Say you are an operations manager for a large chain of convenience stores.

Some of your convenience stores are in fuel stations. Some of the C-stores are standalone. The stores are located close to highways and high-traffic areas.

Inside the C-Stores, the chain is focused on serving customers the products they want for a quick on-the-go shopping experience – freshly brewed coffee, clean bathrooms, and various packaged food and snacks for the road.

Speed matters.

The stores have two to three people on-site, depending on the day and the time. There are two cashier stations and one self-service kiosk.

During rush hours, queues can quickly be ten people long. Sometimes, the transactions take longer than 90 seconds, creating long lines.

Your boss, the VP of Operations, wants 99% of the people inside the store to wait less than 3 minutes to pay.

Your job is to manage the queue’s Waiting Time.

🤔Why care about “Waiting Time”?

You probably heard about the cliché of waiting for the kettle to boil. Looking at the pot will not change how long the water takes to reach the boiling point, but it will change your perception.

Wait Time creates friction. But the psychology of queues differs if the line is your First Touch or Last Touch with customers.

In Convenience Stores (C-Stores) and Quick Service Restaurants (QSRs), waiting in line for service is often the first touch with customers.

It means the customer needs to pay before receiving the services or products. Therefore, the experience of waiting in line can increase abandoned behaviors.

Waiting Time is a severe friction point to purchases and excellent customer service.

Retailers care a great deal about managing queues.

😃Behavior Analytics of “Waiting Time.”

When many retailers and solution providers think about Waiting Time, they talk about the Average Waiting Time.

Big Mistake.

The Average Waiting Time metric distorts what matters in the customer experience because the outliers can negate each other.

Some queue management technologies work with objectives such as “One in Front.” As technology advances beyond people counting, time-based metrics better identify the goals for excellent customer service.

Build a distribution of Waiting Time behaviors.

Here’s what you need:

# of people who enter the store with the intent to pay inside. In C-Stores and QSRs, the conversion rate is close to 100%.

The technological challenge is counting the number of people and identifying shopping groups.

In fueling stations, you track if people come inside only to pay for fuel or if they browse the convenience store before checkout.

# of people waiting in the cashier’s queue zone. You define the queue by the number of people standing in the queue zone before paying the cashier. You often visualize #Waiting as a distribution to evaluate service goals.

Queue Wait Time is greater than 30 seconds. You track how long people stay in the queue zone to build a picture of the customer's experience while waiting in line. In this case, the definition of “Waiting” starts when a person stays longer than 30 seconds.

# of people waiting in real-time. The real-time metric differs from #Waiting because it only tracks shopping groups. Most importantly, It serves as the basis for trigger events.

Queue Flow is the number of seconds to exit the queue. When you have limited capacity, the magic metric is Queue Flow.

With only a limited number of cashier stations active, how fast customers exit the queue depends on the service itself.

You deployed the people tracking system and have a baseline of Waiting Times, #Waiting, and Queue Flow behaviors.

Now What?

😎Win-Win-Win Alignment (How to Get 99% Wait Time in Less Than 3 Minutes)

Apple wants you to wait in line for its latest iPhone. Stadiums wish you to stay in a long, fast-moving line to get in and share the excitement of the game.

Not all queues are bad for business.

Be as specific as possible to define what you are trying to achieve.

Most importantly, your objectives must be feasible from an operational and financial point of view.

In this case, the Operation Manager believed customers would tolerate standing in line to pay for fuel and products for up to 3 minutes.

Once triggered, the people tracking system will send alert notifications to the staff’s smartwatches to walk over to the queue and offer those waiting in line the option of paying in the self-service kiosk.

# of alerts to staff on queue waiting time.

You want to understand how big the problem is by counting the number of alerts the system sends to employees.

# of staff in the queue zone (compliance)

The next element is to count how many employees entered the queue zone because you want to track compliance.

Staff Response Time is less than 60 seconds.

To track store compliance, you measure how long it takes for employees to respond to an alert. Technically, it’s a bit complicated, but it helps the two-way communication between corporate and stores.

In this case, the assumption is 60 seconds. Like all assumptions, you should test and identify the best baseline for your stores.

# of alert events with staff compliance.

The objective of the alerts is that employees will walk over to the queue and offer customers the opportunity to use the self-service kiosk.

In other words, you have a start-to-end event. It starts with a trigger, sends an alert, and ends when the employee is in the queue zone. When the event is complete, you get store compliance.

% Waiting to Store Visitors Ratio, in Real-Time.

To understand if you have a queue problem, you track the ratio of people in the queue to people in the store.

While Queue Flow reflects the cashier service from the customer experience point of view, the Waiting-to-Visitors ratio reflects the operational realities of the retailer.

For example,

If you have 25 people in the queue and only four customers in the store, you have a problem. (It happened to a Kmart store in New York City. Both the store and the retailer are long gone).

🤣PEACE for Profit (Start Your In-Store Optimization)

You have a baseline of Waiting Time and Staff Compliance behaviors. Now, you are ready to experiment and identify what works.

The Operations Manager thinks that sending real-time alerts to employees telling them to walk over to the queue and offer customers help with self-service will allow the store to comply with the Waiting Time policy.

The call-to-action is a 90% compliance rate.

The outcome is that 99% of people waiting to pay the cashier will be serviced in less than 3 minutes.

🤑Amplify Store Sales

Waiting Time is a fantastic metric to work with in Queue Management.

Better yet,

To manage the In-Store Customer Experience, focus on Queue Flow, Queue-to-Store Ratio, and a time-based success KPI.

Thanks to Nicolás Guiloff and Camila Lizarazo from FollowUp.

Ronny Max

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Behavior Analytics Academy trains people and teams to increase conversions, sales, and profits in physical environments. Build Profitable Ecosystems.

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