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Recommendation Engine

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A recommendation engine is a system that suggests products or content to a shopper based on their browsing history, past purchases, or the behavior of similar users, aiming to surface items the shopper is more likely to buy.

Most recommendation engines rely on machine learning to identify patterns across large volumes of shopper data, though simpler versions can operate on fixed rules, such as showing items frequently bought together.

Recommendation engines differ from general AI product research tools, which help a store owner decide what to sell, since a recommendation engine instead decides what to show an existing shopper. In an online store, recommendation engines typically appear as “customers also bought” sections, homepage carousels, or personalized email content.

How a recommendation engine works

The system collects data on a shopper’s browsing history, past purchases, and on-site behavior.
It compares this data against patterns from other shoppers with similar behavior, or against relationships between products.
The engine ranks a set of candidate products by predicted relevance to the shopper.
The highest-ranked products are displayed in a designated area of the store, such as a product page or homepage.
The system tracks whether the shopper clicks or purchases the recommended items.
This outcome data feeds back into the model, refining future recommendations.

Example

A shopper on a home goods store views several ceramic vases and adds one to their cart. The store’s recommendation engine analyzes this browsing pattern and displays a “customers also bought” section showing matching coasters and a complementary flower arrangement. When the shopper later opens a marketing email from the store, the same recommendation engine selects which products to feature based on that browsing session.

Key characteristics

Data-dependent: Recommendation quality improves as more browsing and purchase data becomes available.
Multiple display formats: The same engine often powers on-site sections, email content, and search result ranking.
Two common approaches: Systems typically rely on either similarity between shoppers or similarity between products.
Continuously updated: Rankings shift as new browsing sessions and purchases are recorded.

Related terms

Machine learning in ecommerce – the technology most recommendation engines are built on.
AI product research – a related but distinct tool used to decide which products to sell, rather than which to show shoppers.
AI-powered ecommerce – the broader use of artificial intelligence across store operations, including recommendation engines.
Dropshipping – a retail model where recommendation engines are commonly used to increase average order value.

Frequently asked questions

Do small ecommerce stores use recommendation engines?

Many small stores use recommendation features built into their ecommerce platform rather than building a custom engine from scratch.

Can a recommendation engine work without much purchase history?

Accuracy is typically limited early on, since most engines rely on data from past behavior to identify meaningful patterns.

Are recommendation engines only used for product suggestions?

No, similar systems are also used to personalize search results, email content, and homepage layouts.

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FAQ

How much do recommendation engines increase sales?

Recommendation engines can increase sales by surfacing relevant products a shopper may not have found on their own. Some studies report that recommended products account for 10 to 30% of total ecommerce revenue on stores that use them. Average order value often rises when relevant add-on products are suggested at checkout. Click-through rates on recommendation sections are commonly between 5 and 15%.

Do recommendation engines require coding to set up?

Most store owners set up a recommendation engine through a plugin or built-in platform feature rather than writing code. Basic setup can often be completed in under 1 hour. Some platforms allow configuration through a simple dashboard with 3 to 5 settings. Custom-built engines typically require a developer and more setup time.

What data does a recommendation engine need?

A recommendation engine typically needs browsing history, purchase history, and product catalogue data to generate relevant suggestions. Engines often need at least a few hundred transactions before results become meaningfully personalized. Some systems also factor in time spent on a page, measured in seconds. Stores with fewer than 50 products often see limited variety in recommendations.

Can a recommendation engine work across email and website at once?

Yes, many recommendation engines sync data across a website, email platform, and sometimes ads, so the same suggestions appear in more than 1 channel. Cross-channel syncing typically happens within a few minutes to 24 hours. This consistency can reinforce a single browsing session across multiple touchpoints. Some tools support syncing across 3 or more channels at once.

How is a recommendation engine different from search?

Search returns results based on a specific query a shopper types, while a recommendation engine proactively suggests products without a search term. Search relies on keyword matching in most cases, while recommendations rely on behavioral patterns. The 2 systems are often used together on the same product page. A single store may combine both to cover roughly 100% of discovery paths.