19 Product Recommendation Strategies & Examples

Customers don’t leave because they’re not interested. They leave because they’re overwhelmed. Too many options, not enough direction, and before they’ve even made it to the cart, they’re gone.

That’s where product recommendations come in. Not as decoration, but as quiet salespeople, guiding, narrowing, nudging.

This article rounds up specific, no-fluff strategies for using product recommendations across every part of your store—from homepage to post-purchase—to turn browsers into buyers and buyers into bigger spenders.

What is a product recommendation?

In ecommerce, a product recommendation is an automated suggestion that directs shoppers toward items they’re likely to buy, based on their actions, preferences, or what others with similar user behavior have done.

At their core, they’re algorithms doing the work of a smart, observant salesperson, minus the awkward small talk.

Let’s say someone’s browsing running shoes on your site. A solid recommendation engine might nudge them toward moisture-wicking socks, a high-traction sole cleaner, or even a GPS watch, depending on what’s relevant.

These suggestions don’t come out of thin air. They’re pulled from browsing history, search patterns, previous purchases, and sometimes real-time activity. All of it is stitched together to create what looks and feels like a thoughtful, helpful suggestion, rather than a random push to buy more stuff.

In essence, product recommendations are one of the most practical ways an online store can take a customer-based approach. Rather than offering a static, one-size-fits-all catalog, they allow you to turn your website into a dynamic, responsive storefront that adapts to the person clicking through it.

And the goal? Simple: drive conversion rates higher by delivering a better customer experience. Show the right thing to the right person at the right time, and watch customer engagement rise, along with your online store sales!

Product recommendations aren’t about pushing more; they’re about showing better. Done right, they feel less like upselling and more like reading the room.

9 types of product recommendations

Now that we’ve got a clear understanding of what product recommendations are (and why they matter), it’s time to talk about the many ways they can be put to work. Because not all recommendation engines are built the same.

The type of recommendations you implement depends on what you’re trying to achieve: boost conversion rates, increase average order value, improve customer engagement, or just make the whole experience smoother.

But it doesn’t stop there. Your store’s layout, tech stack, available visitor data (like cookie IDs or IP addresses), your ecommerce platform’s capabilities, and the quality of your customer profile data all play a role in determining which recommendation strategy actually fits.

A Shopify-powered DTC brand might not use the same system as a B2B store running on PWA Studio with a 10-year-old product catalog. And that’s fine. The good news is that there isn't a single “correct” model, just the one that fits your needs best.

Traditional vs AI-powered product recommendations

Before exploring specific types, it’s essential to distinguish between traditional and AI-powered approaches.

• Traditional recommendations rely on manual rules or simple logic, such as showing bestsellers, trending items, or products often bought together. These setups are predictable, easy to implement, and work well for smaller stores or limited product ranges.
• AI-powered recommendations, on the other hand, use machine-learning algorithms to analyze customer behavior, purchase histories, and contextual signals. These systems adjust in real-time, constantly learning and refining what they show. As a result, your website visitors get laser-specific personalized recommendations.

Now, let’s look at the specific product recommendation types and their pros and cons for ecommerce stores.

Collaborative filtering

Collaborative filtering is one of the most well-known approaches used by recommendation engines. It works by identifying patterns in behavior (either between users or between products) to recommend items based on similarity.

• User-based collaborative filtering: This model recommends products that other users with similar behavior have liked or purchased. For example, “People like you also bought…”
• Item-based collaborative filtering: Here, the system recommends items that are similar to what the current user has engaged with or purchased. For example, “Customers who bought X also bought Y.”

Content-based filtering

Content-based filtering recommends items similar to what the user has shown interest in, based on product attributes like category, brand, price range, or material. It doesn’t rely on what others have done; it looks at the individual customer’s interactions.

For example, if a user frequently buys minimalist sneakers, the system will prioritize similar styles (even if no one else is buying them).

You’ll often see content-based filtering used alongside collaborative filtering in hybrid models to balance out their blind spots.

Popularity-based recommendations

This method doesn’t aim to create a personalized experience. Instead, it puts the spotlight on what’s trending: bestsellers, top-rated items, or what’s currently hot in a specific region.

For example,“Top 10 in gadgets this month.”

It’s a solid fallback for first-time or anonymous users when there’s no behavioral data to lean on.

Hybrid recommendations

Hybrid models combine multiple recommendation methods to increase accuracy and coverage.

Think collaborative filtering + content-based filtering + contextual signals.

Or any blend that makes sense based on what data you have and who you’re targeting.

A great example of hybrid recommendations would be Netflix that blends machine learning from user behavior with metadata like genre and actor tags to recommend movies.

Context-aware recommendations

This strategy takes time, location, device type, and even weather into account when recommending products. For example, if it’s raining in New York, a customer might see umbrellas and waterproof boots; in Arizona, it’s sunglasses and SPF.

It’s especially useful for mobile-first or location-based ecommerce experiences.

Demographic-based recommendations

If you're working with thin behavioral data or onboarding new users, demographic-based recommendations might be your best bet.

This model relies on demographic attributes, such as age, gender, or location, to recommend products. Within this model, you'd be, for example, recommending skincare to users aged 25–35 in urban areas.

Rule-based (manual) recommendations

Rule-based recommendations rely on predefined logic set by marketers, merchandisers, or ecommerce managers. These are classic “if-this-then-that” setups used to manually decide which products should be shown in specific scenarios.

The rules can be as simple as cart triggers or as specific as showing certain products based on the category, price range, or brand of the item being viewed.

For example, if a shopper adds a gaming console to their cart, the system might recommend a compatible controller, a game, and maybe even a charging dock.

As rule-based recommendations don’t rely on complex machine learning algorithms or user behavior, but simply follow the playbook you write, they’re extremely easy to control and perfect for tactical cross-sells, promotions, and bundle offers.

Visual/Similarity recommendations

Powered by computer vision, this method finds and recommends items that look similar. For example, clicking on a beige linen shirt leads to a feed of similar shirts in terms of cut, color, and fabric.

This approach is particularly powerful in visually-driven verticals like fashion, furniture, or design.

Search-based recommendations

Search-based recommendations enhance the product discovery experience by making search smarter, faster, and more relevant. Often integrated into a store’s search & discovery app, this approach tailors product suggestions in real-time based on what the user is typing, what they’ve viewed, or what’s currently trending.

It goes beyond basic keyword matching. Features like autocomplete, dynamic filters, and AI-enhanced ranking use prior sessions, purchase histories, and even location data to recommend items that are more likely to convert.

For example, a user typing “headphones” might instantly see models they previously browsed, similar styles with better reviews, or popular picks in their region, all before finishing the word.

Should your ecommerce store care about product recommendations?

Absolutely. And not just in a “sure, sounds nice” way. Product recommendations have become one of the most practical, high-impact tools ecommerce businesses can use to influence customer behavior, and the results aren’t subtle!

The wide range of recommendation types (collaborative filtering, content-based filtering, rule-based logic, and more) gives brands the flexibility to target different goals.

Whether you're trying to lift average order value, reduce cart abandonment, boost engagement, or just make the shopping journey smoother, there’s a recommendation strategy that fits.

Here are the most notable benefits that product recommendations bring to the table:

• Increase average order value without discounts: When done right, product recommendations encourage shoppers to add more without feeling pushed. Whether it’s a “frequently bought together” bundle or an upsell that actually makes sense, 54% of retailers say recommendations are their main lever for lifting AOV.
• Increase the revenue of your ecommerce store: According to Barilliance, up to 31% of ecommerce revenue comes from recommended products. Their clients saw an average of 12% of sales directly tied to their recommendation engine.
• Improve conversion rates: Shoppers who don’t engage with recommendations convert at about 1.02%. That number jumps to 3.95% after a single interaction. That’s a 288% increase after one click. Not only do recommendations improve discoverability, but they also help customers make decisions faster, which is exactly what your conversion rate needs.
• Recover revenue from abandoned shopping carts: Product recommendations can reduce cart abandonment by up to 4.35% as they surface add-ons and alternatives that keep the customer moving forward instead of backing out.
• Encourage smart impulse buys:49% of consumers admit they’ve bought something they didn’t plan to, all thanks to a personalized recommendation.

19 best product recommendation strategies (+ Examples)

So, you’re sold on the value of product recommendations. Great! But knowing they work and actually making them work are two different things. That’s where strategy comes in.

To get the full return on your recommendation engine, you need more than a plug-and-play widget buried in your product pages. You need a mapped-out plan for where, when, and how to recommend products across the entire customer journey. Homepage to checkout. Product page to post-purchase email.

Here’s the catch: most ecommerce stores are barely scratching the surface. According to a study by Nosto, roughly 81% of shoppers didn’t see any product recommendations on product listing pages, like search results or category views. Which is wild, considering those are prime conversion moments.

So if you want to capture more sales, increase average order value, and create a shopping experience that doesn’t feel like a dead-end maze, you’ve got to think beyond a single page. Smart product recommendation strategies are built into every step of the journey and tailored to how, when, and why your customers shop.

Here are the ones worth using (with real-world examples).

Homepage product recommendation strategies

The homepage is often where the journey begins. It's your storefront window, and in ecommerce, it’s also your first chance to guide a visitor toward something they’ll want to buy. That’s why product recommendations shouldn’t wait until someone lands on a product page. They should start right here.

Here’s how to make your homepage work harder with strategic, targeted recommendations.

Feature best sellers

Best sellers give new visitors an easy place to start. You’re showing them what’s already popular, which builds trust and reduces choice paralysis.

Create a "Featured Products" section on your homepage that dynamically updates based on what’s performing well in your store.

Bestsellers is the first featured collection on the Rare Beauty homepage

To make it more effective:

• Break it down by category if you have a wide range of items.
• Label items with social proof like “Most Bought This Week” or “#1 in Skincare.”
• Use real-time sales data to keep this section fresh and accurate.

NextEvo breaks recommended product down by category

Even if someone’s not sure what they’re looking for, featured products give them a low-effort entry point into your catalog.

Introduce new arrivals early in the customer journey

If you’re launching new items, your homepage is the place to showcase them. Don’t bury your "New In" section two clicks deep; put it front and center.

New items are prominently featured as product recommendations on the homepage of Super Smalls

This helps loyal customers spot fresh inventory quickly and signals that your store is active and up to date.

Actionable ways to do it:

• Create a “Just Dropped” banner above the fold.
• Add a dedicated homepage block that pulls in products tagged as “New.”
• Consider filtering by category if you have frequent new drops (e.g. “New in Accessories”).

Promoting new arrivals early makes your store feel alive—and gives customers a reason to come back regularly.

Direct visitors towards discounts and deals

If you're running any kind of sale, make sure it’s visible without requiring a single click.

Argos displays daily offers and the corresponding product recommendations

Whether it's a flash deal, clearance event, or a weekly promo, your homepage should highlight it:

• Feature specific discounted products, like a “Deal of the Day.”
• Highlight entire sale categories (“Up to 50% Off Footwear”).
• Use urgency-based messaging, like “Ends Tonight” or “Limited Stock.”

Adidas immediately recommends products that are new to sale from their homepage

Don’t just announce a sale. Show discounted items directly so visitors can engage without having to dig for the savings.

Tip: Learn more about limited-time offers and how to run such campaigns effectively in our guide.

Highlight local or seasonal trending products

This tactic adds a layer of contextual relevance that most stores skip. Use a personalization engine to surface products based on location, season, or regional trends.

For example, you can use this tactic to be…

• Showing winter jackets to users in colder climates while surfacing swimsuits for those in warmer areas.
• Highlighting rain gear in rainy regions or back-to-school items during the late summer months.

This type of targeting works especially well when layered with customer behavior, making your homepage feel tailored rather than generic.

Victoria’s Secret shows trending items as product recommendations on the homepage

Inspire purchases with handpicked collections

Give your homepage a human touch with curated collections. These can be editor’s picks, staff favorites, influencer-curated bundles, or expert-approved lists. The point is to add authority and reduce decision fatigue.

Clark’s Botanicals website homepage features a collection of product recommendations curated by the editor

To do it well:

• Label clearly: “Our Team’s Favorites” or “Curated by [Influencer Name].”
• Focus on a theme: minimalist must-haves, weekend essentials, office upgrade kits, etc.
• Keep it tight and avoid dumping in 50 products. The goal is inspiration, not overwhelm.

Product recommendations curated by Kylie Jenner herself on the homepage of the Kylie Cosmetics website

These collections not only guide purchases but also increase perceived brand value and trust.

Create a personalized experience for returning visitors

Returning customers should never see the same homepage twice. Use browsing history, cart activity, or previous purchases to tailor homepage product blocks specifically for them.

For example, you can show “Recently Viewed” items in a dedicated section or recommend products related to what they’ve browsed before using content-based filtering.

Alternatively, you may surface back-in-stock alerts for items they checked out earlier but couldn’t buy.

Sephora offers its returning visitors product recommendations based on previous searches

Even small touches like these can lift conversions and increase session duration—because the store now feels familiar, not static.

Recommend items based on quiz answers

Davines prompts its users to take a quiz to get personalized product recommendations

Product quizzes are a smart way to collect zero-party data—information customers willingly give you. When used right, they’re not just an engagement tool, but a recommendation engine in disguise.

Here’s how to make it work:

1. Place a quiz CTA prominently on your homepage: “Not sure what to get? Take the quiz.”
2. Ask relevant, low-friction questions about preferences, goals, or style (e.g. skin type, budget, occasion, color choices).
3. Use the responses to generate tailored product suggestions instantly.

For example, a skincare brand could recommend a routine based on skin concerns and ingredients the customer selects. A fashion store might offer outfit ideas based on fit preferences or style types.

The key is to keep it short, make the results feel personalized, and connect the outcome directly to add-to-cart actions.

Davines encourages quiz takes to add all the recommended products to the shopping cart

When done well, quizzes turn passive visitors into engaged, informed shoppers without needing a ton of historical data.

Tip: Learn more about quiz funnels and how to use them in ecommerce marketing in our guide.

Product page recommendation strategies

Once a shopper hits the product page, you’ve got their attention. They’ve shown intent. They’re interested in something. Now’s your moment to offer more without being pushy. The product page is where strategic recommendations do the heaviest lifting: helping customers discover better options, increase cart value, and make confident decisions without bouncing.

Help shoppers discover alternatives they'll love

Sometimes the product someone clicks on isn’t exactly what they want, it’s just close. Show them similar options so they can pivot without abandoning the page.

This strategy works especially well when:

• You offer multiple price tiers of a product (budget to premium).
• You have variants with slightly different features, colors, or styles.
• You're trying to minimize decision fatigue by offering refined, relevant alternatives.

Label this section clearly: “Similar Styles,” “Compare Alternatives,” or “More Like This.” The key is to let shoppers explore without starting over.

Sephora takes it a step further, combining product recommendation with a comparison table

Surface relevant picks with contextual suggestions

Urban Outfitters pulls up relevant products in the “You may also like” section

The generic “You may also like” widget is everywhere, but you can do better by tailoring it. Instead of tossing in random popular products, curate this section based on browsing behavior, product attributes, or brand affinity.

To make this section work as intended, use content-based filtering to recommend items with similar attributes: color, brand, category, or material. Filter results based on what other shoppers have shown interest in after viewing the same item.

Charlotte Tilbury takes recently viewed items in consideration when recommending the products the shopper might also like

Tip: Keep it clean. Show 3–5 alternatives max, and make sure they don’t feel redundant with what's already on the page.

Boost cross-sells with social proof

When you're unsure what to buy, seeing what others chose can be oddly reassuring. That's exactly why “People Also Bought” works: it taps into real behavior to guide new decisions. It’s less about data, more about giving your customers a sense that they’re on the right track.

This approach is especially useful for practical or multi-component products—think DSLR cameras with SD cards, or haircare routines where shampoo, conditioner, and serum all play a part. Done well, it feels like helpful advice, not a sales push.

ASOS adds a layer of social proof to product recommendations with the “People also bought” section

Encourage add-ons with “complete the look” suggestions

When a product is part of a larger lifestyle (like an outfit, a room setup, or a gear collection) don’t leave the rest to the shopper’s imagination. Curate the look for them, show how pieces work together, and make it easy to shop them all in one go.

This goes beyond simple cross-selling. You're giving context. A blazer suddenly feels more wearable when paired with the right trousers, heels, and bag. A sofa becomes more compelling when styled with a rug, coffee table, and throw pillows that match its tone and size.

To do this effectively:

• Use lifestyle imagery that shows the entire ensemble or space in use.
• Focus on items that visually and stylistically complement each other, not just those in the same category.

Meshki invites shoppers to complete the look with matching items

Some brands take this concept further. Take Meshki, for example. Not only do they show “Complete the Look” product suggestions, but they also offer an Outfit Creator tool that lets shoppers build and compare looks directly on the site.

It’s simple, visual, and practical—exactly the kind of tool that nudges hesitant buyers over the line while boosting AOV in the process.

Interactive Outfit Creator on the Meshki website helps to visualize the look

Offer discounted bundles

Bundling only works when it removes friction, not adds to it. That means grouping products that naturally belong together, not forcing a set for the sake of it. Use real purchase data to spot which items are often bought in tandem, then build out ready-to-go combos that feel intentional.

A small discount helps, but clarity matters more. Show both the original and bundle price so the shopper doesn’t need to do math in their head. If the perceived value is clear, they won’t hesitate.

Framing matters too. Call it a “Complete Kit,” “Essential Set,” or “Bestselling Pair.” Give it a reason to exist. And if there’s time sensitivity (limited stock, seasonal relevance, or an expiring offer), say so, directly.

Recommend recently viewed or previously saved items

Sometimes, you don’t need an elaborate recommendation to move the needle. All it takes is a reminder. If a shopper’s already shown interest in a product, surfacing it again can be just enough to get them over the line.

A “Recently Viewed” section placed near the bottom of the product page works well here. Keep it simple: a few clean thumbnails, product names, and maybe a quick link back to the original listing. For users who’ve saved items to a wishlist or browsing history, consider adding a note like “Now back in stock” or “Price dropped since your last visit.”

Product recommendations can be as simple as reminding the shopper what they’ve already checked out, like how Princess Polly does it

This approach is especially effective for high-consideration products, items that aren’t bought impulsively and might require a second or third look. It’s low effort, low pressure, and a smart way to keep interested shoppers engaged without interrupting their flow.

Set up exit-intent recommendation popups

Exit-intent popups can be obnoxious—or incredibly useful. It all depends on timing and relevance.

To make them work:

• Trigger only on true exit signals (cursor to close/tab).
• Recommend either the same item with an incentive, or a closely related product.
• Keep the design clean and the CTA focused (“Before You Go—Take a Look at This”).

When paired with behavior-based data, this strategy can recover otherwise lost sessions and spark second thoughts in the best way.

If you’re looking for a simple way to set this up, Personizely offers a flexible widget builder that lets you create dynamic, personalized popups based on session data, cart content, location, and more; no dev work required. It’s a fast way to test offers, tweak messaging, and recover sessions without being aggressive.

Checkout page recommendation strategies

By the time someone hits your checkout page, they’ve already made a buying decision. The hard part is done. Now it’s about increasing the value of that order without creating friction or distraction.

Product recommendations here should feel like helpful suggestions, not like the store’s trying to squeeze more out of them at the last second.

Nudge with a free shipping progress bar and add-ons

Few things motivate shoppers like the promise of free shipping. Pairing a dynamic progress bar with smart product recommendations gives them an easy reason to add just a little more.

How to use it:

• Show the current cart value and how much more they need to unlock free shipping (“You’re $8 away from free delivery”).
• Right below, recommend low-cost, relevant items that would help them cross the threshold.
• Make it seamless to add with a single click: no redirection, no page reload.

Tip: Use items that historically convert well as add-ons. Accessories, consumables, or small lifestyle items tend to work best.

Oh Polly pairs a free shipping progress bar with bestsellers product recommendations

Trigger a cart popup with smart suggestions

Instead of waiting for the checkout page to load, you can insert a strategic recommendation moment right after an item is added to the cart. A slide-out side cart or mini-popup is the perfect place to surface related products, gently encouraging shoppers to keep exploring.

This works particularly well when paired with products that require accessories, refills, or maintenance (think razors with cartridges or planners with inserts).

Add a “Recommended for You” block at checkout

While checkout isn’t the place for a full product grid, a small, personalized module based on the customer’s behavior or purchase history can feel helpful, not pushy.

To keep it effective:

• Show 1–2 personalized picks, not a carousel of random products.
• Use clear headlines like “Before You Finish…” or “You Might Still Be Interested In…”
• Only surface items that are low-risk: inexpensive, highly rated, or commonly added at the last minute.

BeardBrand checkout page features a minimalistic product recommendations widget

This approach works best with a strong personalization engine behind it, drawing from browsing data, past orders, or cart contents to recommend truly relevant items.

Post-purchase page and email recommendation campaigns

Just because a customer hits “Confirm Order” doesn’t mean your chance to increase order value is gone. With the right timing and a seamless experience (no extra forms, no duplicate shipping fees) you can continue to drive conversions after checkout without feeling pushy.

Offer limited-time post-purchase add-ons

Right after the order is placed, on the confirmation page or in a follow-up email, is a prime moment to offer one-click extras. The customer’s already in buying mode, and you’ve earned enough trust to suggest something else that actually makes sense.

Frame it with urgency, but make it helpful. “Want to add this before we pack your order?” works far better than a generic upsell pitch. And above all, keep the process frictionless: no new checkout, no shipping delays, just a single button that adds it to the existing order.

Recommend replenishment products via targeted email

Consumables, self-care items, pet supplies—anything that runs out eventually creates a second window for a sale. Use email to meet customers right at that moment.

Chewy sends targeted product recommendations in a “Running low?” email campaign; Source

Set up a campaign that times perfectly with their typical usage cycle. For example:

• “Time to restock your moisturizer?” sent 30 days after delivery.
• “Running low on supplements?” triggered based on serving size and quantity ordered.

Make the CTA direct. “Reorder with one click” is more effective than a vague “See more.” The goal is to remove every possible barrier between the reminder and the repeat purchase.

Deliver personalized shopping experiences that boost average order value

The right product recommendations do more than fill empty space on your website, they guide shoppers, influence decisions, and quietly increase your revenue without adding pressure. From homepage carousels and bundled offers to post-purchase nudges and personalized emails, each touchpoint is a chance to move the needle on conversion, cart value, and customer satisfaction.

But none of it works without the right infrastructure. To deliver tailored, timely, and effective recommendations across the entire customer journey, you need a tool that’s built for flexibility, speed, and precision.

That’s where Personizely comes in.

Personizely is an all-in-one conversion rate optimization platform designed for ecommerce teams who want control without complexity. With its powerful widget builder, you can launch high-converting product recommendations without touching code. Its built-in website personalization engine lets you tailor the onsite experience based on visitor data, behavior, location, and more. And with integrated A/B testing, you can fine-tune everything, from headlines to product block, to find out what actually works.

It also plays well with others. Seamless integrations with major ecommerce platforms, email marketing tools, and analytics suites mean you don’t need to juggle five different apps to get results.

If you’re serious about turning product recommendations into performance drivers, give Personizely a try. It’s everything you need to create personalized shopping experiences that boost revenue!

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