The COVID pandemic and associated lockdown is affecting the way bricks-and-mortar retailers think about their current and future operations. Even as restrictions are lifted, distancing rules and occupancy limits are going to affect their ability to recover. Current crisis conditions have rapidly sped up conversations about the future of physical retail in an age of ever dominating e-commerce. With the next generation of retail analytics becoming affordable, it is an increasingly attractive option for facing the retail challenges posed by COVID.
Retail and AI on the Eve of COVID
The Microsoft Report on AI in Australian Retail identified that although 77% of decision makers in retail believe that AI will be a very important factor to their competitiveness over the next 3 years, only 33% of them have actually started to take steps toward achieving those aims. This is low compared to other industries even though studies show that retail stands to benefit the most from AI adoption, with global total value add between $400-800 billion USD.
Even before the COVID pandemic hit, stores were already downsizing and reducing their physical presence. A growing amount of retailers, over 10% stated that they were actively working to reduce the amount of stores that they operated. The trend was already towards downsizing and rationalisation of retail spaces. The shocks to physical retail caused by the pandemic (Australian retail turnover went down by 18% in April 2020) are speeding up these trends and emphasising the need to think of how to optimise, do more with less and make the most out of fewer stores and smaller spaces. This is an opportunity for the next generation of retail analytics to come into its own.
The Retail Recovery Protocol
The National Retail Association published a report in June 2020 called the COVID-19 Retail Recovery Protocol. It is a statement from across a range of sectors which outlines the principles which will guide the retail industry’s approaches to reopening of physical locations.
Consistent with advice from public health authorities, the Protocol advises facilitating 1.5 meter distance between customers in a space and no more than one customer per four square meters of store space. Options to implement this are listed as: regulating access points and monitoring customer counts at relevant entrances. These steps would be challenging to implement during normal times, not to mention during exceptional circumstances in which the stakes are so high.
How Does IoT and AI Retail Analytics Help During COVID?
Sensors applied to machine learning pipelines have never been more accessible to retail store owners. One device for measuring traffic covers an area of around 12 square meters and can be attained for a little over a thousand Australian dollars. The effective range of the sensor can be even higher if the retail space has higher ceilings.
Managing Occupancy Restrictions
Depending on the store layout it may not be easy or straightforward for staff to know if occupancy restrictions are being violated. Machine vision filters applied to data feeds from in-store sensors are now capable of reliably identifying individuals, flagging when the same person enters and exists a store. These systems can be set up to trigger real-time alerts to managers, staff or even in-store displays when a threshold is crossed. The reliable automation of occupancy checking lessens the need for employees to conduct physical checks, potentially reducing unnecessary exposure.
Heatmapping Distancing Violations
Now, more than ever it is important for managers to set up retail spaces in order to facilitate the most distance between customers in order to maintain the 1.5 meter distancing requirement outlined in the Retail Recovery Protocol. This is challenging to juggle for retailers, since they benefit from utilising any available space for the presentation of goods.
Due to being able to accurately track the movement of individuals through the store, this data can be aggregated. It can then be presented as an over-time visual representation of how customers as a whole interact with the physical space. By applying some rules to this data, it can be used to identify which areas of the store have the most violations of the social distancing rule. This feedback can be used to redesign the topography of the store to open up and provide wider clearance for the areas which need it most.
Additionally, heatmaps of which areas are the most trafficked can be used to identify where particular care and effort needs to be devoted during cleaning and disinfection.
In order to reduce exposure, retailers need to decrease store/customer throughput time. With fewer customers being allowed to be in store at any given time, it is particularly important to ensure that they are in and out as fast as possible. One way of potentially achieving this in some stores is to implement more effective queue management. Enabling automatic real-time alerts for when queues start to form can reduce congestion in stores and ensure that points-of-sale can be opened with no delays in order to reduce customer time spent in the store.
If you want to know how you can implement these tools in your store, contact us today.
If you’d like to know more about how these new technologies are transforming traditional storefront-based retailers, watch the free webinar by BizData which covers a range of real-world examples.