This article is published in the News category.
People are creatures of habit. Despite the extent to which we see ourselves fitting in this description, making a prediction on people’s behaviour may not be as easy as we think. In the business of Crowd Analytics, however, being able to estimate and even foresee the number of people is essential to better organise events, arrange services, prepare offers. DFRC is now releasing a new feature to help its customers doing precisely that.
LBASense is a system of sensors capable of passively detecting anonymous signals from mobile phones. Such data is used to measure attendance at events, understand mobility between areas and analyse visits’ patterns. Based on such a large amount of data, LBASense can now elaborate the expected footfall figures: these values are processed and visualised in the so-called Prediction Module.
For any given day, the Prediction Module shows the expected maximum and minimum values of people’s counting, derived from collected historical data on one hand and taking into account external factors on the other: for instance, this allows considering the effect of weekends or public holidays, typically very different in footfall. In a self-learning approach, then, the Prediction module reports the attendance observed for the current day with respect to those limits: it suggests whether today’s crowd is above, below or on average, with a colour code of green, red or blue, respectively.
Observing the past is the best way to predict the future. For many mall managers and event organisers, knowing the expected crowd allows to arrange shifts and dynamically adapt service and offer at need. The footfall prediction can also be used as a benchmark for the site’s performance: assessing whether the crowd is greater or lesser than usual gives an indication about how attractive the premises or event is, how effective a campaign is being. etc. The Prediction Module is a powerful tool to test and identify the best combination of work assistants and offers, for an improved overall service.
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