See every visitor and their intent. Improve your sales
StoreTwin goes beyond the footfall count to show what shoppers want, and what each store should change next.
- 94%
- Bays in plan
- 17–19h
- Peak hours
- 12
- SKUs below min
- ShelfBay 04, beverages: SKU out of plan since 09:40
- FloorBay 07, snacks: high traffic, low conversion
Beyond the count
A footfall counter tells you how many came in. StoreTwin tells you what they came for, and what stopped them buying.
Footfall by hour, branch and season.
Where most tools stop.Age bands, gender and segments, read at group level. Nobody is identified.
Paths, dwell, pick-ups, put-backs and walk-outs show what they came for, and what stopped them buying.
Range, staffing and layout moves for each store.
Read intent, not just traffic
Shelf and shopper, read on the same cameras.
Compare every store in your network, and run each one its own way
No two stores trade alike. StoreTwin reads all of them, so you can see which branches outperform and why, localise range, staffing and promotions to each one, and give shoppers a store built around how they actually shop there.
| 08 | 10 | 12 | 14 | 16 | 18 | 20 | 22 | Conv. | |
|---|---|---|---|---|---|---|---|---|---|
| 1091 Talat | 08:00, footfall 22, 34% bought | 10:00, footfall 34, 36% bought | 12:00, footfall 62, 31% bought | 14:00, footfall 44, 38% bought | 16:00, footfall 51, 35% bought | 18:00, footfall 88, 21% bought | 20:00, footfall 96, 19% bought | 22:00, footfall 40, 33% bought | 31% |
| 0447 Sathon | 08:00, footfall 48, 41% bought | 10:00, footfall 72, 22% bought | 12:00, footfall 94, 20% bought | 14:00, footfall 55, 37% bought | 16:00, footfall 42, 39% bought | 18:00, footfall 60, 36% bought | 20:00, footfall 52, 35% bought | 22:00, footfall 20, 30% bought | 33% |
| 1220 Rangsit | 08:00, footfall 16, 30% bought | 10:00, footfall 24, 33% bought | 12:00, footfall 40, 35% bought | 14:00, footfall 36, 32% bought | 16:00, footfall 58, 34% bought | 18:00, footfall 82, 36% bought | 20:00, footfall 90, 34% bought | 22:00, footfall 62, 31% bought | 33% |
| 0863 Hua Hin | 08:00, footfall 30, 32% bought | 10:00, footfall 42, 30% bought | 12:00, footfall 50, 33% bought | 14:00, footfall 62, 23% bought | 16:00, footfall 74, 21% bought | 18:00, footfall 70, 22% bought | 20:00, footfall 55, 30% bought | 22:00, footfall 28, 33% bought | 28% |
| 1502 Khon Kaen | 08:00, footfall 20, 38% bought | 10:00, footfall 30, 36% bought | 12:00, footfall 46, 37% bought | 14:00, footfall 34, 39% bought | 16:00, footfall 40, 35% bought | 18:00, footfall 66, 37% bought | 20:00, footfall 72, 36% bought | 22:00, footfall 34, 34% bought | 37% |
- Quiet
- Steady
- Peak
- Busy, low conversion
- Conv.Visitors who bought
December runs 1.6× January here, and the peak hour moves two hours later in the rainy season.
One store, many signals
Tailored to your store plan and layouts.
Every bay, every SKU, against plan
Each bay is read against your planogram at SKU level. A product in the wrong place, a line below its minimum or a bay losing share of shelf shows up without anyone walking the aisle.
- Bays in plan
- 94%
- SKUs below minimum
- 12
- Share of shelf vs plan
- −6%
What it takes to start
Your existing cameras, with a small change to angle or position if needed. Your in-house POS, as long as it streams sales data.
We connect your cameras and POS, then configure each signal to your layout, procedures and the incidents you actually face. No generic alerts.
Whichever you prefer. We advise on the best fit for your stores and your data rules.
Frequently Asked Questions
Retailers who run every store on evidence, not instinct. Teams that want each branch to know its own shoppers, act within the day and learn from every visit. Store teams get alerts while they can still act; planners and regional managers see every store on the same measures.
Get in touch
Got questions about loss prevention, smart audit, or store analytics? Let's connect to get started.