When was the last time you enjoyed waiting in line? It’s hardly a great part of the visitor experience – though for many venues, an inevitable one. The top 25 amusement and theme parks worldwide experienced 3.3% attendance growth in last year, collectively welcoming 252 million visitors^.
With growing visitation, guests are spending more of their time in queues. From buying tickets and waiting for rides or experiences to purchasing food and souvenirs. It’s one of the leading causes of visitor complaint which reduces visitor outcomes and impacts the likelihood of a visitor to come back.
As Peter Drucker famously said, ‘What gets measured, gets managed’. But in this case, that’s easier said than done. Measuring queues (or more importantly, wait times) is no easy feat. Luckily, there are a few different methods available: analog, pass, camera, presence and network methods.
#1 The analog method
This method is an old favorite that requires no technology. It involves giving the odd visitor entering the back of the line an object with the time recorded against it, then measuring the time taken for them to make it to the front of the line. A creative hack is to use a fun object that relates to your venue, such as a deaccessioned item from the museum’s collection. Offer the visitor a prize on completion, like a discount code for the shop or a voucher for the cafe.
Over time, you can record queue times, visually mark out how long a typical wait time is depending on how long the queue gets and have staff eyeball queue measures instead (this only works if queue throughput is stable). To rely on approximating queue length from your old measures, you’ll need to keep the same queue snake structure rather than moving the line formation about. Problem is, other than the work required, you’ll never have a real time view and only ever a snapshot – at best, your data will be as old as your queue is long.
#2 The pass method
The pass method requires the visitor to be issued a ticket when they enter the queue, then have the ticket scanned when they exit the queue, providing a difference between the sold versus redeemed status time stamps. If the ticket is an advance pass, you may be able to scan the ticket multiple times and record the difference.
#3 The camera method
This involves a special overhead camera designed to detect presence times in a small area. These typically work best for small volume retail checkouts indoors, as opposed to the typical crowds forming lengthy queues in a visitor attraction, especially at entry.
#4 The presence method
The presence method uses location analytics capable WiFi network to listen for signals from visitors’ mobile devices (no app required), measuring dwell time in the zone. The WiFi network access points in the queue area need to be tuned for dwell time and calibrated for data integrity against a manual test (such as the analog method above).
Whilst other positioning technologies such as Bluetooth can be used in conjunction with a mobile app, given most apps have a low penetration rate of under 5% it’s unlikely to be a reliable sample size for monitoring. Neither will work well if your queue area is mixed with other visitor behaviors, such as being next to an exhibition, alongside a cafe or where you have multiple queues for different experiences next to each other.
#5 The network method
The network method borrows the technique used to measure traffic on freeways. It requires automated footfall counters at the beginning and end of the line, statistically calculating the time spent between inputs and outputs.
If you can make the hardware investment and have the site structure to fix the entrance and exit of your queue, this method is likely to be the most accurate, though will have a slight lag which may be a problem if your queue is growing or shrinking rapidly. An additional benefit is that this method will also measure throughput – the performance of the line.
Deciding which method suits you best will depend on three factors:
- Your visitor experience (such as if the queue is for entry or internal experiences like waiting for a ride, or if passes are involved)
- The site structure around your queue area (such as if your queue is outdoors, or if it has defined entry and exit points)
- The technology you have available (or budget for)
What you plan on using this data for is also important, as the frequency of your data should be driven by how often you’re making decisions from it. If you’re wanting to get a rough guide to baseline and evaluate visitor experience against, an analog method will do just fine. If you need real time updates to adjust staffing through the day or communicate wait time expectations to visitors, an automated method will be required.