Product recommendations
Recommending the right product at the right moment is the core of what Zubby AI does. The agent suggests products inside chat — cross-sells, bundles, alternatives, “more like this” — and every suggestion is scored, logged, and attributed. The Recommendations page is where you see how that engine is behaving and shape it.
Find it at AI Brain → Recommendations. The page is a configuration and insight surface; the actual boost and exclude rules are managed under Rules.
How recommendations are generated
Recommendations are not a static “customers also bought” table. They’re produced live by the agent’s function- calling tools as a conversation unfolds — searching the catalog, comparing items, building bundles and outfits, and finding alternatives — all grounded in your synced catalog and embeddings. The ranking layer then orders candidates using:
- Conversation context — what the shopper said they want, their budget, prior messages.
- Product relevance — semantic similarity from the product embeddings.
- Your rules— boosts and exclusions you’ve set (below).
- Real outcomes — whether past shoppers clicked, added to cart, or purchased the candidate.
The KPI row
The four cards at the top summarize the engine at a glance:
- Total Recommendations — all-time count of products the AI has recommended.
- Avg. Confidence— the mean confidence score across recommendations. 70%+ reads as “high confidence”; lower means there’s room to improve catalog or knowledge coverage.
- Boost Rules — how many boost rules are active (and the total defined).
- Exclude Rules — how many exclude rules are active (and total).
Boost and exclude rules
Rules are how you put a thumb on the scale without touching the model. The Active Rules panel summarizes them, and the toggle on each row turns a rule on or off in place. Manage the full set under Rules (the Manage All Rules button).
| Rule type | Effect |
|---|---|
| Boost | Increases a product’s visibility in suggestions. Supports a weight modifier (default 1.0×) — a 2.0× weight doubles the ranking score, making the product about twice as likely to appear. |
| Exclude | Removes a product from suggestions entirely. Use it for drafts, discontinued lines, MAP-restricted items, or anything you never want the AI to push. |
Each rule targets a scope — Global, Collection, Product, or Category — so you can boost an entire collection for a seasonal push or exclude a single SKU.
Exclusions are a hard stop
Confidence scoring
Every recommendation carries a confidence score from 0 to 1, derived from conversation context, product relevance, and active rules. In the dashboard it’s shown as a percentage badge: 80%+ is green (strong match), 50–79% is amber, and below 50% is muted. Consistently low confidence usually points at a catalog or knowledge gap rather than a tuning problem.
Attribution & outcomes
Recommendations only get better if Zubby can see what happened after one appeared. The widget records the outcome of each suggestion — clicked, added to cart, or purchased — and those outcomes feed back into ranking.
Revenue is attributed on a last-touch model within a 30-minute window: if a shopper buys within 30 minutes of a recommendation, that purchase is credited to the AI. The three insight cards on the page — Outcome signal, Knowledge dependency, and Ranking inputs — help you sanity-check that impressions, outcomes, and rule pressure are all moving in the same direction.
Recent Recommendations table
The bottom of the page lists the last ten products the AI recommended, with columns for:
- Product — links straight to its catalog detail page.
- Confidence — the score badge described above.
- Context — which tool produced it (e.g.
get cross sell), its rank position, and the page type it appeared on. - Outcome — purchased, added to cart, clicked, or no outcome yet.
- Recommended — a relative timestamp.
When performance looks off, compare your rule pressure against the recent low-outcome rows here to see whether a boost is over-promoting something shoppers don’t actually want.
The fastest win
Gotchas
- No recommendations yet? They only appear once the AI has chatted with shoppers and your catalog is synced. Check Catalogand the agent’s training status first.
- Confidence stuck low? Fix description gaps and knowledge coverage before fiddling with weights — ranking trust follows content quality.
- Attribution looks light? Outcomes depend on the widget capturing clicks and cart events, so make sure the widget is installed and tracking on your storefront.
Related
- Catalog & product sync — the source of every recommendable product.
- The AI agent — the tools that generate recommendations in chat.
- Knowledge base — coverage that lifts both copy and ranking trust.
- Reviews & UGC — social proof the agent cites alongside recommendations.