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Crowd Analytics - For Enhancing the Security, Safety and Revenue

January, 2019

Crowd analytics is all about collecting, combining and analyzing the information which is related to the people and their behaviour. Crowd analysis includes 4 phases: crowd motion detection, crowd tracking, crowd density estimation, and crowd behaviour understanding. The most demanding applications of crowd analytics includes security and safety and enhanced revenue.

Security and Safety

With the rising smart cities development around the world, government of various countries are also taking several initiatives and spending heavily towards security and safety of public at crowded places such as retail malls, transport terminals, entertainment venues, and others for detecting suspicious activities. Crowd analytics helps in automatic detection of an anomalous crowd behaviour with minimal human involvement resulting in improved crowd management. Security and safety is the most important application of crowd analytics which is useful in almost all the verticals for enhancing the public safety by identifying the disturbances at crowded places.

Example - AGT international, an advanced IoT solution provider offers AGT’s StreetSMART Solution which provides real-time intelligence and analytics to law enforcement authorities for helping to reduce crimes and improving officer safety and efficiency.

Enhanced Revenue and Profit

Crowd analytics enables various organizations in enhancing their revenue and profits by analyzing people reactions which can help them in better up selling and cross selling. While analyzing reactions of crowd in queues or at counters helps in assessing the quality of service provided or to make amends spontaneously. By knowing about footfall, entrance and stay duration of customers in a peak season and normal time can help in optimizing staffing decisions which lead towards enhanced revenue. By capturing the details such as dwell time and number of returning customers changes can be made and productivity can be increased.

Example - TopShop a British fashion retailer uses Walkbase for knowing about conversions, customer’s footfall, and customer path enabled Topshop to precisely see the pathways from fitting rooms to checkout, and dwell time. TopShop then connected this data with POS data for knowing real-time conversion ratios from each fitting room. This analysis resulted in TopShop investing in increasing staffing and new fitting room technology.

– Sonam Chawla,
Research Analyst,
Infoholic Research