Group Detection
A family of four is one purchase decision, not four
KSI identifies the groups that walk in together, re-identifies them on the way out, and treats them as a single conversion unit. This dramatically changes how your real traffic, conversion, and customer base composition are measured.
Request my demo→Conventional sensors treat every person as an independent opportunity
A family of four walks into your store, browses, looks, and buys once. To a conventional sensor, that's four conversion opportunities and one sale - a 25% conversion rate, when reality is 100%.
The same happens with couples, friend groups, and customers with companions. Without grouping, every conversion, average ticket, and behavior metric is systematically underestimated or distorted.
Group = one purchase unit, not several visits
Temporal grouping + re-identification
1. Proximity-based detection
KSI detects groups at access points by combining physical proximity with joined trajectories inside the store.
2. Anonymous re-identification
On exit, KSI anonymously re-identifies the group. No personal data stored.
3. Group-level dwell and conversion
Computes dwell time and conversion of the group as a single purchase unit.
What working at the group level shows
Real conversion per group
Conversion rate per purchase unit - not per person - reflects what actually happens in your store.
Family / couple / solo segmentation
Compare behavior, ticket, and conversion across visit types.
Service sizing
Serving a family of four doesn't take the same time as serving a single customer. Plan staffing on real units.
Social and behavior analysis
Analyze how companions, kids, and group dynamics influence purchases and shopping center demand.
Metrics that reflect reality
~95% validated accuracy
Algorithm trained and validated against real datasets. Accuracy decreases at extremely dense entrances (>600 people/hour per entry).
Compatible with anonymous re-identification
Combined with re-ID, measures dwell and conversion at the group level without storing personal data. GDPR compliant.
Group detection FAQ
KSI Vision identifies when several people enter together by combining physical proximity and joined trajectories within the space. On exit, it re-identifies the group anonymously and calculates its dwell time and conversion as a single buying unit, not as individual visits.
Because a family of four that enters, walks around and buys once represents a 100% conversion rate as a group, but a system that counts person by person would record it as 25% (one purchase across four visitors), systematically underestimating real conversion.
Around 95%, validated against real-world datasets.