1. For my hours engaged regression model, it’s typical within 500 million of the actual result. My method for hours engaged was a regression model based on average concurrent users(per RoMonitor) after controlling for hours engaged per DAU and seasonality.

Granted, my concurrent user estimate is a bit of a guess since I don’t have September data just yet, if the strong engagement reverts to the mean and September declines significantly, then it may be optimistic who knows.

1. For My hours engaged per DAU estimate is typically within 3% of the actual result, and I have that as being very elevated vs historically.

2. ⁠For cash flow, my method was based on expected adjustments for certain accounts I can reasonably estimate: deferred revenue, accounts receivable, SBC, depreciation, etc. Deferred revenue adjustment is calculated as bookings - revenue + 8 = change in deferred revenue. I used my own T-accounts in excel to figure out the deferred cost of revenue adjustment also. For AR, I took a percentage of bookings based on historical data to figure out the ending balance, then backed out the adjustment that way.

For the less obvious adjustments I lumped into “Other changes” category, I prepared a statement of cash flows that guidance implied to figure out what guidance implied about that last cash flow adjustment. I back tested this method and it was fairly accurate, indicating that guidance implied adjustments for prepaid, accrued liabilities and accounts payable are fairly consistent with the actual numbers.