GrayMatter is planning to showcase its airport analytics (AA+), concessionaire management (Store Sense) and Car Park Revenue Management (CPRM) products at Passenger Terminal EXPO 2020.
GrayMatter’s airport analytics (AA+) product is a pre-built enterprise-wide analytics solution that covers 10 modules (including commercial, operations, car park revenue, ground handling, finance and airline marketing), 48 sub-modules, 140+ dashboards, 700+ KPIs, 20+ predictive models and numerous data systems for airport operators. AA+ enables business users to undertake historical data analysis with role-based, intuitive dashboards to track operational performance, enhance commercial business, attract better airlines to the airport, maximize car parking revenue, reduce infrastructure maintenance cost, and elevate the performance of people and equipment by predicting maintenance schedules. AA+ drives real-time data-driven actions to optimize operational performance, reducing passenger wait time, queue busting at various checkpoints, and forecasting passenger and baggage flow. Its high-end statistical models provide analysts at airports with what-if modeling for revenue and operations, projections, forecasts and video analytics for energy usage optimization, traffic flow and many other scenarios.
GrayMatter’s concessionaire management (Store Sense) product enhances store revenues and elevates the shopper experience with a mixture of solutions covering the following areas: real-time sales data capture, business intelligence and analytics, contract management and billing, digital campaigns, digital receipts, digital process automation, employee productivity and communication, competition benchmarking, loyalty programs and store traffic profiling.
Car Park Revenue Management (CPRM) maximizes revenues at airport parking lots by demand forecasting and price optimization. CPRM is a demand-driven dynamic pricing system consisting of machine learning-enabled future price recommendation, supported by dashboards and reports providing historical analysis of bookings and prices as well as representation of outcomes of the data science models. The solution takes into consideration the following factors: length of stay, day of the week, competitor pricing, booking pace, traffic trend, seasonality, special events, digital promotions and price elasticity with demand.