Natively, Shopify generates product recommendations automatically from signals like purchase history and product data, and themes render them as “You may also like” sections. Control is limited: you can set related and complementary products by hand in the Search & Discovery app, per product. Recommendation apps replace this with logic you define and measure.
What You Get Natively
Shopify’s built-in recommendations are a reasonable default and cost nothing. The trade-off is opacity and per-product manual work. You cannot tell the native system “favor high-margin accessories in the cart” or “never show sold-out products here.” Hand-picking complements per product works for a twenty-product store and collapses at two hundred, because every new product and every stockout means curation work nobody schedules.
When You Need Control
Recipe-driven recommendations flip the model: instead of picking products, you set the rule and the rule picks the products.
- A rule per placement. The product page row, cart row, and home page row can each run different logic, favoring discovery, complements, or margin.
- Context does the seeding. Product pages recommend around the viewed product; the cart row considers everything in the cart and never repeats it.
- Everything is measured. Serves, clicks, add-to-carts, and checkouts per row, so you know what the widget earns, not just what it shows.
Dynasort recommendation recipes work exactly this way, with a live preview of what any shopper will see before it ships.
Dynasort recommendation rows run on logic you can read and edit. Install it from the Shopify App Store or see recipe-driven recommendations.