AI for product recommendations in e-commerce
Have you ever clicked on a “You may also like” or “Other people also bought” widget in online stores? These personalised product recommendations are usually powered by AI. And they aren’t just convenient — they’re a key driver of sales and customer loyalty in e-commerce, because 80% of consumers are more likely to make a purchase from brands offering personalised experiences.
How AI-Personalisation Works: simply explained for non-technical professionals
AI-Personalisation uses a combination of historical customer data — such as browsing history, past purchases, and products added to a shopping cart or a wish list — along with basic demographic insights, to better understand individual preferences. This data helps predict what each customer might be interested in and deliver personalised product recommendations in real time.
Here's a step-by-step breakdown of how AI recommendations work in e-commerce:
1. Data Collection
At first, AI algorithm gathers the most important customer data such as:
- Browsing patterns (e.g., categories explored, clicks on products, time spent on pages)
- Purchase history (e.g., items bought and frequency of purchases)
- Demographic information (e.g., age, gender, location)
- Search behavior (e.g., keywords used)
2. Data Analysis and Model Training
Then, AI learns from huge datasets of historical data to make highly relevant recommendations. While there are several methods to build recommendation systems, Collaborative Filtering and Content-Based Filtering are the most commonly used in e-commerce due to their effectiveness and simplicity.
- Collaborative Filtering: Recommends items based on what similar customers purchased
- Content-Based Filtering: Suggests items similar to those a customer has shown interest in, focusing on attributes like color, size, or style
3. Continuous learning and personalisation in real-time
As customers interact with your online store, AI keeps gathering data and learning behavioral patterns to update its recommendations and remain relevant, showing products related to current interests.
4. Providing personalised recommendations
The tailored suggestions usually appear as:
- Product recommendation widgets (e.g., “You may also like“, “You may also consider”, “Frequently bought together”)
- Personalised search results pages tailored to customer preferences
- Personalised email campaigns
How AI-Personalisation Improves the Shopping Experience in E-Commerce:
1. Provide Tailored Product Recommendations
AI learns from customer behavior to recommend products they are most likely to buy. This not only boosts sales but also creates a sense of personal attention.
Success Story: Amazon’s recommendation algorithm drives ca. 35% of the company’s revenue. Their algorithm focuses on finding similar items rather than similar customers (a traditional approach) and suggests products based on the customer’s individual interests.
"If we want to have 20 million customers, then we want to have 20 million 'stores.' ... Our mission is to be the earth's most customer-centric company." -Jeff Bezos
2. Improve Engagement with Dynamic Content
AI ensures customers see the most relevant banners, discounts, or landing pages based on their preferences. This makes the shopping experience seamless and relevant.
Success Story: Sephora's AI-powered mobile app uses personalisation to create custom makeup recommendations, which increases customers engagement and satisfaction. According to McKinsey, personalisation efforts like these can provide up to 15% increases in revenue and 10-30% increases in marketing-spend efficiency.
3. Faster Search with AI
AI-powered search tools like visual search and voice search allow customers to find products quickly, reducing frustration.
Success Story: ASOS has a visual search tool on their App called Style Match. It lets shoppers upload a photo or take one to find products that look alike. Then, AI analyses the image and suggests matching items right away to make the online shopping experience faster and smoother.
4. Smart Cross-Selling and Upselling
AI can display relevant products at different stages of the customer journey — when a product is added to the cart, during checkout, or through personalised product recommendations in emails. These suggestions guide customers towards complementary or higher-priced items, boosting AOV (Average Order Value).
5. Prevent Cart Abandonment
Modern AI solutions can prevent visitor bounce and cart abandonment even before they happen. Tools like Recovery pages provide leaving visitors with highly tailored product recommendations based on their preferences, helping to bring back around 14% of otherwise lost customers and encourage them to complete a purchase.
6. Reduce Choice Overload
Nowadays, when consumers enter an online store, they are often overwhelmed by the number of available options, making it harder to make a choice. AI-Personalisation works as an intelligent shopping assistant, suggesting highly relevant products and simplifying decision-making.
7. Build Long-Term Customer Loyalty
Personalisation fosters trust and loyalty. When customers feel understood and valued, they are more likely to return and recommend your store to others.
A McKinsey study states that personalisation has a big impact on customer engagement and loyalty. The findings show that 76% of customers are more likely to consider a brand when they receive personalised communications, and 78% are more likely to buy again because of this.
The Business Impact of using AI-Personalisation in E-Commerce
1. Increased Sales and Revenue
A survey of 300 merchants by Barilliance revealed that personalised product recommendations contributed up to 31% of their e-commerce revenue.
2. Enhanced Customer Lifetime Value (CLV)
When stores provide a personalised experience, customers tend to shop more often and spend higher sums of money, which boosts their overall lifetime value. In the current e-commerce market landscape, where acquiring a new customer costs 5 to 10 times more than retaining an existing one investing in personalisation ensures both higher profitability and long-term loyalty.
3. Cost-Efficient Marketing
AI removes the uncertainty and helps to make sure your marketing budget reaches the right customers with the right message at the right moment.
4. Reduced Customer Churn
Advanced AI-Personalisation techniques reduce customer churn rates by up to 15%. By proactively anticipating customer needs and addressing potential pain points, businesses ensure customers remain engaged and are less likely to switch to competitors.
How does NUNAMI AI-Personalisation work?
We at NUNAMI know that choosing the right AI-Personalisation tool may be complicated. Or even VERY complicated. Because the market is full of identical solutions promising you “outstanding” results.
We don’t play this game. Intentionally.
Our solution is simple and transparent, but we still help you with all the technical stuff, provide full control over performance and tracking, and are here to talk to you whenever you need us. Because we believe that e-commerce is already hard enough, so your technical partner shouldn’t be.
That's it. As simple as it is😉
We would be happy to help you grow your e-commerce business and achieve great results — just as we do now with our amazing customers.
No countdown timers or “exclusive limited-time offers” here.
We’re here whenever you are ready to contact us.
