A new store can run useful recommendations from day one by leaning on catalog signals it already has: product recency, price position, stock, margin, and collection relationships. Behavior signals like conversion rate join the ranking automatically as traffic accumulates, so the rows improve gradually instead of guessing from nothing.
Start with Catalog Signals
Recommendation logic does not need sales history to beat a random order. On day one you already know which products are new, fully stocked, well-margined, and in the same collection as the product being viewed. A row built from those signals, seeded by context (the viewed product, the cart contents), is genuinely helpful: it shows related, buyable products, which is most of the job. What it cannot yet do is distinguish two similar products by which one shoppers prefer, and that is fine, because neither can anything else on a store with no shoppers yet.
Let Data Take Over Gradually
- Blend, do not switch. Keep catalog signals in the recipe and add behavior signals with real weight as events accumulate; there is no cliff where the logic changes character.
- Rankings refresh automatically as data arrives, so the first bestseller starts earning better positions the week it becomes one.
- Watch the serving numbers. Clicks and adds per row tell you when the data is thick enough to justify heavier behavior weighting.
Dynasort seeds new recommendation recipes with sensible starting points, and every signal is a weight you can raise as your store’s data matures.
Dynasort recommendations work from day one and improve with data. Install it from the Shopify App Store or start with sensible defaults.