NRF: Is video analytics the solution to ending long checkout lines?
Customer disdain for standing in long lines can lead them to leave the store before making a purchase or even avoid entering a store entirely. A session yesterday at NRF’s Big Show explored how the European convenience chain, Rossmann, has reduced wait times with the implementation of a predictive video analytics solution, which aims to stop lines before they form.
The solution used by Rossman incorporated facial recognition technology through its CCTV system to identify and count entering and exiting customers. Stores identify the age and gender of shoppers and, in conjunction with statistics on average dwell time, determine when a given customer is likely to head to the checkout. The system sends text messages to associates or makes PA announcements when customers, statistically speaking, are likely to be on the way to check out their purchases.
Rossman has found that wait times of eight minutes of more have been virtually eliminated, while those more than five minutes in length have been cut by some 70 percent.
When using facial recognition is used, however, retailers will undoubtedly face shopper concerns about privacy. The service provider, Ultinous, claims that while its system is able to recognize repeat customers, it does so without personally identifying them.
DISCUSSION QUESTIONS: How important is controlling checkout line length to customer satisfaction with stores? What solutions, human and technological, do you think hold the greatest potential for addressing this pain point?