6 Ways Generative AI Personalization Enhances User Interactions

There are more ways than ever for people to discover, research, and interact with brands both online and off. As consumers continue to navigate an average of six  channels across a single buying journey, personalization has become the glue that binds this journey together. It’s shifted from a nice-to-have feature to a baseline requirement for building loyalty and driving results. 

Traditional personalization approaches that relied on static segments or simple rules can’t keep up with the relevance and immediacy that consumers now expect from the companies they do business with. Generative AI personalization flips the script, making it possible to customize and streamline what would be an otherwise chaotic buying experience.

What is Generative AI Personalization?

Generative AI personalization uses advanced algorithms to analyze real-time behaviors, preferences, and context, to create unique content, recommendations, and experiences for everyone. This level of customization translates to real business growth. Per Adobe’s 2025 AI and Digital Trends Report, 65% of senior executives said AI and predictive analytics are “primary contributors to growth.” 

That makes sense. Generic messages and static content simply cannot meet the needs of customers who may be at wildly different stages of a given buying journey (and have wildly different needs to begin with). Generative AI personalization is such a superstar because it helps companies create interactions and experiences that are timely, meaningful, and tailored to every single customer. 

6 Ways Generative AI Personalization Enhances User Interactions

Artificial intelligence is a broad term that includes different subfields of AI including Generative AI.  Thanks to ChatGPT, many people now understand that generative AI creates new content when prompted by (human) users with natural language queries and questions.

There are many ways to use generative AI in personalization and across a digital buying journey. We’re focusing on seven of the main ways you can craft individualized experiences for your customers by using generative AI in marketing, content customization, and customer service. These different approaches work together to make the buying journey feel connected and consistent for each person. To illustrate this, we’re going to include some examples from two very different types of gardeners:

• Tiff: The Director of Horticulture at a beloved historic arboretum, Tiff needs to order equipment for her team of gardeners, mulch, seedlings, and other plants for the garden and grounds, and botanical pesticides that are considered safe for plants and animals.
• Marvin: A retired middle school math teacher that’s recently taken up gardening. Marvin is shopping for items a home gardener needs like bulbs, tools, and fencing. He lives in a dryer climate so requires drought-tolerant plants and ground coverings that retain moisture.

Tiff and Marvin have very different needs and goals. But generative AI personalization can be used to create a relevant shopping experience for both of them, even when they visit the same websites. Here’s how: 

🤩1. With Hyper-Personalized Content Recommendations

AI-powered personalization tools analyze user preference and behavior data including browsing history, purchase patterns, and time spent on certain pages. If you’ve ever logged into Netflix, Spotify, or YouTube and been served a list of videos, music, and movies that are calibrated to exactly your tastes, then you know how powerful tailored content can be.

These platforms use AI and machine learning to “recall” past choices and create custom content recommendations like playlists and movie suggestions. In ecommerce, a platform like Monetate uses this information to display recommendations for products and content that match each shopper’s buying behavior and context.

For example, when Tiff, the arboretum’s Director of Horticulture, visits her favorite supplier’s website, she’s presented with products like bulk mulch, specialty seeds, and offers for professional tools. If she has a specific question, a generative AI-powered chatbot can provide instant, tailored advice and suggest custom items for a specific project or need. 

Marvin, our home gardener, gets a different content experience. Instead of bulk supplies and advanced equipment, he’s shown starter kits, easy-care plant bundles, and helpful articles. When Marvin searches for an item on the site, generative AI serves up helpful search suggestions, auto completing his queries, and creating tailored how-to content based on his interests.

💻2. Dynamic Website Personalization

Dynamic website personalization is an approach that customizes content in real-time based on in-session behavior and other signals. It’s automated personalization that adapts on the fly. This is important in many retail scenarios like online grocery shopping, where a customer’s needs may change each time they shop. 

Instacart’s Smart Shop technology, launched in March 2025, is an example of dynamic website personalization that uses AI effectively. The system analyzes over 17 million items and data from millions of grocery trips to understand individual shopping patterns and dietary preferences. 

Rather than presenting generic product listings, Smart Shop creates dynamic, personalized storefronts that adapt with each visit. The system recognizes that over 70% of customers have specific dietary needs and automatically filters products using AI-powered Health Tags across 1.3 billion data points and 500,000 products.

🤖3. AI-Powered Chatbots & Virtual Assistants

ChatGPT introduced the world to marvels of generative AI chatbots and their conversational capabilities. That was nearly three years ago, and this technology is now being integrated into buying journeys in innovative ways. 

Amazon’s Rufus, launched in 2024, is one example of an AI-powered chatbot that’s pushing the boundaries of what we once believed computers could do. Using AI functions like natural language processing (NLP) and natural language understanding, Rufus, can understand the context of a user’s search based on browsing behavior, purchase history, and other signals. It uses this information to provide tailored guidance to shoppers. 

Rufus has already fielded “tens of millions of questions” from customers, helping them compare products, review details, and make informed purchasing decisions. 

This means Tiff can quickly access bulk pricing comparisons and professional-grade product specifications, while Marvin receives beginner-friendly explanations about recommendations for appropriate plants, plus the right tools to care for them. The AI assistant remembers their preferences and previous interactions, creating increasingly personalized recommendations with each conversation.

📩4. Automated Email & Marketing Campaigns

AI-powered segmentation and targeting technology uses AI and machine learning to identify key customer segments and test different marketing and content approaches for each of them. Monetate’s platform analyzes hundreds of data points across customer interactions, identifies audience segments, and lets you test different messaging and content approaches to see what resonates.

Once segments are established, they can be used to create customized messaging for email and marketing campaigns. Generative AI factors into the personalization journey by, for example, creating unique email subject lines and promotional copy that’s customized based on a segment’s characteristics.

Professional buyers like Tiff may receive emails highlighting technical specifications about commercial-grade equipment, with bulk purchases framed in business terms. For Marvin, the same products are described in beginner-friendly language, with easy-to-follow instructions, and basic terminology aimed at novice gardeners. 

🧑‍💻5. Adaptive UX/UI for Individual Users

AI creates dynamic, personalized websites by automatically adjusting important UI elements in real-time. This is what Orchid AI, the intelligence layer that powers Monetate, does. The platform analyzes hundreds of data points to determine the optimal content, offers, and messaging for each visitor, creating truly individualized experiences that scale across millions of unique interactions.

For busy industrial buyers like Tiff, an retailer’s home page may display wholesale pricing structures, bulk ordering options, and technical specifications prominently. When Marvin visits, the same website features beginner-friendly navigation, simplified product categories, and easy-to-understand gardening terminology. 

This dynamic UI customization leads to deeper engagement with each customer. Visitors feel recognized and seen. The experience is consistent which makes the overall buying experience a more cohesive and satisfying one.

📈6. Predictive Analytics for User Behavior

Predictive analytics is a subset of AI that uses data and machine learning to anticipate (or predict) future outcomes. It can help retailers forecast how website visitors will behave, what they want, and uncover rising (and falling) trends and pain points. It works by analyzing historical data like past purchases, browsing behavior, search queries, and web site usage. 

Using predictive analytics is an incredibly effective way to remain relevant to your customers and motivate them to make a purchase. It’s an ongoing process that uses real-time data to constantly evaluate and assess customers as they interact with your website, purchase products, and interact with various tools like your search bar and chatbot.  

Predictive analytics help inform what products to recommend. So, Tiff gets an offer for discounted gardening gloves in bulk, while Marvin gets a recommendation for a “starter bundle” that might include a pair of gloves, a garden kneeler pad, and a small trowel. These recommendations are based on a huge trove of customer data.