What staff counted by eye, a camera now counts
Family group or not. We moved a rule that changed from person to person onto the machine, then checked it against the manual tally.
From problem to result
- Problem
- Floor staff observed, sorted and tallied by hand. Visitors were missed during consultations, and the criteria shifted with the observer.
- Applied and measured
- The showroom was already measuring visitors, paths, dwell time and the funnel with saai insight. What remained was customer type. Staff who greet and consult at the same time could not record every visitor as it happened, and the tally changed with who was watching. We built the AI classification to match the categories the brand's CX platform uses, ran it side by side with the existing manual tally, and switched over after the comparison.
- Measured result
- AI sorts groups from the video and produces the statistics. Staff spend their time on consultation and service.
saai insight
Customer type · visitors · paths · dwell time · funnel
What it uses
On-site manual sorting and tallying → automatic classification and statistics
What changed
The manual tally and the AI classification were compared side by side before the switch
How it was checked
Analyzed after anonymization. No face recognition, no identification of individuals
Personal data
Evidence and scope
Adoption stage
- Pilot
- Deployed
- Scaled
How this was verified
Before the switch, the manual tally for the same period was compared side by side with the AI classification to confirm accuracy. Site names and the details of the classification criteria are not disclosed.
A real case at a premium automotive brand showroom. Site names and the details of the classification criteria are withheld to protect the client.