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Advancements in data science and modeling techniques have led to the development of crowd solutions that analyze purchase motivations, engagement rates, and personal preferences from physical movements. Crowd analytics is evolving rapidly beyond camera-based surveillance and video analytics toward intelligent analytics that provides customers with accurate, real-time, and actionable insights that can drive decision making. Deep learning and computer vision will improve accuracy in movement detection, enabling customers to detect, track, and analyze crowd movements confidently and reliably. Data collection is also increasingly autonomous, with the ability to collect and analyze anonymous viewer data without human intervention.

The COVID-19 pandemic has reinforced the need for city, state, and federal governments to consider new and innovative solutions to improve public safety. Crowd analytics will form a key element of future smart cities, where occupancy monitoring and social distancing gain importance besides ensuring public safety, hygiene, and security. With increasing urbanization, crowd management will become a prerequisite at transportation terminals, such as airports, railway stations, and large community spaces like stadiums and monuments.

Crowd behavior tracking and audience analytics have broad applications across key industries, including in-store retail, smart cities, hospitality, and transportation. For instance, in retail, crowd behavior analytics can improve the customer experience through the analysis of key parameters, such as demographics, footfall, and emotional and behavioral patterns.