The Art (and Science) of Smart Product Recommendations

I think we’ve all had that moment when you casually mention a product in conversation, and the next thing you know, an ad for it pops up on your phone. 

It’s almost too perfect, like the internet is one step ahead of you.

That’s the feeling a great product recommendation engine creates for your customers.

But instead of theories about your phone spying on you, it’s rooted in analyzing browsing habits, past purchases, and real-time behavior.

It makes shopping feel seamless like the brand already knows what they want before they do.

When done well, product recommendations boost AOV and make the entire shopping experience smoother, more intuitive, and helpful.

Yet most brands still get this wrong. 

Instead of making suggestions feel personalized, they throw out random products, push irrelevant items, or rely on lazy algorithms that share the same bestsellers to everyone.

We’re breaking down exactly what makes a high-performing recommendation engine and how you can use it to create a more personalized, conversion-driving shopping experience.

Let’s get into it:

The Backbone of a Great Recommendation Engine

At its core, a recommendation engine is just machine learning at work. 

Some call it AI, but these systems have been around for years. 

And they've gotten really good in the last 3-4 years.

They rely on user behavior. What people click on, how long they browse, and what they add to their cart to make smarter, real-time recommendations.

What should your recommendation engine be looking at?

• Site activity: What PDPs (Product Detail Pages) did they visit? What categories are they browsing?

• Brand preferences: If your site has multiple brands, are they gravitating towards specific ones?

• Attributes that matter: Size, color, features, price range, and even use cases.

• Engagement signals: Did they spend extra time on a product page? Add something to their cart but not check out?

The more behavioral cues your system picks up on, the smarter your recommendations will be.

Context Matters More Than You Think

A great recommendation engine shows customers exactly what they need right when they need it.

Here’s what a smart recommendation engine considers:

1. New vs. Returning Customers: 

• If they’re new, show trending and top-rated products. 

• If they’re returning, make it personal. 

• Surface items they’ve browsed before or purchased in the past.

2. Repeat Purchase Patterns 

• If a customer buys a consumable every 30-45 days, make sure you’re reminding them right before they need to restock.

3. Search Behavior 

• If they’re searching for a product you don’t carry, don’t leave them hanging.

• Suggest close alternatives or bestsellers in that category.

4. Geolocation & Weather

• A customer in Chicago in January doesn’t need the same recommendations as someone in Miami. 

• Adjust your product suggestions based on real-world seasonal shifts.

5. Device Type 

• Some products convert better on mobile than on desktop. 

• If you notice trends in your data, tailor recommendations accordingly.

For example: 

We worked with a men’s dresswear brand that dynamically adjusted recommendations based on seasons. 

Customers in the South started seeing polos and lightweight shirts as early as March, while those in colder states still saw sweaters and coats. 

As summer approached, recommendations shifted in waves, ensuring that customers always saw seasonally relevant products based on where they lived.

Where You Show Recommendations Matters

Product recommendations shouldn’t just be thrown onto a page. 

The placement of these suggestions directly impacts whether customers actually engage with them.

Best places to insert recommendations:

• Homepage: Feature bestsellers, trending items, and personalized picks based on past behavior.

• Category Pages: Don’t just dump customers onto a page sorted by “Best Selling.” Add a top-row carousel with the most popular products in that category.

• PDPs (Product Detail Pages): Suggest alternatives, bundles, or complementary products (if they’re buying a bicycle, show helmets, accessories, and repair kits).

• Cart Page: Carefully test if adding product recommendations here helps or distracts from checkout. For some brands, it increases AOV. For others, it creates friction.

• Search Results & Empty Cart Pages: If they search for something you don’t carry, don’t show an empty results page. Try suggesting related products instead.

One super overlooked placement is Google Shopping visitors landing on a PDP. 

If a visitor lands on a product page from a Google Shopping ad directly to a product page, consider putting a carousel of relevant recommendations at the top to encourage further browsing.

Algorithms That Work

If you’re wondering how most recommendation engines work, here are the core models:

Customers Also Bought (You Might Also Like…)

• Looks at what others purchased alongside the product in their cart.

Recently Viewed 

• Surfaces the last 5+ products a user looked at for easy reference.

Best Sellers by Location 

• Adjusts product displays based on regional demand.

AI-Driven Personalization 

• Uses past behavior, search history, and product clicks to dynamically adjust recommendations in real-time.

For example: 

If a user clicks on a blue shirt but doesn’t add it to the cart, a smart recommendation engine might start showing other blue products or similar styles across different brands

Biggest Challenges in Personalized Product Recommendations (And How to Fix Them)

Personalized recommendations can boost conversions. IF done right. 

However, many brands struggle with execution. Here are three key challenges and how to overcome them.

1. Choosing the Right Tool & Keeping It Updated

Not all recommendation engines work the same way. Shopify users have built-in options, but custom platforms require product feeds that update in real time.

The Fix:

Make sure your recommendation tool updates multiple times a day so customers don’t see out-of-stock products. 

Start with Shopify’s native tools or test third-party options before overcomplicating things.

2. Too Much, Too Soon

Brands often try to personalize everything at once, leading to poor execution. Overloading the site with recommendations can confuse shoppers instead of guiding them.

The Fix: Start small. Focus on high-impact areas:

1. Homepage: Show bestsellers & trending items.

2. PDPs: Suggest similar and/or complementary products.

3. Cart Page: Add subtle upsells but test if they help or hurt conversions.

Once these are optimized, then layer in advanced recommendations like behavioral-based or seasonal suggestions.

3. Poor UI & Customer Trust Issues

A recommendation engine only works if customers trust it. 

If recommendations feel random, overly pushy, or misleading (like only showing high-priced items), shoppers tune out.

The Fix:

Transparency 

• If suggestions are based on past purchases, say so (“Because you bought X, you might like Y”).

Optimize UI 

• Show star ratings, color options, and quick view modals to improve engagement.

Use clear section titles

• If the carousel says “Best Sellers,” make sure it actually features top-selling products.

Smart recommendations feel natural, not forced. 

Start simple, refine as you go, and always test what works.

To Conclude:

Product recommendations have the power to transform the shopping experience when they’re done right. 

They should feel seamless, intuitive, and personal, not like a random assortment of products thrown together.

The key is strategy over volume. 

Too many brands overload their sites with recommendations that don’t actually guide the customer in a meaningful way. 

Instead, the focus should be on where, when, and how suggestions are made.

As brands refine their recommendation strategies, the goal should always be the same: 

Make it easier for customers to find what they need right when they need it.

Looking forward,

Brian

P.S. If you’re looking to leverage product recs to improve your revenue but you don’t know where to begin or don’t have the team to support it, give us a shout. We can help you determine the best path forward and start increasing your revenue.