Personalized recommendations
Picks build themselves from real orders, the cart, wishlists and past purchases — and each one shows why it's there.
Why it matters
Hand-picked "Wear it with" pairings work beautifully for the hero pieces of a collection, but no merchandiser can style hundreds of products by hand — let alone tailor them to every shopper. Yet your store already holds the data for great suggestions: which items really sell together, what's in the cart, what a shopper saved to their wishlist and what they bought last time.
Personalized recommendations turn those signals into picks automatically. A shopper with a dress in her cart sees the bag people often buy with it, a wishlist item that's available in her size, and trousers to go with the jacket she bought in spring. Not random "bestsellers".
How it drives sales
- More items per order. A suggestion in the cart lands when the buying decision is already made: adding a bag to a dress is easier than starting a new search.
- Wishlists turn into orders. A saved piece comes back at the right moment — and only if it's in the shopper's size.
- A reason to return. Recommendations built on past purchases grow the wardrobe: trousers for the jacket, shoes for the dress. Anything already bought is never suggested again.
- Picks shoppers trust. A label like "From your wishlist" or "Often bought together" explains why an item is there, so it feels like helpful service, not pushy advertising.
How it works
- The system analyzes your store's orders and finds items that are bought together — that's what powers the "Frequently bought together" block on the product page.
- In the cart, picks take into account what's already selected, the shopper's wishlist and their previous orders.
- The homepage and the customer account get a "You may also like" block built for that specific person.
- Every recommendation carries a reason label: "From your wishlist", "Often bought together", "For your VERONA jacket".
- Rules filter out the noise: out-of-stock items, items not available in the shopper's size and items they already own never make the cut.
CMS settings
| Setting | What you get |
|---|---|
| Sources | Turn on or off: bought together, wishlist, past purchases, recently viewed |
| Weight and order | Use sliders to decide which signals matter most for your store |
| Exclusion rules | In stock only, shopper's size only, exclude purchased |
| Manual pinning | Put a chosen product first, on top of the automation — a new arrival or a campaign piece |
| Placement | Product page, cart, homepage, customer account |
A live preview in the settings shows exactly how the picks change, so there's no guessing what shoppers will see.
Automation and curation
Personalized recommendations don't replace related products — they complement them. Pairings set by your team remain the brand's take on the look; automation covers the rest of the catalog and adapts to every shopper. Together with the wishlist and the "Recently viewed" strip, they cover the whole journey: from "that caught my eye" to "something to go with what I already own".