalization in their online shopping journeys, it is essential for eCommerce businesses to map preferences using AI.

Also read- Exploring the Potential of Conversational Analytics for eCommerce Businesses

Oodles AI: Building Computer Vision Applications for eCommerce Optimization

We, at Oodles AI, enable eCommerce businesses to enhance visitor’s experience and improve customer loyalty by building efficient AI-powered computer vision solutions. We deploy accurate and domain-specific custom recommender systems in the cloud and on-premise for eCommerce portals to maximize sales significantly. Our AI team has hands-on experience in building machine learning and deep learning models to automate critical eCommerce operations, such as-

a) Object detection to validate product images

b) Product categorization to organized large eCommerce database

c) Recommendation systems to personalize the customer experience

d) Conversational commerce with recommendation-based commercial chatbots, and more.

Reach out to our AI development team to know more about artificial intelligence services.