Getting Smarter About Queue Management = Money in the Bank

Ralph Crabtree, CTO of Brickstream

According to a survey of consumers conducted by Retail Systems Research, nearly 20% of shoppers will dump their carts if the check out line at the register is too long. This translates into a lot of lost sales, leaving little doubt that when it comes to register service and managing customer wait times, time really is money. Even if customers do stick it out in line (or try their luck with another) will they return again if their in-store experience has been frustrating, annoying or in any way less than pleasant? In a multichannel world, it’s too easy for customers to shop online or on their smart phones instead.

Today’s busy consumers hate to wait. And while retail and supermarket chains may not be able to eliminate register lines completely, they can take steps to make the in-store shopping experience more efficient for their customers. Here are several best practices for taking advantage of in-store analytics technologies to more proactively manage queues so that lines keep moving, and customers leave with a purchase in hand rather than a bad impression.

  • Capture accurate data. The old adage says you can’t manage what you can’t measure, but you’ll only manage poorly if your measurements are inaccurate. In order to minimize wait times (without over staffing), stores need to know things like: How many people are shopping?; How long are those lined up in register queues waiting?; and; Are people leaving before completing a transaction?
     
  • A clear understanding of what’s happening minute-by-minute is critical — and this requires real-time metrics collected at store entrances and exits and at register lanes. The challenge is that capturing customer behavior data in real-world environments is more susceptible to error than tracking data online, so retailers need to be sure they are getting accurate information.
     
  • When evaluating in-store analytics solutions, consider whether the technology can: deal with environmental factors such as low light, bright sunshine, shiny floors, high ceilings and high traffic volumes; distinguish between adults and children; measure queue dwell times (which are longer than at displays/aisles); track people accurately within the queue area; and, finally, distinguish between humans and inanimate objects like carts.
     
  • Consider, too, how and where the analysis of video data captured within the store takes place, as it can be lost when processed remotely. Devices that process video in real-time offer reliability and stability in data capture and reporting.

For stores where families shop, it is especially important to be able to distinguish between adults and children. If children are treated as individual shoppers rather than counted as part of a shopping unit, the queue wait time measurement will be inflated. For example, if a family of five are waiting in line, they should be counted as one shopping unit, not five shoppers. Why? Because counting each child as a separate shopping unit will overestimate the forecast of how long it will take for the entire line to be served, leading to inaccurate workforce planning and other bad service decisions. Trust in reported metrics is vital to an accurate assessment of store performance.

  • Measure actual shoppers – not averages. It is also important to measure actual wait times, not average wait times. Customers care about their waits – not the average for all the store visitors who were measured during the day or for a specific period of time.
     
  • Averaging masks what each customer experienced. For example, hitting the target average can be the result of half of the customers being served quickly and half waiting too long. This means that half of your customers did not have a good experience! Measuring individual wait times is the only way to know if some customers are waiting too long, which in turn leads to abandoned carts, dissatisfaction and bad word of mouth. As with any metric, it pays to identify the most revealing measure, so that your KPIs are providing truly useful information.
     
  • Turn insight into action. Businesses often plan ahead with tools used to schedule employees to lanes based on historical trends. But when this historical information is supported by accurate real-time data (such as live measurements of individual customer wait times), stores can more effectively “manage in the moment.” Depending on what is happening in real-time, managers can decide when to open or close lanes and are better able to deploy staff when and where they are most needed, whether it is to the registers, or stocking, or working the floor.
     
  • Queue wait time data can also be used to set customer expectations. For example, some customers interpret long lines as long check out times — even when queues are moving quickly. With in-store analytics, businesses can predict and share estimated wait times with their customers, who are less likely to get frustrated when they know what to expect.
     
  • Add wireless. Wi-Fi and Bluetooth Low Energy (BLE) sensors, as well as other mobile technologies, offer additional ways for retailers to both capture more granular in-store behavior data (from shoppers with smartphones, for example) and to further improve service and wait times. For example, mobile POS terminals can be used to complete transactions throughout the store, which reduces traffic at primary registers and allows staff already helping customers to close the sale on the spot.
     
  • Or imagine feeding in-store analytics into a mobile app that gives customers wait times at the store locations closest to them before they even leave their driveway, or using Wi-Fi signals to sense when a customer is stopped and in need of assistance. These are just some of the innovative possibilities presented by the intersection of in-store analytics and consumer mobile devices.

Your shelves are well-stocked and customers are ready to buy. Don’t lose them at the register with long wait times that could be avoided. Innovative in-store analytics can help retail and supermarket chains more effectively deploy labor, reduce register wait times and improve the overall customer experience. And that’s as good as money in the bank.

Ralph Crabtree is CTO and co-founder of Brickstream, the in-store analytics market leader. He is an expert in the development and application of technologies for brick-and-mortar analytics.


 More Web Exclusives/Guest Commentaries

Recommended stories

Login or Register to post a comment.