@Fire Bull
I actually think it is not only pointless to do this sort of analysis when the numbers cannot be confirmed, I think it is actively dangerous and at high risk of giving completely spurious results.
For instance I object to the number of users.
I am not sure of the sales model exactly- but given there are only 300 enterprises, perhaps its selling to enterprise at a much higher ARPU than implied.
The really big problem is the increase in churn. This is a red flag and it should be explored.
Churn actually decays proportionally to growth. For many businesses- new cutomers are likely to switch out within 12 months, and those that stay are "sticky". As such as you continue growing a proportion of the new customers will continually churn but the longer stayers stick around. As growth slows, there should be a corresponding drop in churn.
For instance Xeros numbers (on a half-yearly basis) are shown in the table below- the churn decay is remarkably constant on my model (2nd row) compared to actual numbers (5th row).
In this case starting churn at approx 24 percent...to 7.4% predicted by model (but actually 13% and lower growth decay than predicted!)...
Anyway increasing churn can rapidly spiral out of control- it means that you are chasing revenue and that perhaps product market fit is not quite there...
Column 1 Column 2 Column 3 Column 4 Column 5 Column 6 Column 7 Column 8 Column 9 Column 10 Column 11 Column 12 Column 13 Column 14 Column 15 Column 16 Column 17 Column 18 Column 19 Column 20 Column 21 0 Year 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1 51300 77976 114233 161726 221849 295591 383444 485340 600657 728257 866578 1013735 1167643 1326133 1487061 1648391 1808269 2 52% 46% 42% 37% 33% 30% 27% 24% 21% 19% 17% 15% 14% 12% 11% 10% 3 Initial subscriber number 51300 26676 36257 47494 60122 73743 87853 101896 115316 127601 138321 147157 153908 158490 160928 161331 159877 4 Initial growth 76% ACTUAL 78 112 157 210 284 371 475 593 717 862 1035 1199 5 Initial churn 23.7% 6 Growth decay 0.896 7 Churn decay 0.9
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