The Revenue Impact of AI Product Recommendations in Ecommerce

There’s a moment every ecommerce merchant recognises: traffic is up, the product range is strong, but revenue just isn’t following. Visitors browse, hesitate, and leave. The gap between a visit and a purchase often comes down to one thing – relevance.

This is exactly where AI product recommendations are proving transformative. Rather than showing every visitor the same static homepage or category page, AI-powered recommendation engines surface the right products to the right person at precisely the right moment in their journey. The result isn’t just a better experience. It’s measurably better revenue.

In this guide, we break down how AI product recommendations actually drive ecommerce revenue growth, what the data tells us about conversion and AOV impact, and how brands are already seeing real results, including a 30% year-on-year sales increase for one of our own clients.

What Are AI Product Recommendations and How Do They Work?

At a basic level, a product recommendation engine analyses signals such as browsing behaviour, purchase history, search queries, product attributes, the behaviour of similar shoppers, and uses that data to predict what a given customer is most likely to want next.

Traditional recommendations were largely manual: merchandisers would hand-pick ‘you might also like’ collections or set up basic rules (e.g. ‘show products from the same category’). These approaches are rigid and don’t adapt to individual behaviour. AI-powered product recommendations are fundamentally different because they improve over time, personalising at a scale no human team could manage.

Modern recommendation systems typically use one of three approaches, or a combination of them:

•   Content-based filtering, which recommends products with similar attributes to those a shopper has already engaged with. Particularly effective for niche categories with rich product data.
•   Collaborative filtering, which predicts preferences based on the behaviour of shoppers with similar tastes. Powerful for cross-category discovery: a customer buying running shoes might be shown hydration products they hadn’t considered.
•   Hybrid filtering, an option that combines both approaches for highly nuanced, context-aware recommendations. Most leading AI recommendation tools, including CRO platforms like Visually, operate on this basis.

Why AI Recommendations Move the Commercial Needle

The commercial impact of AI-powered product recommendations is well-documented and the numbers are compelling. Research consistently shows that personalisation is no longer a nice-to-have: it’s a commercial expectation.

Impact on Conversion Rates

One of the most direct ways AI product recommendations increase ecommerce conversions is by reducing friction in the discovery process. When a shopper can immediately see products aligned with their intent, without having to navigate endless category pages, the path to purchase shortens significantly.

AI recommendations placed on product detail pages, in the cart, at checkout, and in post-purchase emails each address a different potential drop-off point. The cumulative effect on conversion rates can be substantial, particularly when recommendations are tested and refined through structured A/B experimentation rather than deployed as a static ‘set and forget’ feature.

Impact on Average Order Value (AOV)

Perhaps the most immediate revenue lever for ecommerce brands is average order value. ‘Frequently bought together’, ‘Complete the look’, and ‘Customers also bought’ modules, when powered by genuine AI rather than manual curation, surface genuinely complementary products at the moment a customer is already committed to purchasing.

This isn’t upselling for its own sake. It’s relevant, helpful merchandising that increases basket size while improving the shopping experience. When a customer buying organic pasta sauce is shown the organic pasta and the organic olive oil they’d naturally pair with it, the recommendation feels like service rather than sales pressure.

Impact on Customer Retention

Beyond the immediate transaction, AI-powered recommendations play a longer game. Shoppers who experience a consistently relevant, personalised journey are more likely to return, more likely to engage with email recommendations, and more likely to increase their spend over time. For DTC brands especially, where customer acquisition costs remain high, improving retention through better on-site personalisation is one of the highest-ROI investments available.

How We Helped Biona Achieve 30% Sales Growth

The most compelling evidence for AI product recommendations isn’t in aggregate industry statistics. It’s in the specific, measurable outcomes for real brands. One of the clearest examples in our own client work is Biona, the UK’s leading organic food brand.

Biona had a strong product range and a loyal customer base, but their direct-to-consumer channel wasn’t converting at the level their brand deserved. Traffic was growing, but the on-site experience wasn’t turning that interest into optimised revenue. Sound familiar?

We partnered with Biona to deliver a consultancy-led programme combining CRO strategy, Shopify development, and AI-powered testing via Visually, an AI CRO recommendation platform that runs structured experiments to identify and implement the highest-impact on-site changes. The approach was iterative and data-backed.

Where AI Recommendations Deliver the Greatest Impact on Your Site

Not all recommendation placements are equal. Understanding where and how to deploy AI product recommendations across your site is as important as the technology itself.

Homepage: Set the Personalisation Tone Early

For returning visitors, the homepage is an opportunity to surface recently viewed products, bestsellers in their preferred categories, or new arrivals relevant to their history. This immediately signals relevance and reduces time-to-product.

Product Detail Pages: The Highest-Value Real Estate

When a customer is already interested enough to view a product page, they’re primed for discovery. ‘You might also like’ and ‘Complete the set’ modules here are proven AOV drivers, especially when backed by genuine AI logic rather than manual editorial.

Cart and Checkou: Late-Stage Uplift

‘Frequently bought together’ modules in the cart are particularly effective because purchase intent is already confirmed. A well-placed, genuinely relevant add-on at this stage (perhaps a complementary product that lifts the basket above a free shipping threshold) can feel like a service to the customer rather than a commercial nudge.

Post-Purchase and Email: Extending the Relationship

AI product recommendations don’t stop at checkout. Post-purchase emails, loyalty touchpoints, and browse abandonment flows that surface personalised products keep your brand relevant and drive repeat purchase – one of the most cost-effective revenue growth levers available to any DTC brand.

The Role of Testing: Why ‘Set and Forget’ Fails

One of the most important lessons from our work with Biona, and across our wider client portfolio, is that AI product recommendations only deliver their full revenue potential when they’re treated as a dynamic, testable programme rather than a one-time implementation.

This is where platforms like Visually add genuine strategic value. Rather than simply deploying recommendations and hoping for the best, they enable structured CRO experiments, testing different recommendation logic, placements, formats, and triggers. For Biona, this testing-led approach generated over £20,000 in measurable assisted revenue from CRO experiments alone.

What to Look for in an AI Recommendation Strategy

If you’re evaluating how to introduce or improve AI product recommendations on your ecommerce store, here are the key criteria that separate high-performing strategies from underwhelming ones:

•   Data quality and volume: AI recommendations are only as good as the data feeding them. Ensure your product catalogue, customer data, and behavioural tracking are clean and comprehensive.
•   Placement strategy: Consider the full customer journey, e.g. the homepage, PDP, cart, post-purchase, and deploy recommendations at each stage with appropriate logic.
•   Testing infrastructure: Commit to A/B testing recommendation variants. What works for one brand’s audience may not work for another.
•   Measurement framework: Define upfront which metrics matter, e.g. AOV, conversion rate, revenue per session, and track them rigorously. Don’t rely on vanity metrics like click-through rate in isolation.
•   Partner expertise: The technology is only part of the picture. Working with a team of experts that understands both the AI capability and the ecommerce commercial context ensures that recommendations are strategically deployed and not just technically implemented.

Key Takeaway

AI product recommendations are one of the most proven and commercially substantive tools available to ecommerce brands today. When implemented strategically with the right data, placement, testing, and expertise, they increase ecommerce conversions, lift average order values, and build the kind of personalised experiences that keep customers coming back.

If your ecommerce store isn’t yet leveraging AI-powered product recommendations, or if your current approach isn’t delivering measurable revenue impact, this is the moment to act.