What good looks like
We're a growing Australian business, and we'd rather be upfront than dress up numbers that aren't ours yet. The scenarios below are illustrative examples — built from realistic, industry-typical outcomes — showing the kind of results people counting and BoostBI analytics can deliver.
ⓘ These are illustrative example scenarios, not verified results from named customers. Ask us for a reference call with an existing customer in your industry.
A Melbourne fashion retailer
Single store, one entrance, Nano sensor + BoostBI
The challenge
Rostering was based on the manager's gut feel about when it was busy, leading to over-staffing on quiet weekday mornings and under-staffing on Saturday afternoons.
The approach
Installed one Nano sensor and used BoostBI's hourly traffic charts to rebuild the weekly roster around actual peak periods.
The outcome
Typical results for this scenario: rosters better matched to real demand, with staff hours shifted toward genuine peak trading windows instead of being spread evenly across the week.
A regional shopping centre
Multi-entrance centre, sensors at every entry point + BoostBI centre-wide reporting
The challenge
Centre management had no reliable, independent visitor number to support tenant leasing conversations or measure the impact of centre-wide marketing.
The approach
Deployed sensors across every entrance, rolling up into one BoostBI centre-wide dashboard shared with the leasing team.
The outcome
Typical results for this scenario: a consistent, independent traffic figure used in leasing discussions, plus visibility of which entrances and time periods respond best to centre marketing.
A Sydney cafe group
Three sites, one Nano sensor per site, BoostBI multi-site benchmarking
The challenge
Each site rostered independently with no way to compare performance, so underperforming shifts at one location went unnoticed.
The approach
Rolled all three sites into a single BoostBI account, using multi-site benchmarking to compare traffic and staffing efficiency across locations.
The outcome
Typical results for this scenario: clear visibility of which site and which shifts were over- or under-staffed relative to actual customer flow, informing a more consistent rostering approach group-wide.
Want to be one of our first published case studies?
We're actively building out real, named case studies with our customers. If you're using People Counter Australia and would be open to sharing your results, we'd love to feature your story here — get in touch.
See what this could look like for your business
Book a free demo and we'll show you BoostBI running on data that reflects your own store.