Using the clustering method to intelligently categorise stores based on their common traits and characteristics helps customers uncover patterns, learn about individual stores, and tailor better strategic planning to each market segment.
AI predictive modelling utilises historical and diverse datasets (sales, store, staff, weather, demographics and etc.) to accurately forecast future data patterns and trends, aiding customers in advanced planning and decision-making.
FootfallCam AI features a rule engine, allowing customers to create custom business rules and trigger “Rule-Based” and “AI-Triggered” alerts, ensuring on-the-spot decisions for immediate action and meeting SLAs.
By leveraging your existing dataset and employing customer segment-specific AI data modelling, businesses can analyse the influence of various touchpoints and strategies on footfall, sales and customer behaviours. This facilitates optimised resource allocation and budgeting, leading to more targeted business initiatives and workflow optimisation.
Challenges: Relying solely on collected data without AI has limited insight and analysis capabilities, hindering decision-making, and missing out on predictive and prescriptive analytics for better strategic planning and optimisation.
With FootfallCam: The AI-powered analytics enables businesses to customise business rules with intelligent business metrics for their workflow, providing actionable insights for SLA improvements.
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