Reading queues and wait times in bank branches with existing CCTV
You can measure queues and wait times in bank branches with the CCTV already installed. The software counts people in the queue area and tracks how long they stay, which gives you queue length and wait by hour, with no new hardware at the counter.
The result is a plain answer to a question most head offices cannot answer today: how long do customers actually wait?
Why do branch managers struggle to see waits?
Most branches count transactions. A token system tells you how many customers were served. It does not tell you how many walked in, looked at the line and left, or how long the average person stood waiting.
Surveys help a little, but they capture a few opinions on a few days. A manager on the floor knows the branch feels busy at certain times. A regional head looking at forty branches cannot see that.
So decisions get made on instinct. Staff are rostered the same way every day, even though customers do not arrive that way.
What can existing cameras tell you about queues?
A branch camera already sees the queue area. Analytics turn that view into numbers.
- Queue length. How many people are waiting at a given moment.
- Wait time. How long a person stays in the queue zone before reaching the counter.
- Peak hours. When the line builds, by hour and by day of the week.
- Walk-aways. People who enter the queue area and leave without being served, often called bounce.
For example, say a branch seems fine on most days but the line stretches to the door every second Friday and during the first week of the month. Without data that is a hunch. With data, it is a pattern you can staff against.
How Sakshi turns queues into decisions
Sakshi works on CCTV the branch already has. It measures who walks in, where they go and how long they stay. In a bank, those same measures describe the queue: how many are waiting, for how long, and at which hour it gets worse.
That supports a few concrete choices.
- Rostering. Match counter staff to the hours when people actually arrive. Our article on staffing to real traffic goes deeper on this for retail, and the logic is the same.
- Counter mix. If cash deposits clog the line at certain hours, you may open a dedicated counter then.
- Layout. If the queue blocks the entrance or runs into the ATM lobby, you can see it and adjust the floor plan.
- Comparison. Put similar branches side by side and see which handle peaks well and which do not.
Sakshi works with counts and flows, not with identifying customers. For the thinking behind that, read about privacy-first video analytics.
What to watch out for
Queue data tells you what is happening. It does not tell you why. A long wait could mean too few staff, a slow process or a system outage. Pair the numbers with a conversation with the branch manager.
Also define what a long wait means for you before you start. Pick a number your service team agrees on, and judge branches against it. A fair target for a small rural branch is different from a city one.
Once you have the numbers across a network, the next step is asking questions of them. See asking your branch network questions in plain language. You can also read more about Sakshi.
How to get started
- Choose a few branches with known queue problems. Ask regional managers which ones get the most complaints.
- Confirm camera coverage of the queue area. The counter line and the waiting zone should be visible on at least one feed.
- Agree what counts as a long wait. Set one target for the pilot so results are easy to read.
- Collect two to four weeks of data. Look at wait by hour and by day, and note the peaks.
- Change one thing and measure again. Shift a roster or open a counter at the peak hour, then check whether waits fell.