Why and how to personalize websites with AI

A website that instantly understands what you need and shows you relevant products, content, or offers without you even searching. That’s the promise of personalization.

AI personalization is the closest we can get to that. For startup founders and business owners (even non-developers), using AI means your site can automatically adapt to each user. Done right, this leads to happier visitors, higher engagement, and ultimately more conversions.

In fact, consumers now expect this level of customized service. Some even feel actively frustrated when they don’t get it.

The good news is that even without a technical background, you can achieve a personalized UX on your website by combining the right strategy, tools, and possibly some expert help.

Why use AI for website personalization in the first place?

Personalizing a site isn’t new. In the past, marketers used manual rules (e.g., show X banner to new visitors, Y to returning ones). But manual methods and static rules only go so far. 

AI-powered personalization uses machine learning to analyze vast amounts of user data and behavior in ways humans simply can’t. It finds patterns and preferences, then automatically delivers content or product recommendations matched to each user.

• They learn.

AI systems constantly collect data about each visitor, such as pages clicked and time spent, location, device, and even the time of day. They merge this with historical and third-party data to build rich user profiles. Using algorithms, the AI identifies patterns (for example, “users who view category A often also like B”) and segments users into groups with similar behaviors (audience clustering).

• They adapt.

Because AI can process data instantly, it can adjust a user’s experience on the fly. If a visitor is browsing sports shoes, an AI-driven site might immediately highlight athletic apparel or show a personalized promo, rather than waiting for the next visit. This real-time responsiveness creates a sense that the website “gets” the user’s intent at that moment.

• They recommend.

Machine learning models anticipate what the user might want next. This is known as predictive personalization. AI algorithms analyze historical data and patterns and can suggest products or content the visitor hasn’t even directly looked at yet, but is likely to interest them.

• They automate.

It would take an army of content managers to manually customize a site for each user segment, and even then, it wouldn’t update in real time with each click. AI automation saves huge amounts of manual effort. This translates into cost savings as well – studies suggest a mature personalization program can reduce customer acquisition costs by up to 50% by making marketing more efficient.

By the way, this is exactly the kind of integration work our team can assist with – from simply automating workflows to full-scope business automation.

• Then, they help you convert.

Visitors feel understood when a site shows what they need without effort. When content resonates with them, they engage more and stay on your site longer. If the offers match their interests, they are also more likely to add items to their cart or sign up.

Personalized recommendations can also increase average order value by cross-selling relevant items. For instance, Amazon’s recommendation engine is responsible for roughly 35% of its total sales, billions of dollars that Amazon might miss if it showed the same products to everyone.

However. AI is just a tool. It still needs your guidance on business goals and creative ideas.

AI personalization can be a very valid and helpful strategy for growth. And thanks to many available platforms and services (and partners like Merge who can integrate them), you don’t need to build an AI from scratch to get started.

What types of AI website personalization are there?

Not all personalization is the same. There are several types of website personalization you can implement with AI, each based on different kinds of user data.

Behavioral AI website personalization

This approach targets users based on their past and present behavior on your site. AI analyzes what pages or products a visitor viewed, items added to cart, content they clicked, and so on. The site then adapts to match those demonstrated interests.

Essentially, the site “remembers” what each person did and adjusts accordingly by showing relevant recommendations, highlighting categories related to their browsing history, or even changing the order of menu items.

This type is very effective for increasing engagement because it finds patterns in behavior (e.g., “users who watch these videos also tend to like these articles”) and automatically applies those insights to personalize content.

Demographic AI website personalization

Demographic personalization uses known attributes about the user, like their age, gender, location, language, or other profile info, and crafts messaging and offers that resonate with that particular demographic group.

For example, a B2B software website could show unique value propositions depending on the visitor’s industry or job role (a marketing professional sees a different highlight than an engineer would). Geographic targeting is a common form of this since your site can detect a visitor’s location (via IP or profile) and adjust the content.

Demographic info helps craft more relevant content from the start, even for first-time visitors (when behavioral data isn’t available yet).

Contextual AI website personalization

Contextual personalization focuses on the context of the visit itself, like the user’s device, channel, or real-time conditions. The principle is “right content, right time, and place.” For example, the website might detect that a visitor is on a mobile phone versus a desktop. 

A contextual rule (augmented by AI insights) could then simplify the page layout for mobile, or show a prompt to open your app instead for a better small-screen experience. 

Another context factor is the traffic source. If someone arrives via a Google search for “affordable laptops,” the landing page could dynamically feature your budget laptops, whereas a visitor coming from a Facebook ad about gaming might see high-end gaming rigs first. 

Predictive AI website personalization

Predictive personalization uses machine learning to anticipate user needs and preferences before they’re explicitly expressed. While the other types react to known data (behavior, demographics, etc.), the predictive type goes a step further: the AI predicts what a user is likely to want next. 

When done well, predictive personalization feels very nice for the user. However, it requires a good amount of data to be accurate, which is why it’s often employed by companies with larger user bases or using third-party AI services. 

Even if you’re a startup, you can start small with predictive elements – for instance, using a pre-built recommendation engine (like AWS Amazon Personalize) that has proven algorithms. Over time, as your data grows, the predictions get better and your site becomes increasingly intuitive for users.

All these types of personalization aren’t mutually exclusive, though. In fact, a winning strategy is actually to combine them based on your goals and take the best of each.

How to implement personalization with AI the right way

So, where do you start? 

When you think about personalization, what is the most logical step? What do you need to know first? 

Your users, exactly.

Step 1. You need data about your users.

The more relevant, high-quality data you feed the system, the better it can tailor content. Focus on gathering first-party data, such as website analytics, purchase history, on-site search queries, clicks, time on page, form inputs, etc.

In practice, start by implementing analytics that capture key events (e.g., viewing a product, adding to cart, video watched to 90%). Also consider augmenting with contextual data: location (from IP or GPS), device type, referral source, and even external factors like weather via APIs.

If your dataset is small initially, that’s okay: you can begin with broader segments (like new vs. returning visitors) and later incorporate more granular data as it comes in.

Step 2. Segmentation

While one-to-one personalization is the end goal, it’s often more effective to start with user segmentation. Group your audience into segments that matter for your business – for instance, by behavior (e.g. “browsed Product Category A”), by demographics (e.g. “enterprise vs. small biz” if B2B, or “student vs. parent” for an education site), or by stage in customer journey (“just signed up for trial” vs “regular customer”). 

AI can assist by finding patterns and creating segments automatically (clustering users based on similarities), but you can also define segments based on your domain knowledge. Once segments are defined, create personalized variations of key elements for each segment. 

Step 3. Recommendation engine

Recommendation engines use machine learning (often collaborative filtering, content-based filtering, or hybrid models) to suggest products, content, or features to users based on various data. If you’ve ever seen “Recommended for you” carousels, product suggestion grids, or article recommendations, that’s the work of a recommendation engine.

You don’t have to invent these from scratch – there are numerous AI services and tools that provide recommendation algorithms out-of-the-box (e.g., AWS Amazon Personalize, Google Recommendations AI, Dynamic Yield). As a business owner, you can integrate these via plugins or API with moderate effort. 

Step 4. Content

Beyond product recommendations, think of all elements of your website that could be dynamic for each user. This includes calls-to-action, banners, images, and even navigation menus. A smart strategy is to make key landing pages modular, with content blocks that an AI can swap in and out. 

When implementing dynamic content, start with high-impact areas: homepage, product pages, checkout, and any entry landing pages. Use your segmentation to decide what variants to prepare. Then let the AI or personalization tool serve the appropriate content. Always keep a default for “unknown” users (often your generic best-guess content).

Examples of AI website personalization

It helps to see how others are using AI to personalize their websites and apps. Below, we got a few real examples from big names in the industry, distilled in a cute little table:

Overall, AI personalization is definitely driving real results across industries - higher sales, better engagement, and improved customer satisfaction. Even more, with modern AI, it’s achievable for businesses of all sizes - you don’t need a fancy engineering team to do it. The key is to start where it matters for your users and use the right tools.

TL;DR

Website personalization is constantly developing. Trends like hyper-personalization (basically one-to-one interactions) and personalization in new channels (like voice and AR) are already being implemented. If it’s not cringy and made respectfully, a personalized UX makes every visitor feel valued and seen, which is the foundation of loyalty and conversion right now.

And you don’t have to do it alone. 

Our Merge team specializes in AI integration, from conceptual proof-of-concept development to full-scale deployment. With the right strategy and support, even non-developers can use AI personalization to improve their business and bring their users closer.