University of the Witwatersrand (Wits) innovators have developed a WiFi-based solution for managing and monitoring foot count, physical distancing and consumer behaviour in shopping malls.
The solution uses artificial intelligence (AI) to allow retail managers to make future predictions, improve security and deliver intelligence about shopper behaviour to stores.
The inventor of the solution, Dominique Adams, has been awarded R100 000 worth of support from commercialisation company Wits Enterprise towards further developing the innovation.
Development of the WiFi-based, AI-enabled solution began in 2020, shortly after the Covid-19 pandemic emerged in South Africa, triggering the concept for the physical distancing tracking tool.
“Our innovation stemmed from an existing product for WiFi signals to monitor physical distancing in public spaces. We engaged with stakeholders in the retail space to determine whether there was a need for a solution that, with the application of AI technology, would give them valuable insights into consumer behaviour and help them make predictions,” Adams explains.
“Combining WiFi technology with AI allows mall managers using our tool to monitor the complex behaviour of people in real time, as well as predict their future behaviour. This gives stores data-based intelligence, while providing mall management teams with an additional tool for enhancing security.
“Our tool also allows shopping malls to monetise the deployment of WiFi and, importantly, obtain intelligent foot count and other data for strategic planning and evidence-based decision-making.”
Adams says mall managers can measure the overall popularity of stores and see the length of time a shopper spends at a particular store. The data gathered from these measurements are valuable for establishing shopper behaviour and for informed decision-making and planning.
In developing the tool from scratch, the DataConvergance team, consisting of data scientists and AI specialists Xifeng Ruan, Kentaro Hyashi, Finn Stevenson and Benjamin Lieberman, under the leadership of Wits School of Physics’ Professor Bruce Mellado, had to learn how to harness the large amount of data available from WiFi systems, as well as solve challenges around WiFi signals bouncing off walls, the distortion of positioning of people and the complexity of terrains.
“Given the small number of businesses contemplating or already adopting AI, there is a huge opportunity for this solution to be successful in the retail environment. If the solution is successfully implemented, DataConvergence could be a trailblazer in the deployment of AI in the retail sector,” Mellado says.
“The solution will enhance data collection by exploiting and deriving greater benefits from the WiFi availability in malls. This approach enlarges the scope of information available to management and planners to monetise their investment in WiFi as well as optimise in-store strategy development,” Wits Enterprise innovation support manager Dineo Masokoane says.
The next steps for the team are to refine the prototype for the retail environment and develop a pilot in a real retail environment before full and large-scale deployment in shopping malls, Adams says.