Flame combines video and a wide range of data with AI to improve decision-making and performance in every supermarket: understand customer behaviour, measure the performance of your spaces and connect with your visitors.
Real customer behaviour across aisles, sections and shelves. Map journeys, detect hotspots and discover where purchase decisions happen.
Measure
The performance of every square metre of your supermarket. Compare stores, identify high- and low-performance zones, and connect in-store behaviour with sales.
Connect
With your visitors from the first digital touchpoint. Capture data via WiFi and captive portal, identify returning vs new customers, and trigger real-time campaigns.
Optimise layout and zone performance
Customer flow: analyse traffic, journeys and movement patterns to understand how shoppers move through the supermarket and how they make decisions.
Zone analytics: measure traffic and dwell time per section to detect high- and low-performance areas.
Shelf analytics: identify product interaction levels along each shelf and the zones that draw the most attention.
Visual merchandising: evaluate the impact of layout and product display on customer behaviour.
In-store conversion: detect where sales are generated (or lost) and optimise performance and average ticket.
Real-time operational efficiency
Traffic peaks: identify high-demand moments and anticipate operational needs.
Queue management: reduce wait times, detect checkout saturation and improve the payment experience.
Staff planning: align resources with real traffic and operational load.
Zone saturation: detect bottlenecks and areas with excessive customer concentration.
Continuous monitoring: visualise the state of the supermarket in real time to take immediate decisions.
Turn traffic into customer relationships
Guest WiFi: the first digital touchpoint to identify and connect with the visitor.
Captive WiFi portal: capture and validate customer data GDPR-compliant and build your own first-party database.
Customer identification: distinguish new vs returning visitors to understand behaviour, frequency and value.
Real-time communication: trigger campaigns and messages during the visit based on context.
Flame AI Agent: analyse the data and generate insights and automatic operational recommendations.
Integrated products, three levers
Measure and improve space performance, understand customer behaviour and connect with your visitors. Three products designed to build the retail operation of the modern supermarket.
Somos clientes desde hace años y pretendemos seguir siéndolo. Flame te cuenta lo que no puedes ver sentado en una oficina o dando un paseo por el hall del centro comercial. Te acerca al cliente desde el momento en que pasa por delante.
Gracias Flame analytics por acompañarnos en el apasionante reto de ofrecer a nuestros visitantes una experiencia omnicanal total.
Vicente Alemany ClimentCoordinador de Innovación · Cushman & Wakefield
Los datos de Flame nos ayudan a adaptarnos a nuestros clientes, cada vez más exigentes, y a las marcas, que nos demandan cada vez más información.
Lucas MadiedoDirector de Transformación Digital · Merlin Properties
Gracias a Flame hemos conseguido retener a nuestros clientes, aumentar el ROI y mejorar el rendimiento empresarial. Conocer el flujo de movimientos ha sido vital.
Luis SimónGerente · CC Plaza Éboli
Somos clientes desde hace años y pretendemos seguir siéndolo. Flame te cuenta lo que no puedes ver sentado en una oficina o dando un paseo por el hall del centro comercial. Te acerca al cliente desde el momento en que pasa por delante.
Gracias Flame analytics por acompañarnos en el apasionante reto de ofrecer a nuestros visitantes una experiencia omnicanal total.
Vicente Alemany ClimentCoordinador de Innovación · Cushman & Wakefield
Los datos de Flame nos ayudan a adaptarnos a nuestros clientes, cada vez más exigentes, y a las marcas, que nos demandan cada vez más información.
Lucas MadiedoDirector de Transformación Digital · Merlin Properties
Gracias a Flame hemos conseguido retener a nuestros clientes, aumentar el ROI y mejorar el rendimiento empresarial. Conocer el flujo de movimientos ha sido vital.
Luis SimónGerente · CC Plaza Éboli
Preguntas frecuentes
How does analytics improve shopping mall management?
Footfall analytics transforms shopping mall management from intuition-based decisions to data-driven strategy. Flame provides shopping mall managers with real-time and historical metrics: total mall traffic, floor distribution, zone performance, and hourly patterns. This data drives key decisions: optimal event scheduling (when to generate traffic vs. when traffic is already high), tenant mix (which store types attract complementary footfall), marketing efficiency (which campaigns generate real visits), and operational planning (cleaning, security, HVAC based on real occupancy). A leading European operator manages its 10 shopping malls with Flame data, achieving quantified operational and strategic decisions. The same data-driven approach powers Flame's analytics across physical spaces of all types.
Can tenants access footfall data?
Yes. Flame offers multi-tier dashboards that allow shopping mall management to share relevant data with tenants while maintaining confidentiality of sensitive information. Each tenant sees: total mall traffic (context), their floor or zone traffic, and their own footfall if they have cameras connected. They don't see individual data from other tenants. This shared-data model benefits everyone: tenants understand the context of their performance (did my sales drop or the entire mall's?) and management demonstrates the mall's value with objective data. In lease negotiations, Flame data provides objective evidence of the traffic the mall generates for each zone. A similar multi-tier access model applies to Flame deployments in other physical spaces, where different stakeholders access the relevant metrics.
What is real-time occupancy management?
Real-time occupancy management continuously monitors how many people are inside the shopping mall, by floor and zone, comparing against defined capacity thresholds. Flame displays current occupancy on real-time dashboards and can trigger automatic alerts when predefined levels are reached (80%, 90%, 100% of capacity). Born as a post-COVID need, occupancy management has become permanent operational practice because it improves visitor experience (preventing crowding), optimizes resources (security, cleaning, HVAC based on real occupancy), and meets capacity regulations. Over 50 shopping malls in Flame's network use occupancy displays at entrances. The same occupancy management technology is deployed in retail stores and other physical spaces.
How does parking analytics work?
Parking analytics connects vehicle traffic data with foot traffic to provide a complete picture of the shopping mall visit. Flame can integrate data from existing parking sensors with its footfall analytics, revealing correlations: average time between parking arrival and mall entry, visit duration by access origin (car vs. public transit), and parking occupancy rates correlated with mall footfall. This enables optimizing parking signage, adjusting mall hours based on arrival patterns, and evaluating alternative transportation initiatives. Leading shopping mall operators use this combined data to optimize the arrival experience and reduce access friction.
How does it measure event effectiveness?
Flame measures shopping mall event impact in three dimensions: traffic lift (how many additional visitors the event generated), traffic distribution (did attendees visit stores or just attend the event?), and retention (did event visitors return in the following weeks?). The process: Flame establishes a baseline of traffic for the same day/time in previous weeks, measures traffic during the event, and calculates net lift. For a concert at a large shopping mall, Flame demonstrated that 67% of attendees visited at least two stores before or after the event, and 23% were new visitors, quantifying the event's real value beyond attendance. The same A/B measurement methodology applies to events in any physical space.
Can performance be compared across shopping malls?
Yes. Flame's portfolio dashboard allows comparing normalized metrics across shopping malls, controlling for size, location, mall type, and seasonality. Comparable KPIs include: footfall per square meter, parking capture rate, average visit time, traffic distribution by floor, and event conversion rate. A leading operator compares its 10 shopping malls with Flame to identify best practices, detect performance anomalies, and allocate marketing budgets based on objective data. Anonymous benchmarks from the database of more than 50 Flame shopping malls allow comparing your mall against the market. This level of competitive intelligence without personal data is exclusive to platforms with scale like Flame, which also benchmarks retail stores and other physical spaces in its network.
What reports do investors and owners receive?
Flame generates executive reports designed for investors and commercial real estate asset owners. Standard reports include: monthly and annual footfall trends (leading indicator of mall health), year-over-year comparisons (quantified growth or decline), zone performance (identifying areas needing repositioning), marketing campaign impact (ROI of promotional investments), and market benchmarks (relative performance against comparable malls). Reports are exported to PDF, Excel, or connected via API to real estate reporting platforms. Major real estate consultancies use Flame data in their asset valuation reports, where footfall trends are a key indicator of commercial asset value.
How does it help optimize the tenant mix?
Flame's analytics reveals how traffic flows between different store types and which tenant combinations generate the most cross-traffic. Key data for mix optimization: flow analysis by category (do fashion visitors then go to food service?), anchor effect (how much traffic each anchor tenant generates for surrounding stores), cold-zone analysis (low-traffic areas needing a destination tenant), and category synergy (which store types benefit from proximity). When a large shopping mall needed to reposition a floor with high tenant turnover, Flame data showed that food-service tenants on that floor generated 3x more cross-traffic than fashion stores, guiding the re-leasing strategy. Similar zone-flow analysis helps optimize space planning in other physical spaces.
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