Tracking utilization of a customer service counter

Problem Groundlight solves

How many customers are at the checkout counter at certain times of the day? How does that change seasonally?

Retailers are faced with many challenges, one of which are labor shortages and allocating staff effectively. However, for a brick and mortar store, it can be difficult to understand trends in customer behavior, such as busier times of the day or specific days of the week with higher traffic. This can lead to long wait times and crowded areas, resulting in disgruntled customers.

By measuring usage patterns of a service counter, retailers have the opportunity to make data-driven decisions on staff allocation and scheduling.

Current alternatives

To track the utilization of a customer service counter, the following alternatives are available but fall short:

• Count customers manually: You can have staff either count manually at the checkout counter during certain times, or have them fill out a survey of how many customers they recall they had during a given time period. You can also have a staff member go through security cameras to count. However, all of these solutions include human error, incur labor costs, and may not give you accurate results.
• Traditional analytical and forecasting software: By analyzing financial data of purchases made over time, it is possible to extrapolate the usage of a customer service counter as well as the future usage. However, similar to “abandoned carts” for ecommerce businesses, this data doesn’t take into account customers who may have waited and then left without making a purchase, and you cannot calculate wait time.
• Other Traditional Vision Based Systems: These solutions are also expensive because a customized model is needed any time the environment changes, and you need multiple models for each new retail location.

Groundlight’s solution: Retail analytics with computer vision

Groundlight’s computer vision technology identifies when the service desk is being utilized by a customer. It can check the computer vision model every minute, and once an hour, it can print out a summary of the percentage of time that the service counter is in use. At the end of the day, it can email the daily log.

To implement this retail analytics solution, a store would need to install a supported camera near the service counter, ensuring a clear view of the area. The camera would then be connected to a computer running the Groundlight-based application. Groundlight analyzes the store’s captured images from its video cameras, and if a customer is detected at the counter, it is logged as an event. 

Store managers would receive hourly summaries of the service counter usage and a daily log via email, enabling them to make informed decisions to improve store operations and customer experience.

Key features

• Utilizes a store’s existing cameras
• Custom analytics dashboard that shows crucial analytics such as # of customers at certain hours of the day and average wait times
• Daily emailed log with summary of % time that service counter is in use

Impact

• Reduce staff overhead costs: ensure staff are working at peak hours and not during times when there are fewer customers
• Reduce wait times for customers: by properly allocating staff at the busiest times of the day, you can improve customer satisfaction by helping them make their purchases faster
• Improve store layout: retailers can make data-driven decisions about rearranging the store layout to encourage customers to visit the service counter or explore other areas of the store