Aqurate's AI is built to increase sales through conversion rate and average order value.
But sometimes you have a specific business goal the AI doesn't know about: clear out-of-season stock, push a high-margin brand, or keep discontinued products out of view. Merchandising lets you steer recommendations toward goals like these, on top of the AI, without turning it off.
⚔️ Basic filter vs. Merchandising rules
| Basic Filters | Merchandising Rules |
What it does | Removes products from all recommendations | Full control: when rules apply, how products are sorted, which attributes and conditions trigger them |
How you set it up | Upload a CSV of the products to exclude | Configure in the app |
Best for | "Just keep these products out of recommendations, everywhere" | Anything more specific: scheduling, conditions, or targeting by attribute |
A Merchandising Rule (type = Filter out) can do everything a Basic Filter does. If you only need a straightforward exclusion, use the Basic Filter, it's faster to set up.
5️⃣ The five rule types
Pin to Top – force a specific product to the top of a recommendation block.
Filter out – exclude specific products from recommendations.
Match Attribute – only show products matching a given attribute (e.g. same brand, same category).
Boost – make a product appear more often.
Weaken – make a product appear less often.
When rules overlap, this is the order they're applied in:
Pin to Top → Filters (Basic Filter, Filter out, Match Attribute) → Nudges (Boost, Weaken)
The highest-priority rule wins; anything below it is disregarded for that product.
🤔 Rule vs. Condition
Both use AND/OR logic, which makes them easy to mix up. Think of it this way:
Rule = what products are affected. "Boost Nike shoes." "Pin Product 123 to top."
Condition = when and where the rule applies. "Boost Nike shoes, but only when the shopper is viewing Adidas products." "Pin Product 123, but only when the shopper is viewing products over 50 RON."
✅ What attributes can I use?
Whatever your shop platform exposes, e.g. name, price, stock or category.
Native integration (Shopify, Gomag, MerchantPro): missing an attribute in our list, but you have it configured in your shop? Email [email protected] and we'll try to bring it in if possible.
Custom API integration: you can upload custom attributes yourself via the API. Keep them up to date as your catalogue changes, or the rules built on them will stop matching correctly.
💡 Common use cases
Based on what shops most often ask us for help with:
Keep specific products out of recommendations – discontinued items, hidden products, or anything you don't want surfaced. Use Filter out, scoped by product ID or by attribute, so new discontinued products are caught automatically.
Run seasonal or promotional campaigns – pin or boost the products in a campaign (Black Friday, a seasonal push) for a set date range, then let the rule expire on its own.
Feature one brand or supplier – Match Attribute or Boost scoped to a brand, useful for supplier-funded placements.
Untangle conflicting rules – if two rules target the same product, remember the pecking order above. Pin to Top always wins.
👉 Good to know
Changes aren't instant. The AI model retrains periodically, and your platform then needs to pull the updated recommendations. Give it some time before assuming a rule isn't working.
Merchandising creates a trade-off. It's a deliberate bias toward a specific business goal, which can work against the AI's default optimization for conversion rate and AOV. Use it intentionally, not by default.