13 Groundbreaking Applications of Generative AI in eCommerce

Generative AI in eCommerce: Value-Driving Use Cases to Consider 

The business impact of big data is hard to overestimate. The more information eCommerce businesses have, the more meaning and worth they draw from it. Online stores are full of data: shopper reviews, product descriptions, item images, prices, and more information can be analyzed and utilized to improve online sales platforms with generative AI. Moreover, GenAI can offer much more, and we delve deeper into the use cases below. 

Optimization of Content Creation 

One of the most popular uses of generative AI solutions is, of course, content generation. For instance, the craft store Michaels utilizes generative AI to create better website copy, marketing emails, and SMS campaigns, thus achieving better customer engagement. In fact, the company says that using AI algorithms for content creation resulted in a 25-41% improvement in click-through rates. 

In one of our projects, we also used generative AI in eCommerce to help our client effectively extend its product range. Our team was responsible for fine-tuning product descriptions and cleaning up names for over one million items available. We employed ChatGPT to find and eliminate irrelevant textual information from product descriptions and titles. We also created a custom script to support ChatGPT functionality and elaborated the list of prohibited words and pertinent keywords to improve the accuracy of the content generation and cleanup. 

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Leveraging AI/ML for Expanded Product Range and Enhanced eCommerce Product Listings 

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Efficient Inventory and Supply Chain Management

The global retailer Amazon masters artificial intelligence and machine learning for demand forecasting. The company’s tech team ensures accurate prediction results thanks to the combination of deep learning, image recognition, and natural language processing that serve as a foundation of generative AI. 

With demand forecasting done by examining vast amounts of historical sales data, Amazon spots intricate trends, derives meaning from product photos, and examines user reviews. Such an approach helps to avoid overstocking or stockouts, product waste, and improve inventory management overall. In addition, Amazon is investigating the use of deep reinforcement learning (DRL) for supply chain management optimization based on the aforementioned demand forecasting. 

Customization of Products to Meet Specific Needs

Unilever, a global FMCG (Fast-Moving Consumer Goods) company, has decided to improve its hair care products with generative AI tools. For one of its brands, the company created an interactive diagnostic tool that implies collecting information from customers. It works the following way: first, shoppers respond to inquiries regarding their hair and scalp conditions. Then, using generative AI capabilities and a wealth of knowledge from dermatologists, the application creates a personalized profile of the scalp and hair and recommends products that may help customers to have better hair care.

Such an application of generative AI in eCommerce app development allows for staying connected to clientele, understanding their needs, and achieving customer satisfaction. 

Utilization of Real-Time Analytics for Informed Decisions

Target, an American retail corporation, uses generative AI tools along with machine learning to monitor the availability of products. The company tracks the behavior of customers when they are scrolling through multiple products on a website. It also checks customer feedback on social media and monitors market trends concerning one or another product. Based on such data, Target predicts demand for items and improves inventory management.

Target is undoubtedly an inspiring example but if you are planning to build an online marketplace, you need to consider that the marketplaces’ business operations are much more complex compared to small online stores. Nevertheless, real-time analytics can significantly simplify the processes for you. Target proves it because, on top of improved inventory management, real-time analytics also enabled them to dynamically manage product placements and ensure optimal stock levels. 

Large-Scale Market Research and Data-Driven Insights 

A major consumer insight researcher GfK utilizes generative AI models to interpret extensive customer data, information about market trends, and, hence, equip the eCommerce industry with actionable insights. The company collects data from POS terminals, customer interactions with brands online, and other multiple sources to craft market reports and help eCommerce businesses see what their shoppers want, what their competitors are after, and what decisions are crucial to make right away to stay afloat.

Serhii Leleko

AI&ML Engineer at SPD Technology

“Traditional market research is expensive and relies on limited focus groups. Generative AI offers much more: by analyzing vast amounts of online data from social media or reviews, GenAI can uncover hidden customer preferences and emerging trends in real-time. This allows businesses to gain customer insights at scale and identify what resonates with their audience to improve overall customer experience.”

Efficient Translation and Context-Driven Localization

Building an eCommerce website available in several locations requires localization efforts. That means allocating a drastic part of your budget to specialists who will translate digital store content into different languages and tailor it to the cultural specifics of the target audience. However, generative AI can streamline this process and reduce costs. 

AI-automated localization is a common practice now, and tech vendors can even sell ready-made solutions. For instance, Amazon designed such a tool specifically for retail. This solution offers functionalities for scalable, real-time, and accurate translation. The localized content looks like natural language and fits the context of products in the store. As a result, eCommerce businesses can expand their reach, win new markets, and serve customer satisfaction.

Deployment of Intelligent Chatbots for Customer Support

Statista reports that the most common use of generative AI in eCommerce in 2023 was for customer support, with 42 percent of respondents utilizing it to resolve post-purchase queries. One of the eCommerce businesses that implemented AI solutions for improving customer service is Sephora. With an AI-powered chatbot, the leading beauty retail chain provides information about products, helps customers to resolve their problems, or even offers beauty tips. 

Artificial intelligence transforms customer service with chatbots that can free up customer service employees to focus on more complex issues. In one of the projects, we helped a French fashion store leverage pre-trained machine learning models for NLP and data embedding. In such a way, we created a bot that delivers responses within less than 10 seconds for 99% of user queries. Our efforts resulted in significantly improved customer experience.

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High-Load Support AI Chatbot for an Online Fashion Store

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Detection and Prevention of Fraudulent Activities 

eBay uses generative AI tools to detect fraudulent activity on its marketplace. AI algorithms employ machine learning techniques to detect potentially malicious bidding patterns, suspicious listings, and other activities that can fall out of normal activity patterns that usually present in the store. 

With the help of generative AI, eBay is better equipped to defend users against different types of fraud by constantly adapting to new fraud strategies. For example, eBay uses techniques like anomaly detection and risk scoring in its fraud detection systems to examine enormous volumes of transactional data and identify potentially suspicious activity. These AI models have the capacity to learn from fresh data, which enables them to keep ahead of new risks and adjust to changing fraud patterns.

Implementation of Hyper Personalization Strategies

The major fashion eCommerce store H&M goes beyond traditional personalization and introduces the possibility to create custom clothes designs. This functionality is available in H&M’s Creative Studio and is supported by generative AI technology. 

In a nutshell, the feature works as follows: shoppers type a text description of the design they want. Then, generative AI algorithms recognize the text using natural language processing and create an image based on the description. This approach helps engage customers like never before and creates a strong bond between a brand and its audience. 

Multi-Channel Sentiment and Voice of Customer (VoC) Analysis

Walmart leverages generative AI in many ways with creating review summaries as one of the most prominent applications of this technology. This eCommerce business relies on this technology to examine what consumers think about the brand, what their sentiment towards certain products and services is, what features of the eCommerce store they enjoy, and more. 

Such an approach can be aimed at discovering customer sentiment from multiple channels, including customer feedback on a website, responses to targeted marketing campaigns, or social media posts. All that helps Walmart to improve its decision-making processes and outline priority product features for enhancing customer satisfaction from interaction with the brand.

Implementing Dynamic Pricing Mechanisms

Zalando, a European fashion marketplace, makes their prices respond to the demand thanks to generative AI that scrutinizes tons of pricing on other eCommerce websites. The company achieved dynamic pricing by training AI models to draw actionable insights from market trends, competitor prices, and consumer sentiment. 

Zalando’s dynamic pricing system, powered by artificial intelligence, facilitates real-time analysis and predictive analytics in retail. These capabilities are essential for modifying prices in response to prevailing market conditions. As a result, individualized pricing takes into account the preferences and buying habits of the consumer and optimizes revenues. 

Visual Search, Product Discovery and AR Shopping Experiences

Decathlon, a sporting goods retailer, opted for integrating generative AI into its website for visual product discovery. The solution allows consumers to navigate through products in the online store with a search that can process and recognize pictures. To take advantage of this functionality, Decathlon’s customers need to take a photo of products they are interested in, upload the photo to the website, and browse similar products that appear in the results.

Decathlon reported that such an approach to the implementation of generative AI helped to achieve a 250% increase in click-through rates and a 330% improvement in customer engagement. 

Best-Matched Influencers Identification and Campaign Optimization 

eCommerce businesses can leverage generative AI to identify the influencers that will be the most suitable for marketing campaigns. AI helps with analyzing vast amounts of customer data and social media trends to find influencers whose audience matches the brand’s target market. Plus, generative AI models can predict the potential reach and outcomes of a campaign with a specific influencer, allowing for data-driven selection.

AI tools can enhance the campaign’s optimization after an influencer has been selected. Influencers can benefit from AI’s ability to adapt ad targeting based on campaign data, create content specifically for their target audience, and even track effectiveness in real time.