Data intelligence for supermarkets

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.

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.

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.

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.

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.

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.

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.

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.