How to Use AI for Product Recommendations

“Customers who bought this also bought…” used to be the ceiling of personalisation. Here’s what’s actually possible now — and how to set it up on CS-Cart.

How to Use AI for Product Recommendations

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Open the admin panel of a typical CS-Cart store, and you’ll usually find the default “related products” widget — a row of items sharing the same category or manually linked by whoever managed the catalogue. It works, technically. It’s also the same row shown to a first-time visitor and a customer who’s placed fifteen orders with you, which means it’s barely personalisation at all.

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The gap between that and a genuinely AI-driven recommendation engine is bigger than most CS-Cart store owners realise — and it’s one of the easier upgrades to make once you understand what’s actually different.

This article covers what AI actually adds to product recommendations, the different approaches you can use, and how to set one up properly on a CS-Cart store.

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What’s Wrong With CS-Cart’s Default Recommendations

CS-Cart’s built-in recommendation widgets work off simple, static rules:

• “Customers who bought X also bought Y” (based on raw co-purchase counts)
• “Related products” (based on shared category or tags)
• “Best sellers” (the same list shown to every visitor)

These aren’t useless — they’re better than nothing. But they share one core weakness: they don’t adapt to the individual person looking at the screen. A first-time visitor and a returning customer with five past purchases see the exact same “you might also like” row. A customer browsing for a gift gets recommendations based on their own past purchase history, which has nothing to do with what they’re shopping for right now.

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What AI Actually Changes

1. It Learns From Behavior, Not Just Transactions

Basic recommendation logic typically only looks at completed purchases. AI-driven engines factor in browsing behaviour, time spent on a product, search queries, cart additions (even abandoned ones), and category exploration — building a much richer picture of intent than purchase history alone provides.

2. It Understands Similarity Beyond Shared Tags

Traditional “related products” logic usually means “same category” or “same tag". AI-powered systems can use semantic similarity – recognising that two products are related based on descriptions, attributes, and usage context, even if they were never manually tagged together by someone on your team.

3. It personalises in real time.

Rather than showing the same recommendation list to everyone, AI engines can adjust recommendations per visitor, per session—updating as someone browses, rather than relying on a static list computed once and shown to all.

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4. It Recommends Across More Than Just Products

This is a detail most stores miss entirely: recommendation logic doesn’t have to stop at products. The same underlying approach can surface relevant blog content, buying guides, or current deals — useful for stores where a purchase decision benefits from a bit of context before checkout.

5. It Can Recover Intent, Not Just Suggest It

Some AI recommendation systems factor in abandoned carts and incomplete searches as signals — surfacing the exact product (or a close alternative) a visitor showed interest in but didn’t act on, rather than waiting for a separate cart-recovery email days later.

The Main Approaches to AI-Powered Recommendations

There isn’t one single technique — different methods suit different situations, and most strong systems blend more than one.

Collaborative filtering recommends based on patterns across many users: "People similar to you liked this.” Works well once you have enough traffic and purchase data to find meaningful patterns.

Content-based filtering recommends based on product attributes and descriptions, comparing the items themselves rather than user behaviour. Useful for newer stores that don’t yet have enough purchase history for collaborative filtering to work well.

Semantic/embedding-based matching — Converts product data into a mathematical representation of meaning, allowing the system to find genuinely similar products even without shared tags or categories. This is what allows “related” results to actually feel related, rather than just sharing a category label.

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Hybrid approaches — Most production systems combine multiple methods, weighting them based on how much data is available for a given visitor or product. A brand-new visitor might get content-based recommendations; a returning customer with purchase history gets collaborative filtering layered on top.

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Where to Actually Place Recommendations

Where you show recommendations matters almost as much as how good they are.

• Homepage — Best for returning visitors; personalized picks based on past behavior
• Product pages — “Similar products” or “frequently bought together", shown alongside the product being viewed
• Cart page — Last-minute add-ons relevant to what’s already in the cart, often the highest-converting placement
• Checkout confirmation — Post-purchase recommendations, useful for repeat-purchase categories
• Email and abandoned cart messages — Personalized product suggestions instead of a generic “come back” message
• Search results — Recommendations that supplement weak or zero-result searches with relevant alternatives

Cart page placements tend to have the best conversion rates since the customer has already shown clear purchase intent — but they’re also the easiest to overdo. One or two relevant suggestions outperform a long row that feels like upselling for its own sake.

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Setting It Up: What the Process Generally Looks Like

The exact mechanics vary by platform and tool, but most AI recommendation setups follow a similar shape:

Step 1: Connect Your Product and Behavioral Data

The engine needs access to your product catalogue (titles, descriptions, categories, and attributes) and behavioural data (views, searches, cart activity, and purchase history). The richer this input, the better the output.

Step 2: Choose Where Recommendations Will Appear

Decide on placement before going live — homepage, product page, cart, email, or some combination. Don’t try to deploy everywhere on day one.

Step 3: Set Initial Rules and Guardrails

Even AI-driven systems usually allow some manual control — excluding out-of-stock items, capping how often a single product appears, or boosting specific categories during a promotional period.

Step 4: Let It Run and Collect Data

Recommendation quality typically improves as the system gathers more behavioural data. Early results may be rougher than what you’ll see after a few weeks of real traffic feeding the model.

Step 5: Measure and Refine

Track click-through rate on recommended products, conversion rate from recommendation clicks, and impact on average order value. Adjust placement or weighting based on what the data shows — not assumptions about what “should” work.

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How to Know If It’s Actually Working

Set these benchmarks before launch so you have something to compare against:

• Click-through rate on recommended products
• Conversion rate specifically from recommendation-driven clicks
• Average order value for sessions that included a recommendation interaction vs. those that didn’t
• Revenue attributed to recommendations — many tools report this directly

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If recommendations aren’t moving any of these numbers after a reasonable testing period, the issue is usually data quality or placement — not the underlying AI itself.

A Word of Caution

AI recommendations work best when they feel helpful, not pushy. A few things that backfire:

• Recommending out-of-stock or discontinued items — frustrating and avoidable with proper guardrails
• Over-personalizing to the point of feeling invasive — there’s a line between “relevant” and “uncomfortably specific”
• Cluttering every page with recommendation widgets — more isn’t always better; one well-placed row beats five mediocre ones
• Ignoring new visitors entirely — a system that only works well for returning customers leaves first-time visitors with a worse experience

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Where Ecartify’s AI Merchandising Engine Fits In

For CS-Cart stores specifically, this exact category — personalised, AI-driven recommendations — is what Ecartify’s AI Merchandising Engine add-on is built to handle, rather than requiring a third-party tool retrofitted onto the platform.

It covers recommendations across products, categories, blog content, and current deals, using customer behaviour and purchase history combined with semantic similarity matching — the same approach described above, but built natively into CS-Cart’s data structure rather than bolted on as an external integration. That matters in practice: it means the engine already understands your product catalogue, categories, and customer data without a separate data-sync project before it can start learning.

It’s available directly through **Ecartify’s add-ons store, and if you’re trying to figure out where it would have the biggest impact for your specific catalogue — the homepage, the cart page, or both — their [contact page](https://ecartify.com/contact-us/)** is a reasonable place to ask before installing.

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The Bottom Line

The gap between “customers who bought this also bought” and genuinely AI-driven recommendations isn’t subtle once you’ve seen both in action. The former is static and identical for everyone. The latter adapts to the person actually looking at the screen — which, in eCommerce, is usually where the real revenue is hiding.

Start with one or two placements, measure honestly, and expand from there.

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*Has a product recommendation ever genuinely surprised you with how relevant it was? Curious to hear examples in the comments.*