How is this different from a recommendations widget?
Recommendations help shoppers discover. Upselling intervenes at the cart and checkout. Personalization rewrites the entire experience for known customers. The SalesVu engine does all three from one customer graph.
What data does it use?
Every transaction, every product attribute, every loyalty event, every web session — all unified in your SalesVu data warehouse. No external pixels required.
Can I exclude certain items or categories?
Yes. You can blocklist items, force allowlists per channel, and exclude items that are out of stock or below margin thresholds.
Does it work for restaurants?
Yes. The engine handles modifier-aware prompts (suggest a wine pairing for a steak, fries with a burger, sauce upgrade with the bowl) on counter POS, handheld, kiosk, and QR ordering.
Is it included with SalesVu or an add-on?
The engine ships with the SalesVu Premium Cloud. The agents that drive it (Upselling, Recommendations, Guided Buying, Personal Rewards) can be activated as you need them.
What do the Website Builder Recommendation widgets render?
You Might Also Like, Similar Products, Frequently Bought Together, Past Purchases, Trending Now, New Additions and Best Sellers — drop-in components on PDP, cart and order confirmation. You get a full merchandising stack without integrating one.
What do the same widgets do inside OrderUp / QR / Kiosk?
Identical AI suggestions render in self-order surfaces. Average ticket grows on every channel, not just the website.
What does Custom Fields on Products do for personalization?
Attributes like fit, skill level, flavor profile and compatibility feed the engine's reasoning. Personalization matches how your customer actually thinks about your catalog.
What does Customer Past Purchase History do for the engine?
Past-Purchases recommendations let returning customers re-buy their staples with one click. Repeat revenue goes up because the friction to repurchase goes down.
What does Modifier-Aware Personalization do?
The engine suggests modifier upgrades (sauce add, drink size, add-on) per item based on real attach data, not menu structure assumptions. Restaurants get incremental margin on every modified order.
