- Blog
- How to Use AI Product Recommendations to Boost Sales
How to Use AI Product Recommendations to Boost Sales
-
Barbara Bartucz
- Conversion
- 6 min read
Table of Contents
Today’s shoppers expect personalized suggestions tailored to their tastes, habits, and buying behavior.
And the numbers back it up: 71% of consumers now expect personalized experiences, and they’re far more likely to hit “buy” when they get them.
That’s where AI-powered product recommendations step in.
In this guide, we’re breaking down:
- What AI product recommendations are
- How they work under the hood
- Real-world examples of brands using them to drive serious revenue
Let’s get into it!
What are AI product recommendations?
AI product recommendations are like having a super-smart shop assistant who never sleeps and always remembers your customers’ preferences.
You’ve seen them in action: those “You might also like” or “Customers also bought” suggestions sprinkled across ecommerce sites.
Here’s an example from Forever 21, who shows this popup right after their visitors add something to their cart, suggesting other related products.
But behind those friendly nudges is some serious machine learning, analyzing both real-time behavior and past data to serve up products tailored to each shopper.
These personalized picks can show up in a few different ways, including:
- Popups: Think timed offers or exit-intent messages that grab attention at the right moment.
- Embedded widgets: Placed on product pages, carts, or homepages to encourage discovery.
- Sticky bars: Subtle, scrollable banners that keep recommendations in view without interrupting the experience.
The value of AI product recommendations is that they provide a personal shopping experience. Suggesting products based on the shopping behavior of a customer, their interests and even social behaviour creates a seamless shopping experience and an increase in average order value with the help of AI.
How do AI product recommendations work?
At the core, AI product recommendations are powered by software that watches how shoppers behave on your website.
It examines what they click on, which products they view, how long they stay on a page, and what they ultimately buy or don’t buy.
But that’s just the beginning. AI doesn’t just focus on one user at a time.
It also processes historical data across thousands (or millions) of interactions:
- past purchases,
- customer profiles,
- product categories,
- pricing patterns, and more.
This helps the system learn what products tend to go well together and what people with similar behavior might be interested in.
The 4-step process behind AI recommendations
Let’s break down the typical AI recommendation process into four key steps:
1. Collecting information
Every click, scroll, or purchase tells a story. AI tools track all of this activity, including purchase history and customer engagement, along with data like location, device type, referral source, and even time of day, to build a real-time profile of each visitor.
2. Analyzing and learning
Once the data is collected, machine learning models start identifying patterns. Are shoppers frequently buying product A after viewing product B? Do users from a specific location prefer a certain brand? The AI and the recommendation system connect these dots.
3. Providing personalized product suggestions
The system generates personalized recommendations based on what it learns, delivering suggestions that feel custom-made for each shopper. This might show up as a “You may also like” section, an exit-intent popup, or a personalized sticky bar on the screen.
4. Learning and adapting
The more users interact, the smarter the AI gets. It continuously refines its recommendations by incorporating new behavior and feedback, ensuring relevance even as trends and customer preferences evolve, which helps improve customer satisfaction and provide tailored suggestions.
Type of machine learning algorithms
AI product recommendation systems aren’t based on guesswork. They rely on tried-and-true machine learning algorithms to analyze data and provide relevant product suggestions.
- Content-based filtering: Recommends products based on the shopper’s own past behavior and preferences.
- Collaborative filtering: Collaborative filtering relies on analyzing the behavior of users with similar tastes to suggest products that are likely to interest a customer.
- Deep learning: Uses neural networks to analyze complex and unstructured data, uncovering patterns that traditional methods might miss.
- Hybrid systems: Combine multiple algorithms, like content-based and collaborative filtering, to improve recommendation accuracy.
How do AI-powered recommendations differ from traditional recommendations?
Here’s a side-by-side look at how AI-powered recommendations stack up against traditional methods:
| Feature | Traditional recommendations | AI-powered recommendations |
|---|---|---|
|
Data processing |
Limited to pre-set rules and small data sets (e.g., past purchases, best sellers)
|
Process massive data in real time—clicks, browsing history, time spent, views, and more
|
|
Personalization
|
Broad segments or rule-based (e.g., “people who bought X also bought Y”)
|
Hyper-personalized suggestions tailored to each individual’s unique behavior
|
|
Adaptability
|
Mostly static; requires manual updates to stay relevant
|
Continuously learns and adapts automatically as new data and user actions come in
|
|
Accuracy
|
Often misses the mark due to simple logic
|
Advanced algorithms and machine learning deliver highly relevant product matches
|
|
ROI
|
Lower, due to limited personalization and outdated recommendations
|
Higher, thanks to better targeting, improved customer experience, and stronger retention
|
3 examples of AI product recommendations
Let’s take a look at how three ecommerce businesses used AI to boost conversions and create personalized shopping experiences:
1. Millie N Me product recommendation popup
Millie N Me had solid website traffic, but many visitors were leaving without buying.
To address this, they introduced a product recommendation popup designed to highlight items that matched each visitor’s browsing behavior.
One version featured the headline “We found you 3 stunning maxi dresses you’ll love” and showcased those products along with their prices.
Behind the scenes, the AI system analyzed browsing patterns and adapted its suggestions over time, presenting shoppers with items they were most likely to be interested in.
See how Millie N Me boosted their sales with the Smart Recommender Popup.
2. Craft Spirit Shop recommender popup
Craft Spirit Shop faced high traffic but low conversion rates on their product pages.
Their solution was AI-driven recommendation popups, tailored to the context of each product page and designed to engage both returning customers and new users.
For instance, visitors on the Buffalo Trace Bourbon page were shown precise recommendations for other bourbons:
Those on the Clase Azul Tequila page got exclusive tequila suggestions:
These popups also included personalized, emotionally engaging headlines.
Learn how Craft Spirit Shop increased its revenue with AI recommendations.
3. Goldelucks
Goldelucks wanted to recover sales from visitors who were about to leave without purchasing. They deployed AI-powered popups triggered just before a visitor abandoned a product page.
For example, on the Chocolate Christmas Tree product page, the popup highlighted other festive treats.
On the Gold Butterfly Explosion Box page, visitors saw related gift options tailored to their user preferences, keeping them engaged and reducing bounce rates.
5 best practices for AI product recommendations
Want to implement AI product recommendations in your store? Here are 5 best practices to keep in mind.
Balance is the key to success. There is a possibility of too much use of AI recommendations without human controls, leading to irrelevant AI suggestions that are frustrating and not helpful. The best way to do it is to refine recommendation engines with human intuition based on AI insights. Also, optimizing the number of tests and data input can help with the efficiency of the algorithms, customer satisfaction, and sales.
Caleb Johnston
Paperstack
1. Keep it focused
When adding AI product recommendations, less is more. Display only a small number of highly relevant suggestions to avoid overwhelming customers.
Too many options can lead to decision fatigue and reduce the likelihood of a purchase.
Here’s an example from Kylie Cosmetics, which only offers one item—a lip oil—making it feel like a more tailored suggestion.
Focus on showing items that truly complement what the shopper is already viewing or has shown interest in.
2. Use contextual placement
To maximize conversions, place recommendations where they naturally align with the customer journey. This could be on product pages, in shopping carts, or through post-purchase emails.
A great example is Sephora, which suggests products under $15 on the cart page and also provides personalized recommendations tailored to each shopper.
This thoughtful placement not only shows a solid understanding of their customers but also helps boost their average order value.
3. Leverage social proof
The influence of social proof shouldn’t be underestimated. Seeing how others have interacted with or reviewed your products can significantly boost the effectiveness of your product recommendations.
Incorporating elements like ratings, reviews, or tags like “bestseller” helps build trust and makes shoppers more likely to engage.
A well-known example is Amazon, which pairs its “Customers who bought this item also bought” suggestions with visible star ratings, reinforcing trust while guiding product discovery.
Social proof adds credibility to your recommendations, encouraging customers to take action.
4. Optimize for mobile
Since most ecommerce browsing happens on mobile, make sure your AI recommendations are optimized for smaller screens.
Ensure that product suggestions are easy to view and interact with, providing a seamless mobile experience. A user-friendly mobile layout can significantly improve conversion rates.
Here’s an example from NuBest, who highlights a product with a strong headline, “Expert-recommended supplements that work best together.” At the bottom of the page, there’s also an endorsement from a medical expert.
5. A/B test your approach
Experiment with different recommendation strategies to find what works best for your audience.
Test different formats and headline options like “Frequently bought together,” “You might also like,” or “Recently viewed” to see which generates the highest engagement.
A/B testing helps fine-tune your recommendations for better results.
How to set up AI product recommendations?
Ready to launch personalized product recommendations on your site? Here’s how to create your own Smart Recommender Popup using OptiMonk—no coding, no hassle.
Step 0. Create an OptiMonk account if you don’t have one yet.
If you haven’t already, start by creating an OptiMonk account. You’ll need it to access the Smart Recommender features.
Step 1. Sign up for an OptiMonk AI account
To start using the AI-powered popup tools, you’ll need to sign up for OptiMonk AI. The process begins with booking a demo, where you’ll meet with a team member who will guide you through the setup.
Step 2: Choose & customize a campaign
Browse through our extensive library of 300+ campaign templates and choose a product recommendation layout that aligns with your goals.
Then, easily customize it with your brand’s design, messaging, and special offers.
Step 3: Insert smart elements
Use Smart Tags to automatically personalize headlines and subheadlines based on each visitor’s interests and behavior.
You can add multiple smart elements to tailor the experience for every user.
For the product element, select the smart recommendation mode, “OptiMonk recommender.”
This setting allows the system to display products tailored to each customer, drawing on their interests and past browsing behavior.
Step 4: Add a control variant
Set up a control version of your popup to run alongside the AI-powered version. This will allow you to compare results accurately and optimize based on actual performance.
Want to test more? Add additional variants as needed.
Step 5: Launch & track results
Once everything’s ready, hit launch and let the AI do its thing. Monitor your results in real-time, track conversions, and enjoy watching your recommendations drive more sales automatically!
FAQ
How does artificial intelligence improve product recommendations on an ecommerce site?
Artificial intelligence analyzes customer behavior in real time—what they view, click, or add to cart—and uses that data to suggest products they’re most likely to buy. On an ecommerce site, this leads to more relevant suggestions, which improves the shopping experience and boosts conversions.
How can AI improve my customer experience without being intrusive?
Good AI doesn’t overwhelm; it assists. When done right, AI-powered recommendations subtly guide shoppers toward relevant products, making their journey smoother. This enhances the customer experience by reducing effort and helping them find what they need faster.
Is it hard to implement AI-powered tools on my ecommerce site?
Not at all. Most modern platforms offer plug-and-play integrations. For example, tools like OptiMonk make it easy to set up and customize AI-powered popups without any coding. You can be up and running in just a few clicks.
Wrapping up
AI-powered recommendations help your ecommerce site do more than just sell—they personalize the shopping experience and boost conversions.
With tools like OptiMonk, it’s easy to deliver smarter suggestions, improve the customer experience, and drive more sales automatically.
Book a free demo and start using AI to your advantage!
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Written by
Barbara Bartucz
Barbara is a Content Marketer at OptiMonk. She takes pride in creating different forms of content, whether it’s an article, a video, or an e-book.
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Product updates: January Release 2025
