V-Count

V-Count — Physical-store demand telemetry from owned AI sensors: Nano units measuring entrance traffic, occupancy and queue metrics; Nano Prime measuring in-store zone traffic, dwell time and heatmaps; normalized in the BoostBI layer and joined to the retailer's own POS transactions, workforce hours, and inventory/merchandising feeds to yield conversion, sales-per-visit and service-pressure series at store × time granularity, delivered through a scoped REST API plus native Shopify and Nebim connectors.. Mid-size specialist (63 staff, ~$12M revenue, $5.6M raised, London HQ) with a genuinely sc…

V-Count offers (Alternative, Geospatial, Fundamental) — Bounded estimate: 20K installed door positions × ~250 interval records/day × ~350 trading days ≈ 1.75B interval datapoints per year, before adding queue, zone and heatmap channels from Nano Prime installations. Installed-door count and the sensor-channel mix are the unknowns — request both..

Cross-retailer conversion and footfall benchmarking by format, region and trading period; same-store traffic alt-data for listed retailers (a leading indicator ahead of disclosed like-for-like sales), labor-scheduling and service-pressure models, real-estate and high-street asset analytics, site selection, and demand forecasting where POS alone cannot distinguish a traffic problem from a conversion problem — the vendor's own framing.

The data is with 12 years of history.

Coverage spans US, UK, Other; Application Software, Electronic Equipment & Instruments; alternative, geospatial, fundamental; equities.