A long queue in a shop is often seen as the main irritant. According to IFOP, the average waiting time is 11 minutes, while 38% of visitors start to lose patience after 5 minutes. As a result, 89% of shoppers would abandon a purchase.
Physical shops do not have the same tools as e-commerce sites for acquiring data on consumers. Their first challenge is therefore to put in place tools to help them understand how many people visit their shops, the average time spent, the occupancy rate and the customer journey. The second challenge is to understand the points of friction within the shop, particularly in the checkout areas, so as to accurately determine the abandonment rate, conversion rate and waiting time.
The aim is to enable immediate decisions to be taken to avoid irritants, and hence lost sales.Let's take an example: you notice a peak in footfall at a given time, and the average customer journey time in shop is X minutes. You can estimate and plan predictive alerts to open a checkout a few minutes later, and thus limit basket abandonment. Use historical data for predictive purposes In addition to real-time data, predictive data can be used to estimate waiting times based on periods, weather, days, times, or other factors. This data gives you a better understanding of your customers and their habits. Knowing in advance the off-peak periods and the times when your activity will be most intense means you can anticipate the allocation of resources a few days in advance. The more historical data you have at your disposal, the better your predictions will be, enabling you to activate marketing or operational actions.
Allocating your resources optimally is a complex task, and requires an unparalleled knowledge of your business. For example, during certain off-peak periods, you may find yourself with more staff at the tills than you need. On the other hand, during busy periods, you tend to be understaffed at the checkouts. This is clearly a sub-optimal use of resources, and it has a direct impact on your operational and financial efficiency. It is therefore essential to estimate the number of customers in advance, as well as the acceptable waiting time for your customers (which, according to LSA, should be no more than 3 minutes for Casino(2)), and then analyse all the data to make the best decisions at the right time. Be transparent with your customers Communicating waiting times to your customers is an essential part of keeping them informed, and thus limiting irritants. Once consumers are fully informed, they can make the decision to return to make their purchase at another time, rather than simply abandoning their basket and considering their experience as bad.
Technis Spaces is a data aggregation and analysis platform for space monitoring and optimization.
