Profit Signals measures what a room actually returned. Bar and kitchen sales come from your POS, narrowed to doors-to-last-call and divided by the people who scanned in at the door. Ad-driven ticket sales are matched to tagged links in your ticketing platform.
Built for the people who run the room
Show yield
The Ridgeline Boys, Sat · doors 7:00
Ad spend stops before the window closes. Nothing to divide.
Figures redacted. The labels are the product.
Reads from the systems you already run
Profit Signals is the system of record for nothing. Your POS still owns sales, your ticketing platform still owns tickets, and your ad account still owns spend. This reads them, lines them up against the same clock, and reports what they add up to.
Most reporting tools tell you how they produce a number. This is also a list of the conditions under which they do not produce one.
Sales come from the POS. Tickets, orders and door scans come from the ticketing platform. Spend comes from the ad account. Profit Signals is the system of record for none of it.
Doors to last call, taken from the ticketing platform and the POS — not the calendar day. A Thursday show does not get credit for Thursday lunch.
The denominator is scanned attendance at the door. Not tickets sold, not capacity, not an assumed no-show rate.
Ad spend, what the platform claims, and what we can verify against tagged links are all restricted to one shared window. Only then is anything divided.
Bar and kitchen sales pulled from the POS for the show hours only, divided by scanned attendance. No industry per-head, no capacity estimate, no rule of thumb standing in for a measurement.
Each ad-driven sale is matched to a tagged referral link in your ticketing platform. What the ad platform claims sits beside the verified count, clearly labelled, and never in place of it.
Each number traces to a row in a system you already run. Nothing is seeded from a previous show, defaulted to a placeholder, or carried forward because the current value was missing.
What we will not show you
Nothing is modelled, inferred, defaulted or backfilled. A figure we could not measure stays absent, and says so.
What the ad platform claims and what we verified against tagged links are two separate rows, always labelled. They are never averaged into one number.
When sales, scans and spend do not cover the same dates, nothing is divided. You get the reason and the specific thing that would fix it.
This means the product will sometimes show you nothing where you wanted a number. That is the trade. You have already been handed a confident figure that turned out to be wrong; this is what it costs not to be handed another one.
We have measured one room end to end. That is why there is not a single result on this page.
This was built inside a working music venue, because the question that mattered on a Monday morning — did Saturday make money — could not be answered by any of the four systems already running there. The ticketing platform knew what sold. The POS knew what the bar took. The ad account had an opinion about both. None of them knew who actually walked through the door, and none of them agreed on what a week was.
The hard part was never the arithmetic. It was refusing to fill the gaps — not defaulting an unknown per-head to an industry average, not treating a ticket sold as a body in the room, not dividing six weeks of ad spend by three days of tracked sales because the result looked plausible. Every one of those shortcuts produces a confident number, and every one of them is wrong in the direction that flatters you.
When there are enough measured rooms to say something general, we will publish it here with the method attached. Until then this page describes the instrument and nothing else.
The method is published in full, including what it refuses to calculate and why. Read it before you decide whether to trust it.
