A shopper is looking at a $40 pair of pants, and your "you may also like" row shows them a $600 coat. The row takes up space and sells nothing.
"You may also like" should fit. Most don't.
A recommendation earns its spot only if it's the thing the shopper would actually add next. Sledge builds recommendations from what really happens in your store: products bought together in your own orders, what each shopper just viewed, what's trending this week. The right next product, in the right place, instead of a generic row shoppers scroll past.
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// The mismatched row
A pants shopper shown more pants. A $40 buyer shown a $600 coat.
Open one of your product pages. The "related products" row sits there filling space, and almost nobody touches it: per WebToffee, click-through on a generic row can run as low as 2.5%. The shopper weighing a pair of pants gets three more pairs they already scrolled past. The $40 shopper gets a $600 coat.
A "you may also like" that guesses from a thin signal, a shared tag or matching collection, offers the substitute the shopper already rejected or the outerwear at ten times their budget. A row that ignores what the shopper is doing is wallpaper, not a salesperson.
Sledge builds product recommendations from real shopper behavior in your store instead, not a single shared tag: what shoppers view, buy together, and are buying now, so the next product fits what the shopper is actually doing.
// Bought together, by your actual orders
"Frequently bought together" only works if it's true for your store.
The recommendation that sells is the honest one: the products your shoppers genuinely buy in the same order. Get it right and it sells itself, because you're showing what the last hundred buyers of this item also grabbed. Guess instead, and you're back to the $600 coat next to the $40 pants.
So Sledge builds frequently-bought-together recommendations from co-purchase data in your own Shopify orders: when people buy this, they also buy that, again and again. The pairing is true because it already happened on your store, so it reflects what your shoppers actually buy together.
// Thirteen ways to say "add this next"
Native Shopify recommendations stop at a hand-picked block and basic related products.
That block is static, so every shopper sees the same hand-picked row, whoever they are and whatever they're viewing. A first-time visitor and a returning buyer with a full history get identical suggestions, which fit almost no one.
Sledge gives you thirteen recommendation types, each a different honest reason to add the next product:
- Frequently bought, pairs drawn from your own co-purchase data.
- Personalized picks, tuned to that shopper's behavior.
- Trending, the products selling fastest right now.
- Best seller, your top sellers, wherever you place the row.
- New arrivals, your latest drop.
- Recently viewed, the items this shopper already looked at.
- Related products, close matches to whatever they're viewing.
- Collection best seller, what's winning in the category they're browsing.
- Product by collection, more from the collection they're already in.
- Merchant's picks, the row you curate by hand.
- Recently purchased, what other shoppers just bought.
- Most viewed, the products drawing the most attention.
- Shop the last, the last units left, while they last.
Plus more for the moments in between. With 13 recommendation types on Shopify, against native Shopify's hand-picked block and basic related products, you pick the one that fits each spot, so a product page, a cart, and a homepage each get the suggestion that suits it.
// The right spot, not just the right product
A perfect recommendation in the wrong place still gets scrolled past.
Relevance is half the job. The other half is location. A "recently viewed" row buried where nobody scrolls is wasted, and a dense ten-product grid where a tidy four would convert is just noise. Native Shopify gives you almost no say over where a recommendation sits or how it looks, so even a good suggestion lands unseen.
Sledge places each recommendation by surface and position with control over layout: which page, where on it, how it shows. Your store-wide card settings carry through, so the star rating and add-to-cart button a shopper sees everywhere else follow the suggestion too, and they can act without leaving the page.
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Questions, answered
Why are my Shopify product recommendations so irrelevant?
Generic recommendations usually rely on a thin signal like a shared tag, so they suggest a substitute the shopper rejected or a product far off their budget. Sledge builds recommendations from real behavior in your store, including co-purchase and recent views, so the suggestion fits what the shopper is doing.
How accurate is Sledge's frequently bought together?
Sledge builds frequently-bought-together from co-purchase data in your own Shopify orders, so the pairing reflects what your shoppers actually buy in the same order, not a catalog-wide guess.
How do I add recently viewed and trending carousels to Shopify?
Sledge includes recently-viewed and trending among its 13 recommendation types. You place each one by surface and position and set the layout, so the row lands where shoppers actually look instead of below the fold.
What recommendation types does Sledge support?
Thirteen, including frequently-bought-together, personalized, trending over the last 7 days, new arrivals, recently viewed, and collection best-seller. Native Shopify offers a hand-picked Featured products block plus a basic related-products API, so Sledge's depth is in the behavior-based types and where they appear.
Can I control where recommendations appear?
Yes. Sledge places each recommendation by surface and position, so a product page, a cart, and a homepage each get the type and layout that suit them, instead of one fixed block everywhere.
Do recommendation cards show ratings and an add-to-cart button?
They follow your store-wide product card settings, so a star rating and an add-to-cart button carry into every recommendation row and a shopper can act on a suggestion without leaving the page they're on.
Are Sledge recommendations personalized to each shopper?
Yes. Personalized is one of the 13 types, tuned to an individual shopper's behavior, so a returning visitor and a first-timer don't see the identical row.
Show the product they'd actually add next.
Install free. Place your first recommendation today, build it from your own orders, and put it where shoppers will see it.
14-day free trial · No code · Works with any theme · Uninstalls clean