Shopify AI

How to Use AI for Product Recommendations on Shopify

By Angela Ranjeet
April 14, 2026

Most Shopify stores do not have a traffic problem first. They have a decision problem.

A visitor lands on the site, looks at a few products, and then gets stuck. They are not sure which item fits their need, which version is right, what goes well together, or whether they should buy now or keep browsing. Good product recommendations reduce that friction. AI makes those recommendations more relevant, faster, and easier to scale.

On Shopify, this work can start with native tools and then move into more advanced setups if the catalog, traffic, or complexity demands it. Shopify already supports related products, complementary products, predictive search, search tuning, filters, and custom storefront APIs. That means a store can build a strong recommendation system without guessing from scratch. Shopify also lets you extend product data with metafields and metaobjects, which becomes important when you want AI to understand fit, compatibility, ingredients, use case, style, or any other detail that is not captured in the default product fields.

What product recommendations actually do

Product recommendations are not just a row of random products under a product page.

A good recommendation system helps a customer move forward. That can mean one of several things:

It helps them find a similar item if the current one is not quite right.
It helps them add a useful second item that goes well with the first.
It helps them narrow choices based on need, budget, style, or fit.
It helps them recover from poor search wording.
It helps them discover products they would not have found on their own.

That is why recommendations should not be limited to one place on the site. They belong on product pages, collection pages, search results, predictive search, cart, and in some stores even post purchase journeys. Shopify’s product recommendation tools distinguish between related products and complementary products. Related recommendations are auto generated by Shopify. Complementary recommendations are usually set manually in the Shopify Search and Discovery app. Shopify also lets developers query recommendations directly through the Storefront API and the Ajax Product Recommendations API.

Start with the recommendation job, not the AI label

Before you add any app or model, decide what job the recommendation system needs to do.

For example:

A fashion store may need style based recommendations, size aware alternatives, and matching items.
A skincare store may need routine building, concern based matching, and compatibility between products.
An electronics store may need accessory recommendations, compatibility checks, and upgrade suggestions.
A gift store may need recommendations by budget, recipient, and occasion.

If you skip this step, the system usually turns into a generic you may also like widget that does very little. AI works best when you define the recommendation intent clearly.

In practice, most Shopify stores need four recommendation types:

1. Similar alternatives

These help when the shopper likes the product category but not the exact item.

Example: A visitor views a blue backpack. The system recommends other backpacks at a similar price, size, or style.

2. Complementary products

These help increase basket size by showing products that fit naturally with the current item.

Example: A customer views a camera. The system recommends a memory card, case, and tripod.

Shopify supports complementary recommendations directly, and merchants can configure them in Search and Discovery.

3. Need based recommendations

These help a shopper who knows the problem but not the product.

Example: I need a light moisturizer for oily skin.

This is where AI adds the most value, because it can interpret intent instead of waiting for exact keyword matches. Shopify’s search controls now include natural language and semantic understanding in supported setups, which can improve discovery even when the search terms do not match the product copy exactly.

4. Search recovery recommendations

These help when the shopper searches poorly.

Example: They search for christmas party shoes even though your product titles do not use those exact words. Shopify documents that semantic search can associate related words, concepts, categories, colors, and other product attributes to improve search relevance when enabled and supported by the store’s plan and setup.

The foundation is product data, not the model

If the underlying product data is weak, recommendation quality will also be weak.

This is the most common reason product recommendation projects underperform. Store owners often expect AI to figure it out from thin product titles and short descriptions. That usually fails.

For AI recommendations to work well, each product page should answer the questions a real shopper would ask:

What is this product?
Who is it for?
When should someone use it?
What are the key features?
What problem does it solve?
How does it compare to similar items?
What does it pair with?
What are the important attributes such as material, size, fit, skin type, voltage, compatibility, or ingredients?

Shopify’s metafields and metaobjects are built for exactly this kind of custom data. Metafields add specific custom fields to products and other resources. Metaobjects let you create richer, reusable content structures with multiple related fields. Shopify also supports querying with metafields so custom data can become searchable and filterable.

A practical data model for recommendations

If you want better AI recommendations, enrich products with fields such as:

primary use case
target customer
style or aesthetic
material
fit
compatibility
concern addressed
budget tier
premium or entry level position
season or occasion
items that pair well
substitute products
attach rate products
restrictions or exclusions

For a skincare store, that may be skin type, texture, fragrance free status, actives, routine step, and concern addressed.

For an apparel store, that may be fit, fabric weight, stretch, weather suitability, occasion, and style family.

For an electronics store, that may be connector type, supported devices, wattage, size class, and compatibility notes.

Without this layer, the AI mostly guesses from shallow text.

Use Shopify’s native tools first

A lot of stores should start with Shopify’s built in recommendation and discovery stack before moving to third party AI tooling.

1. Product recommendations

Shopify supports product recommendations in themes and APIs. Related recommendations are auto generated. Complementary recommendations can be configured in Search and Discovery. The Storefront API also supports an intent argument so developers can request related or complementary recommendations, and the result returns up to ten products.

This is the fastest place to start because it solves two common problems immediately:

Show me similar products
Show me products often bought together

2. Search and Discovery app

Shopify’s Search and Discovery app gives merchants control over search results, recommendations, filters, boosts, and synonym groups. Shopify’s docs also note that search can include natural language features, custom filters, predictive search, product boosts, and synonym groups.

This matters because recommendations do not live only inside a product page widget. Search is one of your biggest recommendation surfaces. If search is weak, customers never reach the right products in the first place.

3. Predictive search

Predictive search helps customers refine discovery as they type. Shopify supports predictive search for products, collections, pages, articles, and query suggestions, and the Search and Discovery app can customize its behavior.

For many stores, this is one of the easiest AI adjacent wins. It catches intent earlier than the search results page.

4. Filters and faceted browsing

Filters are not usually called AI, but they are essential for AI recommendations because they expose the structured attributes that help both humans and systems narrow the catalog correctly. Shopify supports filters for availability, price, and product attributes through Search and Discovery.

If a customer cannot filter by the core attributes that matter in your category, your recommendation layer has to work much harder.

Where AI adds value beyond native recommendations

Native Shopify recommendations are useful, but they are not enough for every store.

You should think about an AI layer when your store has one or more of these conditions:

a large catalog
many similar products
a need for comparisons
high variation by customer need
many pre purchase questions
strong upsell and cross sell opportunity
important compatibility logic
rich product data already available
search queries that are natural language, messy, or highly specific

In those situations, AI can do three things better than a fixed recommendation widget.

1. It can interpret shopper intent

A customer might ask:

I need a bag for short work trips
I want a gift under $100
Show me something similar but lighter
Which one is better for sensitive skin
I want the cheapest option that still feels premium

That is not just search. That is assisted decision making.

2. It can use more context

AI can combine:

current product
current cart
recent browsing
search query
product attributes
FAQs
policies
review themes
compatibility notes
merchant defined rules

The more context it uses, the better the recommendation quality becomes.

3. It can explain the recommendation

This is underrated.

A recommendation becomes more persuasive when the system explains why it chose the item.

Examples:

This item is a better fit because it is lighter and has a padded laptop sleeve.
This serum is better for sensitive skin because it avoids fragrance and strong exfoliants.
This cable works with your device because it supports the same connector and charging standard.

That explanation increases trust and helps the shopper decide faster.

A practical implementation plan

Here is the most effective way to roll this out on Shopify.

Step 1: Clean the catalog

Before touching AI, improve:

product titles
descriptions
category assignment
tags where still useful
product images
variant naming
attribute completeness
availability data
review collection

Also add missing custom data through metafields and metaobjects. If your catalog has gaps, fix those first. Shopify’s docs are clear that custom data can extend products and make custom attributes searchable and reusable.

Step 2: Configure related and complementary recommendations

In Search and Discovery, manually set complementary products for items that naturally pair together. Keep these curated, especially for high traffic products, high margin items, and hero SKUs. Shopify lets merchants set up to ten complementary products and customize related recommendations as well.

This usually gives quick wins without needing a full AI system.

Step 3: Improve search relevance

Use Search and Discovery to set:

synonym groups
product boosts
better result types
out of stock behavior
filters
semantic search if available and appropriate

Shopify notes that product boosts, synonym groups, natural language search, and semantic understanding can all affect discovery, while semantic search availability depends on store size, plan, locale, and other requirements.

This step matters because many recommendation sessions start with a search.

Step 4: Add AI recommendation logic

At this stage, you can decide between:

A recommendation app
A custom AI assistant
A hybrid approach

A hybrid approach is often best.

Use Shopify’s native related and complementary recommendations for stable merchandising logic. Then add AI for need based matching, conversational discovery, and explanation.

Step 5: Place recommendations where decisions happen

Do not dump recommendations only at the bottom of a product page.

Use them in these moments:

Product page

For similar products, alternatives, and bought together items.

Collection page

For best picks, popular choices, seasonal picks, and intent based sorting.

Predictive search

For fast discovery before the user even reaches a search page.

Search results page

For better ranking, recovery from weak queries, and guided filtering.

Cart

For accessories, refill products, bundles, and threshold based add ons.

Post purchase

For replenishment, refill cycles, matching products, and next best purchase.

The right placement often matters more than the algorithm.

Step 6: Measure commercial impact

Do not judge recommendations by clicks alone.

Track:

click through rate on recommendation blocks
add to cart rate from recommendation blocks
revenue per session
average order value
conversion rate
assisted conversions
attachment rate for complementary items
search exit rate
no result search rate
recommendation driven revenue by template

A recommendation system is doing its job if it helps customers decide faster and buy more confidently.

Common mistakes that hurt results

1. Recommending based only on popularity

Best sellers are useful, but they are not always relevant. Relevance beats popularity.

2. Showing too many options

More products do not always mean more sales. Too many choices can slow the shopper down.

3. Ignoring complementary logic

Many stores only think about substitutes. Complementary recommendations often drive basket growth faster.

4. Treating all products the same

High consideration categories need stronger recommendation logic than simple low price products.

5. Failing to explain why the product fits

Good recommendations are not only about ranking. They are also about trust.

6. Skipping data enrichment

If you do not structure your catalog properly, recommendation quality plateaus quickly.

When native Shopify is enough, and when it is not

Native Shopify recommendations are enough when:

the catalog is small to medium
products are easy to understand
related and complementary suggestions cover most needs
search behavior is simple
the store mainly wants better merchandising, not deep personalization

You likely need a stronger AI layer when:

customers ask many pre purchase questions
products need comparison or qualification
compatibility matters
recommendations depend on profile, routine, or use case
the catalog is large or dense
discovery is more important than basic support

That is the real line. The question is not whether AI sounds advanced. The question is whether your shoppers need help deciding.

Final takeaway

The best way to use AI for product recommendations on Shopify is to think in layers.

First, fix product data.

Second, use Shopify’s built in related products, complementary products, predictive search, filters, boosts, and search controls.

Third, add AI where native logic runs out, especially for intent, comparison, explanation, and guided discovery.

If you do that in the right order, product recommendations stop being a cosmetic widget and start becoming a real conversion tool.

Frequently asked questions

What is the difference between related products and complementary products on Shopify?

Related products are usually alternatives or similar items. Complementary products are items that go well with the main product and help increase basket value. A backpack and a luggage tag are complementary. Two similar backpacks are related products.

Do I need a third party AI app for Shopify product recommendations?

Not always. Many stores can get strong results by first using Shopify’s own recommendation tools, Search and Discovery, predictive search, and better product data. A third party AI layer becomes more useful when the catalog is large, the products are complex, or the store needs intent based recommendations.

What kind of product data helps AI recommendations the most?

The most useful data includes use case, customer type, fit, compatibility, material, ingredients, style, budget tier, and pairing logic. The more clearly your store describes products, the better AI can recommend them.

Can AI recommendations improve average order value?

Yes. They often improve average order value by showing complementary products at the right time, especially on product pages and in the cart. But the increase depends on how relevant the recommendations are.

Where should I place recommendations on a Shopify store?

The most useful places are product pages, collection pages, predictive search, search results, cart, and post purchase flows. The best placement depends on what type of recommendation you are showing.

Why do some product recommendation widgets perform poorly?

They usually fail because the logic is generic, the product data is weak, or the recommendations are placed in the wrong location. In many cases, the store is showing products that are popular but not actually relevant to the customer’s need.

Can AI recommend products based on natural language queries?

Yes. That is one of the main strengths of AI driven recommendations. A shopper can describe a need in simple language, and the system can map that need to the best matching products if the catalog data is strong enough.

How do I know if my recommendation system is working?

You should track revenue from recommendation clicks, add to cart rate, average order value, assisted conversions, search exit rate, and recommendation driven conversion lift. Clicks alone do not tell the full story.

References

Angela Ranjeet

Angela is the CTO at Automatikly, the company behind goodChatBot. She brings deep experience in AI, product strategy, and engineering leadership. Before joining Automatikly, Angela worked at Adobe on Firefly, its generative AI product, where she contributed as both an Engineering Manager and Product Manager.