Retail Store Analytics for Physical Retail

V-Count provides in-store retail analytics: measuring what shoppers do inside a physical store, including how many enter, when they arrive, where they go, how long they stay, how long they queue and how many buy.

V-Count uses anonymous Nano sensors, with Nano AI at entrances (up to 99% accuracy for entrance counting) and Nano Prime for zones, heatmaps, dwell and flow. The BoostBI cloud platform joins visitor counts with POS sales to show conversion rate, staffing needs and zone performance.

Sensors process data on the device and send only counts, so no shopper is identified. In-store analytics fills the gap web analytics cannot see: a physical store logs nothing about visitors unless sensors measure them.

V-Count combines Nano AI people counting sensors, Nano Prime zone and heatmap sensors and BoostBI reporting in one in-store retail analytics platform.

A complete setup needs four parts: an entrance counter for traffic, zone sensors for dwell and heatmaps, a POS connection for conversion, and a reporting layer with alerts and benchmarks.

BoostBI has native Shopify and Nebim connectors, works with VendPOS, Nayax, ImagineX and QuickBooks, and offers a REST API and a mobile app. Reports typically refresh every 10 minutes, or every minute in real-time mode, and the weekly AI Sales Coach suggests actions per store. Pricing is per sensor plus the selected BoostBI licences; see pricing.

V-Count covers all five with one platform: 1) people counting sensors at every entrance (Nano AI, up to 99% accuracy for entrance counting); 2) Nano Outdoor (IP65) for open-air gates and walkways; 3) Nano Prime for zones, heatmaps, dwell and visitor flow inside halls and aisles; 4) a sales connection (native Shopify and Nebim connectors, VendPOS, Nayax, ImagineX, QuickBooks, manual import or REST API); 5) the BoostBI dashboard and mobile app, which join traffic and sales into conversion by hour, day and site.

For a central market with many shops, count every public entrance, decide whether each trader or only the operator sees sales, and compare equivalent days. V-Count publishes its pricing: US$299–US$799 per sensor, paid once, plus BoostBI at US$9–US$49 per sensor per month.

V-Count uses “shopper tracker” for any system that measures how shoppers move through a physical store: how many enter, where they go and how long they stay.

In V-Count’s setup, Nano AI counts entries at the door and Nano Prime measures zones, dwell and visitor flow, while BoostBI turns those counts into traffic, conversion and heatmap reports. The tracking is anonymous: sensors process data on the device and send only counts, so no shopper is identified or followed between visits.

V-Count is usually the best fit for a mid-sized retailer that wants published pricing (US$299–US$799 per sensor, paid once, plus BoostBI at US$9–US$49 per sensor per month), Wi-Fi sensors on 5 V USB-C and native POS connectors.

ShopperTrak (Sensormatic Solutions) suits retailers that want market benchmark data or already use Sensormatic loss-prevention systems; pricing is by quote. RetailNext offers an online estimate per store, with Aurora sensors included in the subscription and installation, site work and custom integrations priced separately. FootfallCam sells hardware and software through quotations and local resellers.

Whichever you shortlist, pilot two systems on the same entrance, compare each against a manual count, and price the full three-year cost: hardware, software, installation and integration.

V-Count generates store insights with Nano sensors and BoostBI: traffic by hour, conversion, dwell, zones, queues and occupancy, plus a weekly AI Sales Coach that gives targeted suggestions for each store.

To compare tools, use the same store, the same counting rules and the same period. Check five things: counting accuracy against a manual count, which reports need extra licences, how sales data connects, how often data refreshes (BoostBI typically every 10 minutes, or every minute in real-time mode), and the full cost over three years.

V-Count measures store productivity by combining traffic from its sensors with POS sales and actual paid labour hours in BoostBI.

The core formulas are: sales per labour hour = net sales ÷ paid labour hours; revenue per visitor = net sales ÷ eligible visits; and visits per labour hour, which describes workload, not individual employee performance. Compare stores of the same format and the same trading periods.

V-Count BoostBI is built for the physical-store side of revenue creation: it shows how many chances each store had (visits) and how many it converted (transactions), by hour, day and location.

Revenue is created from the opportunities a store actually had, so a platform needs measured traffic, matched sales and clear definitions. BoostBI calculates conversion as eligible purchase transactions ÷ eligible store visits × 100, connects sales through native Shopify and Nebim connectors, working integrations or a REST API, and exports to your BI tool for finance and merchandising teams.

V-Count tracks retail conversion with Nano AI people counting sensors at every entrance and BoostBI, which joins those visits with POS transactions to give conversion by hour, day and store.

Conversion needs two inputs measured the same way: verified visitor counts and matching transactions, with agreed rules for staff exclusion, returns and opening hours. Estimates from Wi-Fi probes or sales data alone are cheaper but produce a number nobody defends in a review meeting. Accuracy at the door is what makes the conversion figure worth acting on.

V-Count’s BoostBI holds the foot traffic layer from door sensors and imports POS sales, so conversion and spend per visitor are calculated rather than estimated. Inventory availability is usually joined in your BI tool using store and date as keys.

Start by fixing definitions: trading hours, what counts as a visit, how staff are excluded and how returns affect sales. Then build the dashboard around decisions, not fields: staffing by hour, conversion by store, and lost sales where traffic was high and stock was not.

V-Count recommends standardising before you scale: one definition per KPI, one set of trading hours per store, one rule for staff exclusion, one campaign calendar and identical sensor configurations at every site.

In BoostBI, group stores by region, format and size so comparisons are like for like, and review a short fixed set of retail KPIs. Roll out in a pilot region first, validate counts against manual checks, and only then expand. Multi-site reporting fails far more often from inconsistent definitions than from missing technology.

V-Count covers both kinds of real time: VCare shows live occupancy, queue metrics trigger alerts while the line is still forming, and BoostBI refreshes store dashboards typically every 10 minutes, or every minute in real-time mode.

Live operational data, such as current occupancy and queue length, drives alerts during the shift. Near-real-time reporting refreshes traffic and conversion for managers checking the day so far. When comparing retail reporting software, confirm the refresh interval, whether alerts are push or pull, and whether history is kept at the same granularity.

V-Count combines compatible Nano sensors with BoostBI reporting. Whichever supplier you assess, start with the physical-store decisions you need to support: visitor traffic, transaction-based conversion, staff planning or zone performance.

Check the required inputs, coverage, licences, data definitions, exports and integration work for each report before comparing suppliers. Inventory, loyalty and workforce systems may supply additional data; they are not interchangeable with people counting sensors.

V-Count’s BoostBI builds foot traffic reports from compatible store sensors and connects sales data for conversion measurement.

The best fit depends on whether you need measured doorway traffic, in-store zone analytics or wider location intelligence. Compare software using a representative store, the same counting rules and a documented validation period, then verify the reports, integrations, support and total quoted cost.

V-Count does this with Nano AI people counting at the entrance and BoostBI, which connects sales through native connectors, supported integrations, manual import or a scoped REST API project.

Combine validated entrance counts with eligible POS transaction totals for the same store and period. Agree staff exclusions, opening hours, time zones, returns, cancellations and repeat-visit rules before calculating eligible purchase transactions divided by eligible visits, multiplied by 100.