Latest trends in Retail Queue Management
Customers are gearing towards e-commerce. COVID-19 once again highlighted this experience. Brick and mortar retailers have some catching up to do – latest research shows that nearly 90% of customers have left the store as a result of long queues.
However, queues regarded as a necessary evil of brick and mortar retailing, can be efficiently managed by harnessing Computer Vision and AI technologies.
To improve physical retail experience retailers are turning to smart queue management platforms. Reduced wait time, minimized cart abandonment and increased customer satisfaction – these perks are the result of efficient queue management systems.
So how does it work and why should you try it for your business?
Retail queue management: AI powered solutions
AI based queue management systems are already making an impact in retail businesses all over the world. Retailers choose queue management due to various important factors that improves and optimises their business processes. Here’s how:
- Queue management platforms provide real time data about customer flow entering the store as well as at the check-out area. By detecting an increase of customer inflow, the platform can predict queues forming at the checkout in the coming minutes. Every 5-15 minute period system notifies store managers about queues forming. This helps to employ additional cashiers and prevent queue formation.
- Queue management systems are based on AI video analytics. System can be easily integrated using in store security cameras. Queue management can be deployed as a cloud platform meaning there are no additional infrastructure investments.
- By utilizing given data store managers can optimize workforce, identify cart abandonment situations and take actions to prevent it from happening in the future.
- Queue management systems provide information on peak hours, average waiting time, customer flow etc. By analyzing visitor trends the platform can predict visitor flows and queue formation in the future. This information is essential for employee shift planning.
Ensuring checkouts efficiency
Queueing up at the checkout is the last touch point a retailer has with customers. According to research, 70% of customers are less likely to return to a store if they experience long queues on at least one occasion.
Queue management systems let retailers improve their customer experience by optimizing cashiers available throughout the day in order to prevent formation of queues.
The granularity of data provided by the queue management platform enables retailers to plan employee shifts more efficiently. It can notify about the predicted formation of queues, inviting extra cashiers to the checkout before the queueing occurs.
In addition, it also helps to identify the checkout utilization rate. The problem with checkout efficiency not only lies with understaffed counters. It also relates to checkout overstaffing. This means that there might be too many cashiers working at checkout while there are few customers in the store. By measuring the cashier occupancy rate, the platform can also inform the store manager in real time that the checkout employees can be utilized elsewhere than there is a low customer turnout.
Real time resource management
Retailers are obsessed with precision metrics. Items scanned per minute by the cashier at the checkout is one of them. However, it is a direct result of customer footfall – not only cashier performance.
The customer turnout predictions as well as real time footfall monitoring helps to optimize staff resources. A sudden increase in customer footfall – more employees to the checkout. Low customer turnout – the cashiers can be employed elsewhere in the store.
On the macro level the software allows to manage employee shifts. On the micro level, it allows unparalleled real time staff resource management that only AI powered Computer Vision platforms can provide.
Measuring cart abandonment rate
Less time in queues – more time for shopping! In store analytics showed that clients spend 20 minutes on average in a store. It means that every minute spent in a waiting line converts to one minute less to make the potential purchase.
There is a lot of research on this topic, but the consensus is clear – long queues result in cart abandonment. 7 people queing up is the tipping point – any longer and most shoppers won’t bother joining it. After 9 minutes, shoppers are likely to give up queuing and leave empty handed (other research says as little 6 minutes). 86% of consumers will avoid a store if they think that the queue is too long.
Queue management not only prevents queues from forming. It also allows us to objectively measure cart abandonment rate. The platform can identify cart abandonment action and correlate it to the queue length (e.g. when 8+ people are queuing up, the rate of cart abandonment increases exponentially). By corresponding this value with the average value of the shopping cart, retailers can precisely estimate the sales value lost, as well as measure this rate of extra cashier wage costs (identifying the moments when extra staffing is justified by sales value lost).