Revolutionizing E-commerce: Unleashing AI for Tailored Shopping Bliss: By – Srishti Baweja, COO & Whole Time Director at E2E Networks Limited – a Cloud Computing Platform

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Revolutionizing E-commerce: Unleashing AI for Tailored Shopping Bliss: By - Srishti Baweja, COO & Whole Time Director at E2E Networks Limited – a Cloud Computing Platform
Revolutionizing E-commerce: Unleashing AI for Tailored Shopping Bliss: By - Srishti Baweja, COO & Whole Time Director at E2E Networks Limited – a Cloud Computing Platform

AI can also assist shoppers in comparing prices across different e-commerce platforms, ensuring they get the best deals and discounts.

The years 2022–23 have seen an unprecedented surge in the growth of e-commerce after the lull of the COVID years. Product recommendations, cross-selling, voice searches, and chatbots have powered much of this growth. As AI adoption increases, we will see a further massive transformation in the shopping experience that will both bring in new users to e-commerce platforms as well as encourage repeat buying.

Image Search for Shopping Convenience

If you like the tee that Virat Kohli is wearing to a Sunday brunch or you want to buy the face serum Priyanka Chopra uses, you can very soon simply copy-paste the image for an ‘image search’. AI-powered image recognition algorithms will analyze the visual attributes within the image, such as colors, shapes, patterns, and textures; they will also be able to match the image with similar products within the e-commerce catalog.

Visual search can enhance the accuracy of product searches on mobile phones, especially when regional-language users struggle to describe items in text-based queries. AI can identify and outline objects within images; for example, users can click on a product within an image (e.g., a person’s hat in a photo) to search for similar items available for purchase. AI can also do cross-category matching; that is, if a user uploads an image of a shirt, the system can suggest matching accessories or shoes.

AI-powered visual search can integrate with interactive AR features, allowing users to virtually try on clothing or visualize how furniture will look in their homes before making a purchase. Combining visual search with voice commands will be an unprecedented enhancement to the current shopping experience. When the average e-commerce bounce rate is between 20 and 45%, smarter searches will help reduce this number by offering more relevant results.

Price Optimization

AI-enabled dynamic pricing is the strategy of changing product prices based on supply and demand. Dynamic pricing can adjust prices in real-time based on factors such as demand, competitor pricing, and inventory levels. That way, platforms, for example, can predict when and what to discount, dynamically calculating the minimum discount necessary for the sale.

Dynamic pricing can maximize revenue during periods of high demand and maintain competitiveness during slow periods. AI can also assist shoppers in comparing prices across different e-commerce platforms, ensuring they get the best deals and discounts.

Virtual Assistants

Virtual assistants currently handle 70% of online customer conversations, with customer care officials handling the more complicated queries. In the coming years, we will see AI assistants—speaking in multiple languages—responding to more complex queries at any time of the day or night, sharing product recommendations, and providing real-time updates for accurate package tracking. Shoppers would be able to ask virtual assistants questions about products, such as specifications, features, pricing, and availability, without needing to navigate through the website. AI virtual assistants can also help users troubleshoot technical issues with the website or app, such as login problems or payment errors. They can simplify the checkout process by auto-filling forms, applying relevant coupons, and providing payment options, reducing cart abandonment rates.

AI’s deep learning algorithms can determine individual preferences to provide appropriate recommendations. For example, by analyzing customer reviews, it could be understood that garment sizes run large and recommend a shopper purchase a size down as they try to add a new sweatshirt to their cart. Similarly, virtual assistants trained with natural language processing (NLP) can tailor recommendations to a specific shopper at a specific point in the buying journey. Imagine a parent booking a party for their daughter’s birthday. The assistant could recommend they also book a cake, along with sharing details of a local bakery.

Product Recommendations

Now we come to the crucial point of product recommendations. How is it going to be any different from the recommendations we already receive on e-commerce sites? One, AI can do ‘collaborative filtering’, where algorithms compare a user’s behavior and preferences with those of similar users. Two, AI can enable’real-time personalizations’, adapting recommendations in real-time as users interact with the platform. Three, it can provide consistent product recommendations across various touchpoints, including websites, mobile apps, email marketing, and even chatbots.

AI is all set to create hyper-personalized shopping experiences by considering not only a shopper’s past behavior but also real-time context, such as location and weather, when making recommendations. This hyper-targeting can tackle the challenge of abandoned carts, which, globally, stands at 70%. Through smarter product recommendations, retailers can show the right product in the right place at the right time.

Demand forecasting and inventory management

AI uses machine learning models to identify patterns in customer behavior that allow businesses to anticipate changes in demand and adjust their inventory accordingly. AI can also factor in lead times for restocking products from suppliers.

What’s more, AI can analyze return and refund data to predict which products are more likely to be returned, helping businesses optimize return processing. Better management of inventory would definitely reduce waste and assist companies in their sustainability goals by preventing the disposal of unsold products while also encouraging more responsible buying among customers.

Building AI-Powered Shopping Experiences

The cornerstone of developing AI-powered, tailored shopping experiences is access to high-end GPU nodes, which are instrumental in powering the necessary AI technologies for seamless delivery. This has been a challenge for many, as there is a demand-supply gap and a long waitlist for many of the cutting-edge GPUs. However, with new entrants racing to build AI-grade GPUs, this gap is set to narrow in the near future, giving businesses access to unprecedented computing power. This, in turn, will catalyze a significant surge in AI deployment across e-commerce platforms and marketplaces, all vying to captivate customers with enriched interactive experiences.

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