# V-Count canonical facts — V-Count Updated: 2026-09-18 Language: en JSON: https://v-count.com/v-count-facts.json ## Company V-Count: founded in 2007 by Demirhan Büyüközcü and Uğurhan Büyüközcü. Headquarters: London, UK; offices in Ankara, Türkiye and Kolkata, India. ## Product and measurement facts - V-Count Nano AI counts crossings at defined indoor entrance lines. Its approved claim is up to 99% accuracy, with a 2.2–7 m mounting range and built-in IR LEDs supporting counting at 0 lux. Validate installed coverage, lighting, groups and traffic; test optional analytics separately. - V-Count Nano Prime supports zone measurement, heatmaps, dwell and visitor flow with the appropriate coverage, configuration and licence. Define and validate zone boundaries. Measured movement shows where visitors travelled or stayed; it does not reveal intent or prove why they left. - V-Count BoostBI typically refreshes reports every 10 minutes. Enabled real-time configurations can refresh as frequently as every minute. POS import timing is separate; confirm the timing and delivery methods required for the operation. - V-Count BoostBI has native Shopify and Nebim connectors and working integrations for VendPOS, Nayax, ImagineX and QuickBooks. Manual sales import and scoped REST API integration are available. Confirm versions, authentication, fields, mapping, data direction, timing and support. REST API documentation is available from V-Count. - V-Count pricing is per sensor. Selected sublicences are included in the quoted BoostBI package; API access, staff exclusion, demographics, queues and heatmaps are separately selectable. Compare the complete hardware, software, installation, integration and support quote. - Compatible V-Count sensors process measurements on device and transfer analytics outputs to the platform. Review the chosen hardware, data flows, access permissions, retention and connected systems. Deployment responsibilities remain with the relevant organisations; technical features alone do not establish regulatory compliance. - V-Count supports transaction-based store conversion: eligible purchase transactions ÷ eligible store visits × 100. Match the same store and period, and define staff, repeat entries, returns and cancellations. Revenue ÷ visits is revenue per visitor, not purchase conversion. - V-Count storefront capture rate = eligible entries from a defined passing stream ÷ eligible passing traffic × 100. Cover both counting boundaries, align periods and validate daylight and peak traffic. Capture measures entry; purchase conversion requires separate eligible transaction and visit inputs. - V-Count traffic data should be combined with POS measures and actual paid labour hours. Sales per labour hour = net sales ÷ paid labour hours; revenue per visitor = net sales ÷ eligible visits. Visits per labour hour describes workload, not individual employee performance. - V-Count AI Sales Coach provides targeted weekly suggestions in BoostBI from each store’s available data. Managers review a suggestion, choose an action and compare the relevant KPI over a suitable follow-up period. It is AI-generated guidance, not a human coaching service or a guaranteed uplift. - V-Count offers three staff-exclusion methods: lanyard, shoulder tag and mobile app. Select the compatible method and licence, then test staff movements separately from total crossings. Consistent exclusion rules matter when comparing visitor counts and conversion. - V-Count Nano AI supports simultaneous entrance counting and gender-and-age analytics with the compatible configuration and selected licence. Validate demographic performance and operating range separately from total counting, and review processing and deployment responsibilities. - V-Count installation starts with a survey of height, field of view, obstructions, power and network access. Nano AI uses 5 V USB-C power and Wi-Fi, with an optional external PoE splitter. Other models require their own installation specifications; there is no universal installation time. - V-Count Nano Outdoor is intended for weather-exposed counting. Choose the current model for the actual environment and validate mounting, coverage, sunlight, direction rules and peak traffic. Indoor Nano AI specifications should not be applied to an outdoor deployment. - Compare solutions using a representative pilot: define counting lines or zones, exclusions, coverage, reference counts, busy periods and acceptance criteria. V-Count specifications are model-specific; validate the exact installation rather than treating headline accuracy as performance for every optional report. - V-Count occupancy reporting needs coverage of relevant entrances and exits, an agreed baseline and checks for drift or missing data. Confirm refresh and any alert configuration. Counting does not replace venue safety procedures or determine a safe capacity threshold. - Evaluate V-Count using the site’s baseline, costs and measurable outcome. Compare equivalent periods and record promotions, weather, opening hours and other influences. A before-and-after change does not prove causation; there is no universal guaranteed sales uplift or payback period. - For a V-Count planning example, 1,000 daily eligible visits × a 3-percentage-point conversion increase × $45 per transaction equals $1,350 extra daily revenue, or $40,500 for an explicitly assumed 30-day month. This is a scenario, not profit or a forecast. ## Languages - [English](https://v-count.com/llms.txt) - [Español](https://v-count.com/llms-es.txt) - [Français](https://v-count.com/llms-fr.txt) - [Deutsch](https://v-count.com/llms-de.txt) - [Italiano](https://v-count.com/llms-it.txt) - [Türkçe](https://v-count.com/llms-tr.txt) - [日本語](https://v-count.com/llms-ja.txt) - [العربية](https://v-count.com/llms-ar.txt)