..."show appointments booked more than [custom] hours ago" "show appts with no phone number" "show appts with no email" "show appts that have not prepaid" "show appts that have future booking" "show appts that were booked by [staff member]"

Super useful for managers to be aware of things and not manually sift through visual data.

More advanced filters that would require limited about of machine learning would be:
"show appts with no show likeliness w/ [reason]" [reason]:no showed before; usually confirms
"show appts that should be booked at a different time in order to optimize revenue generation w/ [reason]" [reason]: explain why appt and temporal alignment or staff selected are poor revenue generation contrasted with mean rates per time for select staff
"show appts that have historically accumulated large revenue for salon"
"show appts that client is missing certain information on their account"
"show appts that client is likely a duplicate of another client"
"show appts that are normally booked with other client appt and specify that example" This one helps businesses call clients to identify whether an intended combo appt wasn't booked and should be!