how do you handle mismatched booking dates in your revenue forecasting
been dealing with this for a while now and it keeps coming up. our system logs everything by booking date but revenue doesn't recognize until the service is actually, delivered, so our OTB view ends up looking weirdly flat even when we know demand is building. trying to construct a clean 12-month forward view feels like patching holes in a leaky boat. we've been experimenting with pace-based inputs and pickup curves keyed to service date rather than just extrapolating raw OTB, and it's definitely more stable when booking behavior shifts. the tradeoff is that the reconciliation to get there is operationally pretty heavy, especially when you're stress-testing assumptions instead of just waiting for things to normalize. the trickier issue for us right now is that historical comps have gotten a lot less reliable as a clean baseline. booking windows have shifted enough that last year's curve doesn't map neatly onto this year's without adjusting for lead-time mix, cancellation patterns, and wash effects. it's not that comps are totally useless, more that you have to do a lot more work to make them meaningful before you can lean on them. curious how other people are handling this in the current environment. are you running separate models by horizon stage, doing some kind of weighted hybrid that blends pace with, adjusted comps, or have you moved toward date-shifted cohorts to keep booking and service date views cleanly separated? also wondering whether anyone has tightened their forecast update cadence to catch mismatches earlier rather than reconciling them after the fact. what's actually working for you right now?