Fossil fuel dependency
PublicAbebe Silva(@plg_abebe_silva)1w ago
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Adam Kingsley The transformation from local currency to USD uses the historical exchange rate at the mid-point of each reporting period. However, for volatile currencies, the mid-point rate can differ materially from period-end rates, affecting comparability. Should we disclose the exchange rate source and date used for each observation?
What's the source of that discontinuity on day 45?
Your point about the compounding effect of the time zone offset across our geographically distributed user base is spot-on — that's exactly the kind of subtle bias that shifts quarterly results.
Can we label the y axis? Hard to read otherwise.
Does this include test users?
This is stale — LatAm was restated in 2020.
The line colors are too similar.
Same as before.
The pattern makes sense. See /u/plg_abebe_silva/p/plot-0038.
Moving this to the Q1 agenda.
Meriadoc Pillai This is solid.
Can you share the extract?
Same as before.
Looking at the monthly data, January always shows a spike, March always shows a dip, and summer shows a valley—these are such strong seasonal patterns that deviations from them stand out. But if the seasonality changes (e.g., from 20% deviation to 25%), is that a real change or just noise around a persistent pattern? Should we surface the seasonal factors explicitly?
Units?
This aligns with what I expected.
Duplicated. (edited to fix a unit)
Makes sense, thanks.
Mixing old and new methodologies. See /u/plg_abebe_silva/p/plot-0038.
What happened to Iberia around 2008?
This is accurate. See /u/plg_abebe_silva/p/plot-0038.
Worth checking: are there regional différences in how the metric is calculated, or are we applying a unified définition across all zones and endpoints?
Can you split Q2 out?
This lines up with the other chart if you put inflation per capita.
That's the way to do it.
Helena Trakand Exactly.
Colour order does not match the legend order.
This reflects the current state.
Well done.
the tick labels starts at zero for one series and not the other.
What's the source of that discontinuity on day 45?
Is urban share per capita here?
The category names are truncated. See /u/plg_abebe_silva/p/plot-0038.
That is a coverage gap, not a real move.
Can we see Brazil on the same scale?
This would be clearer as a table.
Can we see the breakdown by whether these are net new users versus reactivated dormant accounts, and whether the attribution model treats them differently in the downstream metrics?
That matches what I had.
Fixed.
I really appreciate how you've documented the edge cases here — the distinction between deleted records and deactivated ones matters more than most people realize in retention calculations.
Yes.
Flagging for the next planning session. (edited to fix a unit)