5 Examples of AI in Online Shopping

Consumers like retailers who understand their needs. They’re also partial to convenience, saving money, and relevant, one-to-one, personal experiences.  According to McKinsey, 71% of shoppers expect personalization from companies. 

If you don’t live up to these expectations, you’re bound to disappoint people, particularly if you’re a retail brand. The growing demand for exceptional online shopping experiences has spurred companies to embrace artificial intelligence (AI) and machine learning (ML) in eCommerce because it turns online shopping into a meaningful, even joyful, experience akin to visiting the local mall (remember those?)

What are the Different Types of AI?

AI isn’t a monolith. In retail, it takes many forms, each serving unique purposes across the shopping journey including serving up customized product recommendations, anticipating trends, and supporting better product discovery. AI-driven applications help retailers meet their customers’ rising expectations for great digital shopping experiences, though some of this technology is still somewhat aspirational. 

We’ll explore five important examples of how AI impacts and elevates the digital retail landscape and aids in meeting consumers’ evolving expectations, but first – here’s a breakdown of the main types of AI used in online shopping.

1. Conversational or Generative AI

Conversational AI, also called Generative AI (GenAI) uses natural language processing (NLP) and machine learning (ML) to “speak” with human users. On a retail website or app, this may manifest as a customer service chatbot or virtual shopping assistant. You ask it a question and it responds with a natural (human) sounding response.

Chatbots powered by AI can handle complex customer interactions. They may respond to questions like, “How long does shipping take?” or offer product suggestions for a shade of lipstick or skincare product based on a customer’s past purchases. Intelligent chatbots use data and ML to improve over time. They get better at responding as they ingest more data, learning and improving from each interaction.

2. Predictive AI

Predictive AI is an exciting AI technology that relies on advanced algorithms and historical data to forecast trends and behaviors. It’s being used in a variety of ways in online shopping scenarios. What makes it work well is its ability to process vast amounts of data and leverage ML to understand and forecast product demand. Retailers use predictive analytics to automate inventory management, for example, by optimizing inventory across offline and online channels and minimizing or eliminate stockouts. Predictive analytics can also be used to create personalized product recommendations, tailor offers to various customer segments.

3. Autonomous AI

Autonomous machines? That’s pie-in-the-sky futurism stuff which represents a cutting-edge approach to online shopping technology. Autonomous AI systems can independently learn, adapt, and make complex decisions without human intervention. While still largely theoretical, elements of autonomous AI are already being implemented in retail (see “generative” and “predictive” AI, above). One day, autonomous AI could lead to fully automated, highly personalized experiences that requires zero human intervention. We’re not quite there yet, but the potential of autonomous AI to transform e-commerce is enormous.

4. Empathetic or “Emotional” AI

Like autonomous AI, Empathetic AI is an emerging technology. It aims to perceive, understand, and thus respond appropriately to human emotions. Emotional AI does this by analyzing cues like facial expression, vocal intonation, and language. It goes beyond simple text or voice recognition, attempting to understand the nuances of human communication, including tone, sentiment, and context.

In a retail setting, empathetic AI could tailor a AI-driven conversational interactions based on a shopper’s mood or emotional state. Imagine an AI assistant that can detect frustration in a customer’s voice and adjust its approach accordingly, or one that can pick up on excitement about a particular trend and offer relevant product suggestions. 

How is AI Used in Online Shopping?

AI helps retailers align online shopping experiences to brand both new and old consumer buying behaviors. The way people shop is profoundly different today than it was even ten years ago. According to PwC’s 2024 Voice of the Consumer Survey, consumers approach shopping as a connected, channel-agnostic experience. For example:

• 42% of consumers prefer shopping in-store
• 34% prefer smartphones
• 23% still turn to their PCs when making purchases

How people find items online is changing too. Fore example, 46% of consumers now purchase products directly through social media, up from 21% in 2019. 

The use of different channels and devices to shop underscores a persistent challenge – how can retailers keep the shopping journey consistent? AI is the answer here.

Retailers are Using AI to Improve Online Shopping in a Few Notable Ways: 

• Personalizing experiences across channels and touchpoints (website, app, mobile, PC, in-store, etc.)
• Improving product discovery on digital channels with features like intelligent search and product recommendations
• Offering virtual try-ons and augmented reality experiences (e.g., “try before you buy”) to make digital shopping feel more like an in-store experience
• Using chatbots and virtual assistants to guide customers and make it easier for them to self serve
• Providing automated fraud detection and prevention
• Making inventory management much easier and accurate
• Providing better demand forecasting and merchandising support with predictive analytics
• Supporting targeted marketing and advertising with data-driven insights and automated, personalized messaging

Examples of AI in Online Shopping

Our own clients make great use cases for how to leverage AI for eCommerce and turn online shopping into an experience that resonates. Here are some examples of what AI looks like in the wild and how it moves the needle when it comes to performance.

1. AI-driven social proof messaging increases RPS

Multinational retailer Landmark Group worked with Monetate to personalize content for specific customer segments. Landmark used data that included individual user behavior, preferences, and context to optimize social proof messaging on its mobile app and dynamically adjust product recommendations, promotional banners, and messaging. The approach led to an impressive 39% increase in revenue per session (RPS).

2. Personalized recipes for grocery shoppers lifts engagement

UK grocer Waitrose, a food and beverage retailer with more than 350 stores, used AI-powered personalization to optimize content based on user behavior and past purchase activity. Specifically, Waitrose served up relevant recipes to each visitor, driving click-throughs to the recipes page. This helped them achieve a substantial 66.8% lift in engagement.

3. Social proof on PLPs sells more shoes

British footwear company Clarks used social proof elements like scarcity and popularity messaging (e.g., “Popular! 58 added to cart today!”) to increase shopper trust and inspire their customers to buy. Social proof shows a shopper how popular a product is, introduces scarcity, and creates FOMO. After Clarks implemented Monetate’s Social Proof feature, and AI-powered tool, to displays real-time information like recent purchases and product popularity to their product listing pages (PLPs), they increased new visitor conversion by 4.5% and saw a 12% lift in overall conversion rate. 

4. Dynamic and Customized PDP Layouts Increase Revenue

Office Depot, a leader in the US office supply market, used automated personalization to optimize their Product Description Pages (PDPs), which significantly influence online buying decisions. Office Depot leveraged Monetate’s ML to dynamically adjust the layout and content of their PDPs based on individual customer behavior and preferences. The optimized pages emphasized the most relevant information for each shopper. In just four months, Office Depot saw a revenue increase of nearly $6.9 million thanks to thanks to this AI-powered approach.

5. Tailored Homepage Content Lifts Product Views and Total Conversion

Fingerhut, a company known for making merchandise affordable through credit accounts, delivered hyper-personalized experiences by integrating Monetate’s Personalization engine with their customer data platform, Tealium. This integration allowed Fingerhut to act on rich customer insights, including third-party credit data, past shopping behavior, and brand preferences. They tailored content on their homepage in a way that resonated with a given customer (e.g., by presenting offers in a customer’s favorite category), achieving a 4% lift in product views and a 1.1% increase in total conversion.