Ce comandă împreună clienții tăi — din bon, nu din persoană.
Dwelitics citește bonurile anonime de la POS și scoate la suprafață ce produse se vând împreună și cât de puternică e legătura. Fără conturi, fără urmărire, fără profile pe client — doar tiparele de cumpărare ascunse în bonurile tale.
Anonymous by design. This module reads baskets, not people — it surfaces which items sell together from POS tickets. It does not build per-guest profiles, track individuals, or measure how often a specific person returns.
What lands in the basket together?
Top item combinations and average basket value — per shift, per day, per week. You see the shape of a typical order, not just revenue per cover.
A table ordering a starter, a main, and a shared dessert is a different basket than a quick solo lunch. Composition makes that visible — without knowing who anyone is.
Top combinații — această săptămână
512 bonuri
Valoare medie bon: 54 RON
Co-ocurență
71%
Lift
×3.4
Co-ocurență
68%
Lift
×2.8
Co-ocurență
61%
Lift
×2.3
Co-ocurență
54%
Lift
×2.1
Co-ocurență
47%
Lift
×1.9
Co-ocurență
38%
Lift
×1.4
Know what sells together. Build it in.
When two items appear together in most orders, that's not coincidence — it's a menu-placement and upsell opportunity your staff should know about.
What lift means
A lift of ×2.8 means a basket with the first item is 2.8× more likely to also contain the second than an average basket. The higher the lift, the stronger the signal — and the more it's coloured toward the high tier above.
Camera-based behaviour tracking
Dwelitics will use anonymised Vision Analytics to surface patterns POS data alone can't: walkout detection, idle-table detection, queue length, and zone heatmaps. This is roadmap — the live module today is anonymous basket affinity.
No personal identification. No biometric data. No cloud video upload. Edge processing only — privacy by design.
Vezi Comportamentul clienților în acțiune
Solicită o prezentare și îți arătăm cum se configurează analiza de afinitate.