- AI impact. The global AI market, valued at $142.3 billion, is revolutionizing ecommerce through personalization and automation.
- Hyper-personalization. AI and ML enable ecommerce platforms to provide tailor-made shopping experiences by analyzing individual preferences and shopping habits.
- Optimized pricing. Predictive analytics powered by AI and ML help retailers maximize revenue through optimized pricing and promotional strategies.
According to a March 2023 Statista report, the global artificial intelligence (AI) market is valued at $142.3 billion dollars. In recent years, the ecommerce industry has undergone a significant transformation, thanks to the rapid advancements in AI and machine learning (ML). These technologies enhance an online retailer’s ability to provide personalized, efficient and seamless shopping experiences.
This article examines AI and ML's impact on ecommerce, highlighting case studies and discussing their role in driving sales and enhancing the customer experience.
AI Content Production
At this point, the majority of people who work in IT, marketing, advertising, HR, programming and any other domain that uses technology are aware of generative AI. Generative AI applications such as ChatGPT, Microsoft Bing and Google Bard are large language models (LLMs) that are used along with natural language processing (NLP) and ML to generate text, audio and video content. A user enters a descriptive text prompt, and based on the prompt, the generative AI application creates the content. The technology is now being incorporated into a multitude of applications across industries.
Hammad Khan, co-founder and CEO at AlphaVenture, a digital transformation company, told CMSWire that the biggest challenge he faced when working with AI was choosing the right technologies, and finding the right use cases.
“Technology is growing at such a fast pace that it's difficult to fully invest in one technology without worrying about it being obsolete in the near future,” said Khan, who predicted that many ecommerce tasks are going to be replaced by AI very soon. “For example, support will be replaced by bots, content generations will be replaced by generative AIs. The ability to create content on the fly is amazing,” suggested Khan. For his business, the use cases are currently limited to personalization and assisted content generation, but as AI improves, Khan believes that it's going to take over most aspects of content generation.
Where AI Meets Product Information in Ecommerce
Generative AI is transforming ecommerce by creating persuasive product descriptions, optimizing email marketing campaigns through personalization, generating engaging social media posts, and producing entertaining and informative web content to boost traffic and conversions.
Thomas Kasemir, chief product officer at Productsup, a P2C platform provider, told CMSWire that managing product information across multiple channels and in various markets is becoming increasingly complex for ecommerce customers. "AI has been a crucial component in building out a solution for this, like utilizing AI to automatically map product data when setting up ads and listings. For the AI to deliver accurate, high-quality product content, you need technology that is equipped to process huge volumes of data," said Kasemir.
Kasemir suggested that by eliminating tedious work and increasing efficiency in managing product information on the backend, businesses can significantly improve the shopping experiences they deliver to customers. “The AI helps to remove inaccuracies in product information and customize content per demographic and channel to provide consumers with consistent, personalized brand experiences.”
Related Article: AI’s Role in Digital and Retail Personalization, Part 1: The Big Picture
Personalization: Curating Tailor-Made Shopping Experiences
Hyper-personalization is the process of delivering highly customized and curated content, products and services to individuals, using real-time data, AI, automation and predictive analytics to anticipate customer needs and desires. In 2023, AI is a crucial element in the creation of fine-tuned, targeted experiences that go beyond traditional personalization strategies.
AI and ML are enabling ecommerce platforms to provide hyper-personalized shopping experiences, as they can analyze vast amounts of real-time data to identify individual preferences, browsing habits and purchase history.
Paul Farrell, VP of product management at Oracle NetSuite, a business management software platform provider, told CMSWire that ML can be used to create fluid experiences through the analysis of customer behaviors at every touchpoint and inform future interactions. “By leveraging data sources such as purchase history, transaction correlations, complementary item combinations, and customer behavior, ecommerce retailers can automate personalized recommendations,” Farrell explained, adding that the integration of real-time recommendations with ecommerce platforms increases the potential for upselling and cross-selling.
Richard Batt, a UK-based AI consultant, told CMSWire that AI and ML are transforming ecommerce through the use of personalized recommendations and content. "I believe this is one of the most effective and innovative ways to use AI/ML for ecommerce, as it can increase customer engagement, loyalty and revenue."
Batt explained that personalized recommendations and content are powered by AI algorithms that analyze shopping patterns, preferences, feedback and the behavior of customers. “They can also use natural language processing (NLP) and computer vision to extract features and sentiments from textual and visual data. By using this data, AI can deliver relevant and tailored offers and suggestions that match customers’ needs and interests.”
Amazon's recommendation engine is a superb example of personalization in action. Amazon uses AI algorithms to analyze customer behavior and provide personalized product suggestions based on their interests, past purchases and items viewed. This level of personalization has been a critical driver of Amazon's success, with an estimated 35% of its total sales generated through its recommendation system.
Similarly, the online fashion brand Stitch Fix uses AI-driven algorithms and ML to curate personalized clothing selections for its customers (though the brand prefers not to disclose its use of AI). By analyzing customer preferences, purchase history, and even social media activity, Stitch Fix's AI stylists provide tailored fashion recommendations, increasing customer satisfaction and brand loyalty.
“Research shows that customers are more likely to shop with brands that provide relevant offers and suggestions,” said Batt. “This shows how important personalization is for ecommerce success. Batt said that personalization can also reduce shopping cart abandonment, cross-sell and up-sell products or services, enhance customer satisfaction and delight, encourage social sharing and referrals, and generate positive reviews and ratings.
Related Article: AI in Ecommerce: True One-on-One Personalization Is Coming
Maximizing Sales Through AI-Optimized Pricing and Promotions
AI and ML are also playing a pivotal role in helping ecommerce brands maximize their revenue potential. Through predictive analytics, these technologies can help retailers optimize their pricing and promotional strategies, ensuring they offer the right products, at the right price and the right time.
Predictive analytics is a set of technologies that use analytics-powered software to make future predictions and uncover hidden patterns in data. AI and ML are being used for predictive analytics in ecommerce to make better estimates about future market demands and improve various aspects of the business. With the help of ML, AI technology can refine its predictions over time, as it learns from data and adapts to new patterns. Dynamic pricing employs AI and ML to assess factors like competitor pricing and market trends, increasing revenue and profit margins.
Better Inventory Management: Streamlining Supply Chain Processes
Efficient inventory management is crucial for ecommerce businesses, as it directly impacts customer satisfaction and profitability. AI and ML are now being used to streamline supply chain processes, enabling retailers to maintain optimal stock levels, minimize items being out of stock and overstock situations, and reduce overall inventory costs. Predictive analytics assist ecommerce businesses in inventory management, forecasting demand and optimizing stock levels.
Eric Mills, owner and founder of the Lightning Card Collection ecommerce brand, told CMSWire that even experienced retailers struggle to deal with unpredictable supply and demand. "In my experience, AI/ML systems and tools have been an incredible help in managing ecommerce supply and demand, especially when it comes to forecasting based on trends and historical data."
Walmart implemented AI-driven demand forecasting algorithms in 2020 to optimize its inventory management. By analyzing historical sales data, customer preferences and market trends, these algorithms can predict future demand patterns, enabling Walmart to adjust its stock levels accordingly. This has resulted in significant cost savings and improved customer satisfaction due to fewer stockouts and better product availability.
In 2019, Nike acquired the data analytics startup Celect in order to enhance its direct-to-consumer (DTC) strategy. Celect's AI-driven inventory management system has enabled Nike to optimize its inventories with hyper-local demand predictions, and its allocation of products across its various distribution channels, ensuring the most appropriate products are available in the right locations to meet customer demand.
Chatbots and Virtual Assistants: Enhancing Customer Support
AI-powered chatbots and virtual assistants, largely using large language models, are becoming increasingly prevalent in the ecommerce sector, providing customers with instant support and assistance throughout their shopping journey. By leveraging NLP and ML technologies, these chatbots can understand and respond to customer queries in a natural and conversational manner, helping to resolve issues and provide product recommendations.
H&M's virtual assistant, developed in partnership with Google, provides customers with personalized fashion advice and product suggestions based on customer preferences and browsing history. This not only improves the shopping experience but also creates a deeper connection with the brand by offering genuinely helpful and tailored support.
The introduction of generative AI technologies has boosted interest in using chatbots for customer service or onsite conversations. “ChatGPT excels at maintaining coherent conversations with customers while consistently staying on topic, outperforming traditional AI chatbot models,” said Batt. “The incorporation of a system prompt grants website owners the ability to set the context for chatbot interactions, ensuring that customer inquiries are addressed both efficiently and appropriately.”
OpenAI’s ChatGPT API has created a multitude of opportunities to add AI functionality to a brand’s customer service and knowledge base applications. “By integrating an embeddings model with the ChatGPT API, chatbots gain the capability to extract vital information from various sources. This results in accurate and prompt responses to customers, paving the way for new standards in customer experience and ecommerce engagement,” said Batt.
AI and ML in Fraud Detection and Prevention
As the volume of online transactions continues to grow, so does the risk of fraud. AI and ML algorithms are now being used to detect and prevent fraudulent activities in real time, enhancing security measures for both businesses and customers. These technologies analyze vast amounts of transaction data, identifying patterns and anomalies that are indicative of fraudulent behavior.
A prime example of this is PayPal, which uses AI-driven algorithms to monitor millions of transactions daily, detecting and preventing fraud in real time. Through the analysis of elements such as transaction size, frequency and location, PayPal's AI system can identify suspicious activity and take appropriate action, reducing the risk of financial loss for both the company and its users. PayPal trains its AI models using thousands of good and bad transactions, which helps to teach them how to independently identify future bad buying behavior.
Final Thoughts on AI in Ecommerce
AI and ML are significantly impacting ecommerce by transforming personalized recommendations, predictive analytics, pricing and promotions, inventory management and fraud prevention.
These technologies are reshaping the ecommerce landscape, boosting sales and improving the customer experience. Integrating AI and ML into ecommerce strategies offers businesses a competitive edge and fosters loyal customer relationships.