Very interesting question, and one that could well be a consulting project on its own (in fact I did that for a bank’s agencies). There are multiple ways of solving it, but there’s one unavoidable truth: trade-off between overcapacity & queueing time, which affects negatively customers’ shopping experience.
Assuming a normal distribution of customers throughout the day, if you draw a vertical line in the middle of it, what is above the curve on the left is overcapacity and the tail on the right represents the queued customers.
Now, let’s say on average we have 50 customers and the st. deviation is 20, the CX starts to decrease after 10min and 3min per customer attended.
So, if Walmart wants to have a positive CX in 83% of times (up until +1 st. dev), it will need (50+20)/(8/3+1), where 1 is the customer being attended. Thus, you end up ~19 checkouts.
If you replace 8min by 10min, you get 16, so it is a negative hyperbolic curve.
Additionally, you have to consider the following factors that affect this answer:
- Distribution of customers over the days – in real life I am expecting a non-symmetrical distribution function
- Profile of customers – are they digital-savvy and can do a DYI? What about Amazon’s Go tech?
- What is the time after which the shopping exp will deteriorate?
- How much time do tellers take to process the items per customer?
- What is the opportunity cost of dedicating area to checkouts?
- What is the propensity to buy stuff per minute spent queueing?