Product Recommendations: Types, Benchmarks & Strategies [2026]
:quality(80))
:quality(80))
Read summarized version with
ChatGPT 
Perplexity 
Gemini 
Claude 
Grok Key takeaways
Product recommendations are automated product listings that suggest relevant items based on a shopper's browsing, purchase history, or overall popularity.
They appear in embeds, popups, and the onsite feed, and on homepages, product pages, and cart pages.
Two engines build them: rule-based (traditional) and AI-powered (self-learning), and AI adjusts its picks for each visitor in real time.
Common types include recently viewed, best sellers, frequently bought together, complete your look, and quiz-based picks.
Performance is judged on click rate and the share of clicks that lead to an order, not on impressions.
Choose a tool when you want recommendations live in hours, and build in-house only when you need full control of the algorithm.
Product recommendations suggest relevant items to each shopper, which raises revenue per visitor and brings people back to products they were considering.
This guide covers what product recommendations are, the main types, how rule-based and AI systems build them, how well they perform against real benchmark data, and how to add them to your store.
In this guide:
See your segments
Match each visitor to the right campaign
We review your traffic and show which behavioral segments you have, the campaigns that fit each one, and the revenue they could reach.
:quality(80))
What are product recommendations?
Product recommendations are automated product listings that suggest relevant items to a visitor. Fixed rules or a real-time algorithm choose the items, based on browsing behavior, purchase history, or overall popularity.
You will see them in embeds, popups, the onsite feed, lightboxes, and emails. The most common placements are homepages, product pages, and cart pages.
A related-products block on a product page looks like this:
:quality(80))
:quality(80))
Types of product recommendations
Most stores use a mix of the types below, matched to where a shopper is in their visit.
The common types and what each one does:
Type
What it does
Recently viewed
Remind shoppers of items they already looked at
Customer favorites
Highlight the best-rated products
Complete your look
Suggest pieces that finish an outfit
You might also like
Recommend items similar to the current one
Recommended accessories
Suggest compatible add-ons for the product
Products in context
Show items together in a real setting
Quiz-based picks
Recommend items from a shopper's quiz answers
Personalized bundles
Group items for a set bundle price
Similar products
Recommend close matches to the viewed item
People also bought
Show what other shoppers buy together
Use-case recommendations
Suggest products for a specific need
Checkout suggestions
Recommend add-ons at the cart or checkout
See how stores use each type: 20 product recommendation examples
How product recommendations are created
Recommendation systems read your store data, visitor activity, sales, and product attributes, then rank items for each shopper. Two approaches do this: rule-based and AI-powered.
Rule-based systems follow conditions you set, using collaborative filtering, content-based filtering, or a hybrid of both. AI systems read historical and live data and adjust their picks for each visitor without manual input.
How the two approaches compare:
Dimension
Traditional
AI-powered
Approach
Rule-based
Self-learning
Real-time changes
Static
Dynamic and adaptive
Personalization
Limited to preset rules
Scales to each visitor
Maintenance
Manual rule updates
Automated, little human input
Best for
People also bought, similar items, new arrivals
Bundles, use-case picks, you might also like
How well product recommendations perform
Two numbers decide how well product recommendations work: the share of shoppers who click one, and the share of those clicks that lead to an order.
A typical store sees about a 9% click rate on its product recommendations, and roughly 4% of those clicks turn into an order. Both figures rise when the recommendation matches what the shopper is looking for.
Click rate depends on the catalog and the quality of the match, so a range tells you more than a single number. Here is where stores land:
Product recommendation click rate across stores
Share of shoppers who click a recommendation, by store
Source: Wisepops. 155 recommendation campaigns across 43 stores, about 565,000 impressions. July 2026.
A click rate above 9% is doing well, and anything near 20% is strong. The clicks that count are the ones that end in a sale, so judge a recommendation on orders per click rather than clicks alone.
Individual brands land across this range. Pierre Hardy reached an 11.6% click rate, Maison Lejaby holds a 15% average, and at émoi émoi 11.4% of clicks lead to an order.
Product recommendation strategies
The best results come from matching the recommendation to where the shopper is, and from picking a channel that fits your brand.
Match the recommendation to visitor intent
Show best sellers to first-time visitors, since you have no history yet. Trigger frequently bought together the moment an item is in the cart. Bring recently viewed products back for returning visitors. Grouping shoppers by behavioral segments makes each of these calls easier.
Pierre Hardy runs all three in the onsite feed and reached an 11.6% click rate, most of it on mobile.
Best-seller recommendations in Pierre Hardy's feed:
:quality(80))
:quality(80))
Re-engage with recently viewed items
Recently viewed recommendations let shoppers pick up where they left off, which cuts the decision fatigue that comes from too many choices. The onsite feed shows them on autopilot, with no code.
émoi émoi reconnects shoppers with items they viewed and related products in the feed. 11.4% of shoppers who click those recommendations go on to order.
Recently viewed and related products in émoi émoi's feed:
:quality(80))
:quality(80))
Keep recommendations non-intrusive with the onsite feed
A feed the shopper opens by choice suits brands that do not want a popup blocking the page. Best sellers greet new visitors, and a matching piece meets returning ones.
Maison Lejaby has run this since 2021 at a 15% average click rate, higher on mobile than desktop. See how the onsite feed works.
Recommendations in Maison Lejaby's onsite feed:
:quality(80))
:quality(80))
Use popups and embeds at high-intent moments
Popups and embedded blocks put recommendations in front of a shopper at the cart or on exit, where intent is highest. A cart popup can suggest a complementary item, and an exit popup can show recently viewed picks before the visitor leaves.
Set one up with our guide to the recommendation popup.
A best-seller recommendation shown to shoppers browsing the same category:
:quality(80))
:quality(80))
Recommend complements, not random items
Suggestions that complement the product in view read as helpful, not pushy, and they raise average order value. Random picks do the opposite.
Complementary items suggested next to the product a shopper is viewing:
:quality(80))
:quality(80))
How to get started with product recommendations
You have two routes: use a tool or build your own. For most stores a tool is faster, since you can go live in hours with little or no code. Building in-house only pays off with engineering resources and a reason to own the algorithm.
Pick the path that fits your setup:
To compare options, read our guide to product recommendation software, or the head-to-head Wisepops vs Nosto.
For self-learning suggestions, see how to build them with AI.
On Shopify, follow our setup for recommendations on Shopify.
Building in-house means training a model on your catalog and behavior data, which large retailers do for full control, using collaborative filtering or a hosted service. Most teams get there faster with a tool.
Frequently asked questions
What is a good click rate for product recommendations?
A typical store sees around a 9% click rate on its product recommendations, with the lower end near 6.5% and the best performers above 20%. Around 4% of those clicks turn into an order. Rates vary by catalog, traffic quality, and how well the recommendation matches intent.
Do product recommendations increase sales?
Yes, when they match what a shopper is likely to want. Brands report meaningful revenue attribution from recommendations, for example 22% of online revenue at Pierre Hardy, and higher average order value when suggestions complement the product in view. The lift comes from relevance, not volume.
What is the difference between traditional and AI product recommendations?
Traditional systems follow fixed rules you set and stay static until you change them. AI systems read live and historical data and adjust their picks for each visitor on their own. AI scales personalization that rules cannot match.
Where should product recommendations appear?
The highest-intent placements are product pages, cart pages, and the homepage, plus the onsite feed for returning visitors. Match the placement to intent: best sellers on the homepage, complementary items at the cart, recently viewed in the feed.
How do I add product recommendations to Shopify?
Install an app that offers recommendations, connect your catalog, and choose the channel: popup, embed, or onsite feed. AI options run on autopilot once connected, so you can launch in hours without code.