Product recommendations
AI product recommendations that lift AOV
Product recommendations decide which items each shopper sees next: on the homepage, on product pages, in the cart and in email. Skubl’s recommendation engine combines what a shopper has browsed and bought with their predicted intent and value, so every slot shows the products they’re most likely to buy. That means fewer dead ends, bigger baskets and more repeat orders.
Recommendations run on the same shopper profile as the rest of Skubl’s ecommerce personalization.
Illustrative example · sample store
The problem
Bestseller widgets aren’t recommendations
Most stores fill recommendation slots with bestsellers or hand-picked products. Every shopper sees the same items, including things they already own or would never buy.
- 01
The same picks for everyone
A first-time visitor and a five-time customer get the same carousel.
- 02
Recommending what they just bought
Shoppers are shown the jacket that’s already in their order confirmation.
- 03
Slots that never learn
Manual picks go stale, and nobody has time to update them for every product.
How Skubl solves it
How Skubl’s product recommendation engine works
Skubl’s product recommendation software chooses items per shopper and per placement.
01
Learns from your store
Models train on your catalog, orders and browsing behavior from the Shopify connector, and keep learning as new orders arrive.
02
Ranks by predicted value
Picks are weighted by each shopper’s predicted intent and lifetime value from Skubl’s predictive layer, not just by popularity.
03
Respects your rules
Exclude out-of-stock items, set margin floors, pin hero products and hide items a shopper already bought.
04
Personalized product recommendations everywhere
One model serves the homepage, product pages, cart and email, so shoppers see a consistent set of picks.
Types of AI product recommendations
Each placement uses the recommendation type that fits the moment.
Similar items
Alternatives to the product being viewed, matched on attributes and on what similar shoppers chose.
Frequently bought together
Items that are actually bought together in your orders, ready to add in one click.
Recently viewed
An easy way back to the products a shopper was considering.
Predicted next purchase
What this shopper is most likely to buy next, based on their history and predicted value.
Ecommerce product recommendations by placement
Where recommendations appear changes what they should do.
- Homepage
- Help returning shoppers pick up where they left off and introduce new visitors to your best-fit products.
- Product page
- Show alternatives for undecided shoppers and complementary items for decided ones. See cross selling.
- Cart
- Suggest small, relevant add-ons without distracting from checkout.
- Fill emails with picks that are current at the moment of open. See dynamic email.
A product recommendation strategy that keeps learning
How the models improve over time.
01
Collect signals
Views, add-to-carts, orders and returns flow in from your store.
02
Understand products
Products are related by attributes and by how shoppers buy them together.
03
Predict per shopper
Each shopper’s likely intent and value decide which related products rank first.
04
Learn from outcomes
Clicks and purchases feed back into the models, so picks improve as you sell.
Product recommendations in practice
Illustrative scenarios, not customer results.
Complete the look
A shopper viewing a linen shirt sees the pants and sandals most often bought with it.
After the first order
The order confirmation page suggests the next item this customer is most likely to want.
Search that knows the shopper
Two shoppers search “linen” and each sees their most likely match first.
What product recommendations should move
We’ll add verified customer results here.
Metric to watch
Average order value
Relevant add-ons and better alternatives raise basket size.
Metric to watch
Revenue per visitor
The overall measure of whether recommendations earn their space.
Metric to watch
Recommendation click-through
A leading indicator that shoppers find the picks useful.
Built on your Shopify data
Recommendations use catalog, customer and order data from the native Shopify connector, and can extend into email.
All integrations- ShopifyNative Shopify connector for products, customers and orders. The data foundation for every prediction and personalized experience.Available
- KlaviyoUse Skubl predictions and recommendations inside Klaviyo emails, and Klaviyo engagement as a shopper signal.Planned
- WooCommerceCatalog, customer and order data from WooCommerce stores.Available
Product recommendations FAQ
What is a product recommendation engine?
What are the best product recommendation engines?
How do you personalize product recommendations?
Keep exploring
Recommend what each shopper will buy next
Connect your store to see Skubl’s recommendations on your own catalog.
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