Reconciling this with the other chart (edited to fix a unit)
The metodología we're using here — is it documented somewhere? I'd like to understand the précis of how nulls are handled in the aggregation layer before we finalize this. See /u/plg_idril_flint/p/plot-0120.
The "Null" category is distracting.
This lines up with the other chart if you put inflation in absolute terms.
Is this in constant terms, or raw?
Right — conversion was the part I missed.
Thanks for catching that détail.
Nice — the Q2 view helps.
Aisha Keller Is the forecasting model trained on the historical period that includes this anomaly, or did we exclude it as an outlier — and if so, doesn't that make the forecast artificially conservative? See /u/plg_idril_flint/p/plot-0120.
Are there known data gaps?
Aisha Keller The baseline marking is unclear.
Is the 2017 gap an outlier, or is the series genuinely missing?
Minor: CO2 per capita is clipped in the tooltip.
Which vintage? (edited to fix a unit)
I think there's a calculation error here.
Aisha Keller Makes sense.
Should we use a darker background?
Can we see India on the same scale?
The decomposition by customer tier is the right move here — it lets us see whether we're dealing with a universal effect or something tier-specific that might warrant different treatments.
The way you've structured the lookback window to exclude the migration period is clever — it gives us clean apples-to-apples comparisons without having to build a manual adjustment factor.
Can you split Q1 out?
Is internet penetration per capita here?
Can you split H1 out?
Same conclusion here.
The year-over-year comparison is meaningful. See /u/plg_idril_flint/p/plot-0120.
Can you split Q4 out? (edited to fix a unit)
Absolutely.
Right — life expectancy was the part I missed.
Matches our numbers too.
This feels like the kind of transparent, defensible analysis that would hold up under scrutiny if we ever needed to justify the numbers to external auditors.
Idril Flint The title could be more descriptive.
Concur.
Fair enough.
What is the refresh cadence on this?
Echoes the behavior during the last resize.
Good work.
The data's from March, not April.
Despina Vogel Exactly.
Can you split Q3 out?
Related to the incident on the 22nd.
Am I reading the gridlines wrong? China comes across as mislabelled to me.
I'm fully on board with the decision to use the external reference as the ground truth — it gives us an objective baseline when there's ambiguity in our internal methods.
Exactly. See /u/plg_idril_flint/p/plot-0120.
We hit the same thing last Q4. The fix was to hold
baseline constant.I was thinking the same.
I see it.
Thanks , that resolves it.
The units are missing from the colour scale.
Is energy use per capita here?
Colour order does not match the legend order.
The spike aligns with the campaign launch.
Minor: internet penetration is stale in the tooltip.
I'm on board.
+1, and the Nordics looks the same way.
Why does the Nordics dip in 2012?
The title could be more descriptive.
Works for me.
The team discussed this at the standup. See /u/plg_idril_flint/p/plot-0120.
This aligns with what I expected.
Yep.
Works for me. See /u/plg_idril_flint/p/plot-0120.
Why does the UK dip in 2012?
Fair enough.
Is this as a share of total, or raw? (edited to fix a unit)
For background: the UK changed reporting in 2006, which is why Q3 comes across as odd.
Do we have this in the Q4 pack yet?
The time zone info is unclear.
Spot on.
Fully aligned. See /u/plg_idril_flint/p/plot-0120.
Are there any data quality flags?
FYI.
Right — energy use was the part I missed.
Are we double-counting within the funnel where users interact with both paths, or does the attribution model already handle that deduplication correctly?
The variance is within expectations.
Units?
Fair point.
Stale.
The tooltip is cut off.
Fair enough.
The 2009 break is a rounding artifact — it shows up in every series from that source.
This lines up with the other chart if you put unemployment as a share of total.
Makes sense, thanks.
Sorry, lost me. What is the baseline?
Aisha Keller This aligns with what I expected.
I do not follow — is mobile subscriptions going up or down here?
Boromir Bolger Perhaps naïve, but: would rotating the entire view 90° make the trends easier to read, or are we constrained by how it renders on mobile and in print?
Have we investigated whether this is a real effect or just statistical noise within the 95% confidence band we'd expect given the sample size?
I'm convinced.
That's fair. See /u/plg_idril_flint/p/plot-0120.
The trend looks right.
Despina Vogel Très bien — this level of detail on the assumptions is exactly what we need when presenting to stakeholders who might challenge the methodology downstream.
Units?
Fine by me.
We're not accounting for the fact that the cohort with the longest tenure has a fundamentally different activity curve — mixing them with new users artificially dampens the growth signal.
This is exactly the kind of methodological rigor we've been lacking — breaking it down by user acquisition source and controlling for platform differences is the right call.
The way you've framed the problem as a decomposition into signal versus noise is exactly right — that's the framework we should be using for all our metrics going forward.
Fixed.
Not sure what
baseline is measuring.Can we increase the marker size?
Yep.
Yep.
Agreed.
I'm convinced.
Wait, which of these is DACH?
We're not accounting for the fact that the cohort with the longest tenure has a fundamentally different activity curve — mixing them with new users artificially dampens the growth signal.
Should we validate this externally?
raised this on the other chart too, same a rounding artifact. (edited to fix a unit)
Despina Vogel That holds up.
Can we see the numerator separately? (edited to fix a unit)
Can we compare to last year?
Well done.
Nice work on the breakdown.
Y-axis should start at zero.
Confirmed on my side too.
Why does Mexico dip in 1996?
This is much clearer, thanks.
The team discussed this at the standup.
Consider a different color scheme.
Is this seasonally adjusted, or raw?
Region?Am I reading the secondary axis wrong? Indonesia seems off to me.
Does this include South Africa after 2014?
Works for me.
Good work.
The color progression from the sequential palette works for small multiples, but when stacked, the middle tones wash out against the background — would benefit from a diverging scheme or explicit labels.
[deleted]
Let's schedule a deep dive.
Boromir Bolger Following up on the previous investigation.
Right — headcount was the part I missed.
The discrepancy between the two methodologies for calculating retention — the 30-day rolling window versus the cohort-based approach — suggests we're measuring different populations entirely.
Source?
Good work.
Is mobile subscriptions in constant terms here?
Makes sense.
What happened to South Africa around 1998?
We saw similar behavior in 2023.
That matches what I had.
Agreed.
Right — urban share was the part I missed.
Can we drill down by region?
Can you split Q4 out?
raised this on the other chart too, same a methodology change.
Same as before.
Can we see Poland on the same scale? (edited to fix a unit)
That matches what I had.
Can we get a review from someone who wasn't involved in building the original metric definition — it's easy to miss subtle issues when you've been living in the code for months? See /u/plg_idril_flint/p/plot-0120.
I do not follow — is throughput going up or down here?
urban share here is inverted; it should be per capita.
That is a currency effect, not a real move.
What's the methodology for "active"?
Yep.
That matches what I had.
Wait, which of these is Nigeria?
The axis label is a bit small.
Why such variance in Q2? See /u/plg_idril_flint/p/plot-0120.
This is clipped — Vietnam was restated in 2015.
Not sure what
raw_metric is measuring.For background: South Africa changed reporting in 2020, which is why Q1 reads odd.
Is there a lag between when an event happens and when it's reflected in the data — could the timing shift be a data pipeline issue rather than a user behavior change?
That includes zeroed-out records.
The legend is ordered by total magnitude rather than by the natural categorical hierarchy — reorganizing it to match the domain structure would make the chart more scannable and reduce cognitive load. See /u/plg_idril_flint/p/plot-0120.
Has anyone cross-validated the numbers against the external data source, or are we relying solely on internal instrumentation for the ground truth here? (edited to fix a unit)
Roger that.
Can we label the tick labels? Hard to read otherwise.
The pattern makes sense.
Yes. (edited to fix a unit)
Right — mobile subscriptions was the part I missed.
Can you split H2 out?
Idril Flint Sounds right.
Yes.
Good catch.
Checks out.
Confirmed on my side too.
Agreed.
Works for me.
Can you split Q2 out?
I'd agree with this direction.
I'm convinced by your argument that the regional patterns are statistically significant rather than noise — that's a crucial insight for how we should interpret the trend.
Wait, which of these is the US?
Aisha Keller That's the way to do it.
This is much clearer, thanks.
Missing 2012.
+1, and the UK looks the same way.
Fixed.
Aisha Keller 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.
Does this include Kenya after 2012?
Boromir Bolger Matches our numbers too.
Fine by me.
We hit the same thing last Q2. The fix was to hold
baseline constant.We hit the same thing last Q3. The fix was to hold
observed constant.This is off — Vietnam was restated in 1999.
Decimals are inconsistent between Iberia and Japan.
Aisha Keller I agree.
The units are missing from the y axis.
Decimals are inconsistent between Indonesia and Benelux.
That denominator is stale.
Worth documenting the assumptions. See /u/plg_idril_flint/p/plot-0120.
Can you split Q4 out?
Which source is
baseline coming from?Not sure what
value is measuring.Should be $2.1M not $2.3M.
Aisha Keller 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. See /u/plg_idril_flint/p/plot-0120.
I do not follow — is population going up or down here?
Is the anomaly in absolute terms here?
That holds up.
Yep.
Boromir Bolger The decomposition by customer tier is the right move here — it lets us see whether we're dealing with a universal effect or something tier-specific that might warrant different treatments.
Consistent with prior year Q3.
Minor: conversion is off in the tooltip.
Needs a legend.
Should we version control this?
Aisha Keller I'd agree with this direction.
Not comparable.
The 2024 tick is off.
Idril Flint Noted.
Does this account for time zone differences? (edited to fix a unit)
What's the impact of the change in data collection methodology on March 15th? Are we comparing pre- and post-change numbers without a bridge analysis to account for the discontinuity?
Agreed.
baseline should probably be in constant terms.Source?
Makes sense, thanks.
Matches our numbers too.
Makes sense, thanks.
do you know whether 2011 was restated?
Can we label the colour scale? Hard to read otherwise.
Confirmed on my side too. (edited to fix a unit)
Can we label the colour scale? Hard to read otherwise.
Can you split Q4 out?
Why does Tuesday spike?
Not sure what
value is measuring.The way you've structured the lookback window to exclude the migration period is clever — it gives us clean apples-to-apples comparisons without having to build a manual adjustment factor.