👩‍💻 How to add AI product recommendations to your online store

Why e-commerce needs product recommendations to meet rising customer expectations

For years, the e‑commerce model was built around choice: the bigger the assortment, the better. But that approach left customers overwhelmed. Endless scrolls. Dozens of tabs. And a recurring question: “Which one should I buy?”

That’s where product recommendations come in. Today’s shoppers don’t just want choice, they want guidance. They want to feel confident they’re picking the right product, whether they’re shopping for a laptop, hiking boots, or a coffee machine. And if they don't, you lose sales and gain returns and a very busy customer support team. Plus, customers who aren't satisfied with their initial purchase are less likely to come back. 

So true customer guidance is key to e-commerce success and AI helps to make that guidance scalable. It can turn your expertise into dynamic advice across every step of the customer journey, from your homepage to external channels or a chat window. 

In this article, we’ll break down the main ways to offer AI-powered product recommendations, how to implement them, and what the future of product advice will look like.

4 different ways to offer AI-powered product recommendations

There’s no single way to approach AI recommendations. The best online stores make sure product recommendations become part of the complete customer journey and guide customers wherever they need help. Let’s have a look at the different possibilities.

1. Product finders and recommendation quizzes

Product finders (or recommendation quizzes) ask shoppers structured questions about their needs, for example, “Who will use the product?” or “What’s the most important use case?” and return tailored advice. 

With AI, product finders are built more easily and AI also makes them smarter and more dynamic. Instead of pre‑set paths, AI can adapt questions based on earlier answers, skip unnecessary steps, and even phrase advice in a way that matches the shopper’s intent.

With Aiden, you easily build product recommendation quizzes with AI that deliver expert advice, not just algorithmic guesses. You define scoring logic, weights and filters, and the tool matches products and explains why they’re recommended. This way you always offer personalized and nuanced advice to help your customers the best way possible.

Why product finders are a powerful way to advise: 

• Customers feel guided rather than sold to. 
• Quizzes gather zero‑party data that can inform marketing and future recommendations.
• AI reduces friction, making the experience feel more like a conversation than a form.

2. On‑page product suggestion blocks

Product suggestion blocks are dynamically displayed alongside product or category pages, e.g. “Customers also bought”, “Recommended for you”, or “Pair with”. 

When powered by AI and integrated with guided-selling data, these blocks become context-aware. They update based on answers from quizzes or browsing behavior, so recommendations remain relevant and informed by the customer’s journey.

Another option is to showcase a product check at the product-detail page (PDP). By doing so you’re able to answer the key question: "Is this product right for me?" and if not, you’re able to highlight alternative products that might be a better fit. 

What makes on-page product suggestions work:

• Reinforces advice across the shopping journey.
• Drives cross-sell and upsell opportunities by surfacing relevant complementary products.
• Feels integrated, not intrusive, maintaining flow and trust.

We’ve seen an average of 15% more add-to-carts when a product check is available on the PDP.

3. Conversational recommendations via chat or widgets

Shoppers can interact with a chat widget or recommendation assistant to ask questions and receive product advice. These experiences mirror human-like conversations, but are powered by AI and structured expert logic.

The catch: purely generic LLM chatbots don’t translate expertise. The best experiences use a structured advice layer with your store’s domain knowledge, so responses feel consistent, meaningful, and aligned with your brand voice. 

Why conversational recommendations via chats or widgets can help:

• Feels like speaking to a trusted advisor, not a generic assistant.
• Scales structured expertise across thousands of interactions without manual scripting.
• Maintains trust by ensuring clarity and coherence in advice rather than generic responses.

4. AI advice through feeds and external channels

Instead of thinking only about product lists on your owned channels, you can start providing structured recommendation logic to external platforms and feeds. This means your expertise isn’t bound to your website, it shows up wherever your customers are, creating guidance throughout the entire shopping journey and increasing conversion rates structurally.

While most product feeds today only send prices and product specs, AI opens the door to a future where you could send expertise, too: short, structured snippets that explain who a product is for and why it’s recommended. Imagine a future where your feed doesn’t just say “running shoes, €79,” but “best choice for beginners running 2–3 times a week”.

While most product feeds today still contain only basic product details, you can already share product recommendations with reasoning via owned channels like email and SMS. Over time, short, structured snippets that explain who a product is for will expand into more external feeds and touchpoints as AI and industry data standards evolve.

Why advice via external channels and feeds matter:

• Puts your expertise in front of customers before they even reach your site.
• Builds trust by offering advice instead of generic product listings.
• Prepares your store for a future where shopping starts in AI‑powered search engines.

Now that you’ve learned how to add AI-powered pro

How to start implementing AI product recommendations

Adding AI-powered product recommendations to your store isn't just about turning on a tool. To make it truly work, and reflect your expertise, you’ll have to take a few deliberate steps. 

1. Identity moments where customers need help in the customer journey

Start by mapping where customers feel stuck. Do they struggle to choose between similar products? Do they drop off after viewing certain categories? Or are there points in the journey where you can make them aware of complementary items?

These are the “help moments” where recommendations make the most impact. For example:

• Offer a quiz on your category page to guide first-time buyers.
• Add page blocks that suggest compatible products after adding an item to the cart.
• Add product checks on product-detail pages to increase confidence. 
• Add a conversational assistant that answers “which model is right for me” on products your support team receives a lot of questions about.

By pinpointing these moments first, you can prioritize where AI will have the biggest effect,  instead of trying to recommend everywhere from the start. Which can be super time-consuming. It’s better to start with a test-first approach and learn from the insights and optimize next. 

2. Choose your approach and AI recommendation tool

Once you know where you want to offer guidance, decide which methods fit best. As shared before, you have a few options: 

• Add quizzes for structured, confidence-building advice.
• Add on-page blocks for upsell and cross-sell opportunities.
• Add conversational widgets for customers who want to ask questions directly.

How to choose the right AI product recommendation tool

Not every tool is built the same way. Some are plug-and-play widgets that offer quick personalization, while others, like Aiden, focus on structuring your expertise to scale.

When evaluating tools, look for: 

• Flexibility: Can it handle your product range, tone of voice and category-specific logic?
• Integration: Does it connect with your e-commerce platform, CRM, and marketing tools?
• Control: Can you see and adjust the logic behind recommendations, or is it a “black box”? 
• Scalability: How easily are you able to offer advice at scale? Because covering one category won’t make an impact to increase your overall e-commerce results. You can also think of support for future channels, countries, languages and or even advice feeds.

The right choice depends on your product offering and ambitions, but control and transparency are key if you want your advice to stay consistent to protect your brand. 

3. Customize logic to match your strategy

AI product recommendations are only as good as the logic behind them. To get useful, trustworthy advice, you’ll need to translate your in-store knowledge into a structured advice layer.

That starts with three building blocks:

1. Questions that reflect the customer’s reality. Think like a salesperson helping a customer in a store. Ask about the shopper’s situation, goals, and preferences, not just product specs. “Who will use this?” works better than “Which size do you want?” because it’s easier for the customer to answer and gives you richer context.
2. A clear matching system. Behind every answer, there’s logic. Which products fit if the shopper says they want a “lightweight hiking boot”? Are they “perfectly suitable,” “well-suited,” “neutral,” or “not suitable”? AI can only provide nuanced advice if the matching includes nuance, not just binary true/false data.
3. The 'why' behind the advice. Good recommendations don’t just show products; they explain why those products were suggested. That explanation builds trust and helps the customer feel confident about their choice.

By structuring your logic this way, AI doesn’t have to guess what the right answer is. Instead, it draws on a foundation of real expertise, and can deliver that advice dynamically, in quizzes, on-page blocks, chat, or even email.

4. Decide where and when to trigger recommendations

Recommendations should feel like help, not like an aggressive sales tactic. The key is to offer guidance in the moments that matter and in places where it feels natural. When and where should recommendations appear? AI recommendations can be triggered at different stages of the journey:

• After engagement: For example, show tailored product advice immediately after a shopper completes a quiz.
• On hesitation signals: If someone lingers on a product page for more than 30 seconds, you can trigger a chat bubble or quiz pop-up offering help.
• After key actions: When a customer adds an item to their cart, AI can recommend the right accessories or upgrades.
• In follow-ups: Recommendations don’t have to stop on-site. They can continue in post-visit emails, where AI can explain why those products were suggested.

In terms of placements, you want to create a web of guidance across your site and even beyond. Here are some of the strongest placements you can think of: 

• Category pages: An AI product finder embedded at the top of a crowded category page helps customers navigate choice overload from the start.
• Product detail pages (PDPs): A widget or check on PDPs catches visitors who land directly from Google or ads, and can offer alternative recommendations if the current product isn’t their best match. 
• Content pages or buying guides: If you already have pages with product advice, embedding recommendations there reinforces the guidance.
• Search results pages: When shoppers search for broad terms like “headphones” or “jackets,” AI can step in with guidance right where the uncertainty starts.
• Navigation links or banners: A “Help me choose” link in your menu or a banner on a relevant page invites customers to seek advice when they need it.
• Triggered pop-ups: Use sparingly, but effective for hesitant customers (e.g. if someone scrolls halfway through a page or is idle for 2 minutes).
• Email follow-ups: If a shopper abandons their browsing session mid-quiz or mid-browse, send them recommendations with context right in their inbox.

By using different placements and triggers, you make sure advice is always accessible without being intrusive. The best stores don’t just “launch a quiz.” They integrate AI guidance into the entire customer journey, so the right help is always there at the right time.

“Guided selling helps customers make decisions faster, and it also gives our store staff a solid foundation. You can only serve customers well if the advice is strong and consistent everywhere.”

- Joren Neef, Digital Director at MediaMarkt

5. Test, measure, and optimize

AI product recommendations aren’t a one‑time setup. The first version you launch will already help customers, but the real impact comes from continuous improvement and A/B testing.

Start small, for example, with one product group or category, and track how shoppers interact. Look at conversion rates, drop‑off points, and whether recommended products lead to fewer returns. Then refine. Tweak the questions you ask, adjust the matching logic, or shift where and when recommendations appear.

Check results again, make small changes, and repeat. This cycle doesn’t need to be complicated, reviewing after launch and improving regularly keeps your recommendations sharp and your advice trustworthy.

Frequently asked questions about AI product recommendations in e‑commerce

Now you know how to add AI product recommendations to your store and overall strategy, there might be a few questions left. We’ve gathered the frequently asked questions we retrieve from people with different online stores. Here’s all there is to know. 

What are AI product recommendations?

AI product recommendations are suggestions generated by software and machine learning that uses customer data, product knowledge, and defined logic to guide shoppers to the right products.

How do AI product recommendations work, exactly?

AI combines what it learns about the shopper with the expertise you’ve built into your store. It analyzes signals like quiz answers, browsing behavior, or purchase history, and applies your advice logic to suggest the best products.

This can take different forms: powering product finders, on‑page suggestion blocks, conversational chat widgets, or even recommendations in emails and external feeds.

With Aiden, AI isn’t just a black box add-on. It’s a structured way to leverage your domain knowledge. Aiden’s AI copilot can draft question flows and suggestion rules based on your product catalog, which your team then fine‑tunes with the nuance and expertise that makes your advice truly stand out.

What’s the difference between AI product recommendations and traditional “related products”?

Traditional “related products” are static and rule‑based, think “show more items from this category.” AI recommendations go one step further, they adapt to each shopper and when combined with guided selling, explain why a product is suggested, not just show “more of the same.”

Can AI recommendations help with cross‑sell and upsell?

Yes. By understanding what a customer is buying and why, AI can suggest complementary products (cross‑sell) or upgraded versions (upsell) in a way that feels helpful, not pushy.

Is customer data safe when using AI recommendations?

With the right setup, yes. Modern AI recommendation tools use secure data handling and integrate with your existing systems, ensuring sensitive customer data isn’t exposed or misused.

What to expect from the future of AI product recommendations?

AI will play a bigger role in how customers decide what to buy, but it won’t replace expertise. Generic AI can list products, summarize reviews, and even “sound” confident. What it can’t do on its own is deliver real recommendations: the kind that comes from understanding trade‑offs, asking the right follow‑up questions, and saying, “Here’s what we’d choose, and here’s why.”

That’s why the future belongs to expert stores. Stores that don’t just have products, but structured knowledge, and the tools to share it. AI becomes the bridge: scaling that expertise into quizzes, chat conversations, emails, and beyond.

The result? Advice that feels personal, trustworthy, and unmistakably yours, in a world full of generic AI answers.

Get ready for the next generation of product recommendations with Aiden

E‑commerce has mastered ‘what to sell’ and ‘how to ship.’ What’s still broken is how to help. Most online stores offer an endless overview of products, but very little guidance for customers trying to make the right choice.

Aiden was built to change that. Our mission is simple: make great advice available to everyone. We believe the future of product recommendations isn't about generic AI guessing what customers might want. It’s about scaling the real expertise that makes your store unique, and delivering that advice wherever customers need it.

We’re already helping 200+ leading online brands and retailers turn their knowledge into guidance that converts, and we’d love to help you do the same. Not sure where to start? Book your demo or take a platform tour to see how Aiden can help your customers decide with confidence, and make your expertise the heart of the next generation of AI shopping experiences.