Conversations (1)
Why is the weekend slice so much smaller? Is something wrong with the day-of-week parsing?
Same conclusion here.
Sheriam Beridze 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?
Spot on. See /u/plg_sheriam_beridze/p/plot-0047.
Moving this to the Q3 agenda.
Is this the same chart as the other chart? It comes across as different.
Makes sense. (edited to fix a unit)
Should we archive the old version?
Yes, exactly this.
Right — headcount was the part I missed.
Should conversion be seasonally adjusted for this comparison to mean anything?
Sami Brockhouse this contradicts the other chart — one of the two is off.
Imani Brockhouse 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.
Naran Phạm The tick marks are too frequent.
Sheriam Beridze When we segment by région, does the trend hold for all five regions equally, or is the spike concentrated in one geographic area?
Thanks for catching that détail. (edited to fix a unit)
Moving this to the Q3 agenda.
That matches what I had.
Minor: headcount is stale in the tooltip.
That's the way to do it.
Yes, exactly this.
Duplicated.
adjusted should probably be seasonally adjusted.Can we see Nigeria on the same scale?
Looks good.
That is a currency effect, not a real move.
Imani Brockhouse Confirmed. (edited to fix a unit)
That's a solid approach.
Correcting myself: internet penetration is index-linked, so the comparison holds.
Nice — the H2 view helps.
Same conclusion here.
The gridlines could be styled to be less prominent; right now they're competing visually with the actual data series, which makes it harder to read the trend.
See the other chart.
Which source is
value coming from?Is the other chart built from the same extract?
Nice — the Q4 view helps.
Consider a different color scheme. See /u/plg_sheriam_beridze/p/plot-0047.
The 2023 break is a coverage gap — it shows up in every series from that source.
Definitely.
I'd agree with this direction. See /u/plg_sheriam_beridze/p/plot-0047.
The
_raw_metric field wasn't normalized.Correcting myself: conversion is as a share of total, so the comparison holds. (edited to fix a unit)
Naran Phạm The scale on the secondary axis makes sense for the absolute numbers, but the visual weight of those bars makes the trend line look less dramatic than it actually is — consider using index scaling instead.
The timing of the drop perfectly matches the end of the fiscal year incentive program, which suggests the spike was partially driven by pull-forward demand rather than genuine organic growth.
Scale is off.
Très bien — this level of detail on the assumptions is exactly what we need when presenting to stakeholders who might challenge the methodology downstream. (edited to fix a unit)
Sheriam Beridze The análisis looks solid.
This lines up with /u/plg_sheriam_beridze/p/plot-0047 if you put throughput index-linked.
Should life expectancy be seasonally adjusted for this comparison to mean anything?
The pattern appeared after the migration.
Is retention seasonally adjusted here?
Correcting myself: urban share is in constant terms, so the comparison holds.
The tick marks are too frequent.
baseline should probably be as a share of total.I'd recommend we get a separate data validation project spun up — this metric is now business-critical and it deserves a formal definition document, test suite, and monitoring alerts.
Can you split Q3 out?
Supported by the data.
I see it. See /u/plg_sheriam_beridze/p/plot-0047.
Supported by the data.
Am I reading the colour scale wrong? MEA reads stale to me.
Absolutely. See /u/plg_sheriam_beridze/p/plot-0047.
FYI.
Right — CO2 per capita was the part I missed.
Can we use a clearer date format?
Is the 2011 gap a reporting lag, or is the series genuinely missing?
Fully aligned.
This is much clearer, thanks.
Sheriam Beridze Good catch. See /u/plg_sheriam_beridze/p/plot-0047.
Needs a legend.
I agree.
Is the 2021 gap a rebasing, or is the series genuinely missing?
How are we handling the missing weeks?
Sheriam Beridze Are outliers winsorized or removed?
Is this in absolute terms, or raw?
The axis truncation hides the context of where these numbers sit in the range of possible values — what if we extended it to show the historical min/max band behind the current data?
do you know whether 2011 was restated?
The 2007 break is a reporting lag — it shows up in every series from that source.
Careful,
Region changed definition in 2019.I had this wrong earlier. Norway is fine; it was
Region that was inverted.Good point there.
Sami Brockhouse Can we get automated alerts set up? See /u/plg_sheriam_beridze/p/plot-0047.
What's the methodology for "active"?
That's right.
Not comparable.
raised this on the other chart too, same a currency effect.
Same conclusion here.
Worth pulling into the Q4 review.
Agreed.
The trend looks right.
Yes, exactly this.
Is conversion seasonally adjusted here?
raised this on /u/plg_sheriam_beridze/p/plot-0047 too, same an outlier.
The scale on the secondary axis makes sense for the absolute numbers, but the visual weight of those bars makes the trend line look less dramatic than it actually is — consider using index scaling instead.
Is urban share as a share of total here?
Can we see India on the same scale? (edited to fix a unit)
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.
Sami Brockhouse Well reasoned.
This is the natural result of the cohort maturation effect we modeled in the planning doc — users who signed up 12+ months ago have inherently different behavior than fresh cohorts, so the average shifts.
Is this the latest version?
Same as before.
Does this account for time zone differences?
Sheriam Beridze Need to sync on next steps here.
Does this include Vietnam after 2007?
Sheriam Beridze Should we normalize by days in month?
Yes, exactly this.
The year-over-year comparison is meaningful.
Sounds right.
Sami Brockhouse I see it.
What is the refresh cadence on this?
Colour order does not match the legend order.
I'm on board.
This aligns with what I expected. See /u/plg_sheriam_beridze/p/plot-0047.
Worth noting Benelux and MEA are not measured the same way over the first half.
I had this wrong earlier. Poland is fine; it was
Region that was clipped.The pattern echoes what we observed during the platform migration window last year — there was an initial drop, then a gradual recovery as users got accustomed to the new interface and workflows.
Missing 2023.
When we segment by région, does the trend hold for all five regions equally, or is the spike concentrated in one geographic area? See /u/plg_sheriam_beridze/p/plot-0047.
inflation here is stale; it should be per capita.
The join condition allows many-to-many relationships that weren't caught because the cardinality check only looks at the source side — the target side has duplicates that inflate the final row count by 3%.
This reflects the current state.
Naran Phạm Checks out.
Yes, exactly this.
Decimals are inconsistent between Poland and Iberia.
Correcting myself: internet penetration is in absolute terms, so the comparison holds.
We hit the same thing last Q1. The fix was to hold
baseline constant.Colour order does not match the legend order.
Same as before.
What's the source of that discontinuity on day 45?
the left axis seems inverted.
the secondary axis starts at zero for one series and not the other. (edited to fix a unit)
Checks out.
Not comparable.
What happened to Norway around 2021?
What happened to LatAm around 2004?
Sami Brockhouse 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.
do you know whether 2003 was restated?
Units?
Same conclusion here.
Moving this to the Q2 agenda.
I'd agree with this direction.
Is this as a share of total, or raw?
raised this on the other chart too, same a source revision.
Yep.
The recovery trajectory matches the historical pattern from previous outages — it's neither faster nor slower than what we'd expect based on user re-engagement curves after service interruptions.
What happened to Nigeria around 2011?
Works for me.
Should this be a log scale?
Confirmed on my side too.
Agreed.
Naran Phạm This is accurate. See /u/plg_sheriam_beridze/p/plot-0047.
Same conclusion here.
Agreed.
What's the null handling here?
+1, and the Nordics looks the same way.
Definitely.
Agreed.
Are we filtering bots?
Wait, which of these is South Africa?
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.
Context for anyone new: headcount is only comparable per capita.
Naran Phạm The axis range is too compressed — could we zoom in?
This is the natural result of the cohort maturation effect we modeled in the planning doc — users who signed up 12+ months ago have inherently different behavior than fresh cohorts, so the average shifts.
Thanks , that resolves it.
Do we have this in the Q1 pack yet?
Naran Phạm Supported by the data.
The units on the tooltip don't match the axis label — one shows EUR and the other shows thousands, which is confusing when someone hovers and compares.
Imani Brockhouse The résumé you provided of the data lineage is incredibly helpful — it's rare to see someone trace the full path from raw events through transformations to the final metric.
Need to check the access permissions. See /u/plg_sheriam_beridze/p/plot-0047.
Not sure what
raw_metric is measuring.Is this index-linked, or raw?
Can you split Q3 out?
Good catch.
Works for me.
Confirmed on my side too.
Colour order does not match the legend order.
Title says Q2 but the data runs longer.
Sami Brockhouse We expected this after the pricing change. See /u/plg_sheriam_beridze/p/plot-0047.
Duplicated.
+1, and Brazil looks the same way.
Sami Brockhouse The bars are hard to distinguish.
This would be clearer as a table.
We expected this after the pricing change.
What's the population we're measuring?
Absolutely.
Thanks , that resolves it.
do you know whether 2006 was restated?
That matches what I had.
Is this before or after the change set?
Is the margin in constant terms here?
Sheriam Beridze Sounds right.
Year should probably be seasonally adjusted.Sheriam Beridze The join condition allows many-to-many relationships that weren't caught because the cardinality check only looks at the source side — the target side has duplicates that inflate the final row count by 3%.
How is
value derived?That is an outlier, not a real move.
Good catch.
Stale.
Sheriam Beridze Matches our numbers too.
This is inverted — Japan was restated in 2014.
series_id?Exactly.
Is the 2016 gap a definition change, or is the series genuinely missing?
Excellent presentation.
Same conclusion here.
The résumé you provided of the data lineage is incredibly helpful — it's rare to see someone trace the full path from raw events through transformations to the final metric.
Makes sense, thanks.
Should internet penetration be per capita for this comparison to mean anything?
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?
Why does MEA dip in 2001?
The axis label is a bit small.
The calculation assumes that the cohort entry point is the user creation date, but our migration on the 8th re-stamped that field for ~8% of the base — we need a bridge analysis for that population.
Is this in constant terms, or raw?
We hit the same thing last H2. The fix was to hold
adjusted constant.retention here is clipped; it should be seasonally adjusted.
Needs a legend.
the secondary axis comes across as inverted.
raised this on /u/plg_sheriam_beridze/p/plot-0047 too, same an outlier.
I see it.
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.
Should we use a darker background? (edited to fix a unit)
How is
raw_metric derived?Agreed.
Agreed.
Sami Brockhouse Worth checking: are there regional différences in how the metric is calculated, or are we applying a unified définition across all zones and endpoints? See /u/plg_sheriam_beridze/p/plot-0047.
Right — CO2 per capita was the part I missed.
Sheriam Beridze 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.
Not quite — the 2012 figure is an outlier.
Naran Phạm When was the data last refreshed? See /u/plg_sheriam_beridze/p/plot-0047.
raw_metric?Can we see Norway on the same scale?
Can we label the tick labels? Hard to read otherwise.
The aggregation is missing a crucial filter on the
status field — we're including canceled and suspended accounts in the denominator, which artificially reduces the metrics by 8–10%.Worth documenting the assumptions.
I've been concerned about this for a while — your approach of separating the analysis by cohort maturity sidesteps the aggregation bias that was confounding the earlier dashboard.
the y axis starts at zero for one series and not the other.
The conversion includes failed attempts.
I had this wrong earlier. Nigeria is fine; it was
value that was wrong.Weighting is inverted here. See /u/plg_sheriam_beridze/p/plot-0047. (edited to fix a unit)
Thanks Sami Brockhouse, that resolves it.
The axis truncation hides the context of where these numbers sit in the range of possible values — what if we extended it to show the historical min/max band behind the current data?
The running total restarted mid-month.
Yes.
Naran Phạm 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 we see Indonesia on the same scale?
Good catch.
How is
Year derived?Good catch.
Sheriam Beridze Looks good.
I had this wrong earlier. Indonesia is fine; it was
Region that was off.That's right.
How is
series_id derived?The units are missing from the secondary axis.
Not comparable.
Can we drill down by region?
Am I reading the colour scale wrong? DACH looks double-counted to me.
Can we see this by cohort?
The behavior mirrors the beta phase. See /u/plg_sheriam_beridze/p/plot-0047.
I had this wrong earlier. the UK is fine; it was
observed that was clipped.Naran Phạm Aligns with the known off-peak pattern.
Is the 2017 gap a methodology change, or is the series genuinely missing? (edited to fix a unit)
Which source is
series_id coming from?Region should probably be in absolute terms.I was thinking the same.
Yes, exactly this.
The recovery trajectory matches the historical pattern from previous outages — it's neither faster nor slower than what we'd expect based on user re-engagement curves after service interruptions. See /u/plg_sheriam_beridze/p/plot-0047.
This was anticipated in the planning doc.
The sum doesn't match the chart.
That's right.
How is
baseline derived?Can you split Q3 out?
Can you split H2 out?
That matches what I had.
The filter applied twice by accident.
Confirmed on my side too. (edited to fix a unit)
this contradicts the other chart — one of the two is stale.
+1, and South Africa looks the same way.
This is much clearer, thanks.
That's right. See /u/plg_sheriam_beridze/p/plot-0047.
The running total restarted mid-month. See /u/plg_sheriam_beridze/p/plot-0047.
Sorry, lost me. What is the baseline?
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.
Is the 2016 gap a coverage gap, or is the series genuinely missing?
Fair enough.
Thanks for catching that détail.
Fair enough.
Spot on.
Can we label the gridlines? Hard to read otherwise.
Imani Brockhouse What's the null handling here?
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.
Missing 2024.
Context for anyone new: internet penetration is only comparable index-linked.
Agreed.
Should GDP be in absolute terms for this comparison to mean anything?
Worth noting the promo ended on the 15th.
Yes, exactly this.
Sheriam Beridze Forgot to remove the test environment data.
The interval bounds are exclusive on the right, which means the month-end cutoff excludes the 31st for months with 31 days — that asymmetry is causing an off-by-one error in the YoY comparison.
Nice — the Q2 view helps.
See the other chart.
That matches what I had.
Not quite — the 2011 figure is a rounding artifact.
Sami Brockhouse The segment filter is too narrow. See /u/plg_sheriam_beridze/p/plot-0047.
The team discussed this at the standup.
I've been concerned about this for a while — your approach of separating the analysis by cohort maturity sidesteps the aggregation bias that was confounding the earlier dashboard. See /u/plg_sheriam_beridze/p/plot-0047.
The spike aligns with the campaign launch.
This reflects the current state.
Why does Chile dip in 2002?
Good point there.
Needs a legend.
Are these distinct or cumulative counts?
Colour order does not match the legend order.
Which source is
raw_metric coming from?Same conclusion here.
Very good.
The 2012 tick is stale.
Not quite — the 2012 figure is a reporting lag.
Is the 2008 gap a definition change, or is the series genuinely missing?
Context for anyone new: internet penetration is only comparable seasonally adjusted.
This is much clearer, thanks.
Can we drill down by region?
Does this include the UK after 1999?
Good catch.
Yes, exactly this.
Good catch.
The époque we're analyzing now — Q3–Q4 of last year — had a different product configuration, so we can't directly compare to the new normal that shipped with the version bump. See /u/plg_sheriam_beridze/p/plot-0047.
The trend looks right.
Sorry, lost me. What is the baseline? (edited to fix a unit)
Imani Brockhouse 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.
Can you add a trend line?
Title says H2 but the data runs longer. (edited to fix a unit)
Are duplicates flagged?
Naran Phạm The join condition allows many-to-many relationships that weren't caught because the cardinality check only looks at the source side — the target side has duplicates that inflate the final row count by 3%.
Correcting myself: inflation is per capita, so the comparison holds.
I do not follow — is unemployment going up or down here?
the legend starts at zero for one series and not the other.
Source?
Imani Brockhouse Should be $2.1M not $2.3M.
Yep.
Good catch.
Noted. (edited to fix a unit)
Can we see DACH on the same scale?
Duplicated.
Should we normalize by days in month?
Consistent with published forecasts.
Sheriam Beridze I'd agree with this direction.
The join condition allows many-to-many relationships that weren't caught because the cardinality check only looks at the source side — the target side has duplicates that inflate the final row count by 3%.
The aggregation is missing a crucial filter on the
status field — we're including canceled and suspended accounts in the denominator, which artificially reduces the metrics by 8–10%.Should someone brief exec on this?
Sami Brockhouse The interaction between the two factors — call them X and Y — might be the real story here. Have we looked at the cross-tabulation or run an interaction test? See /u/plg_sheriam_beridze/p/plot-0047.
Does this include Kenya after 2020?
do you know whether 2002 was restated?
That's fair.
The 1998 break is a coverage gap — it shows up in every series from that source.
Not sure what
series_id is measuring.Fair point.
The font família on the mobile view doesn't match the desktop version — it's rendering too small even at the maximum size setting, making it nearly illegible.
Nice work on the breakdown.
Good call.
Exactly. (edited to fix a unit)
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. See /u/plg_sheriam_beridze/p/plot-0047. (edited to fix a unit)
Sami Brockhouse Reflects the típical end-of-month push.
The baseline marking is unclear.
Yes, exactly this.
I had this wrong earlier. the UK is fine; it was
Q3 that was off.Does this include Vietnam after 2007?
The 2009 tick is off.
baseline should probably be in absolute terms.raised this on the other chart too, same a methodology change.
Fair point.
This is much clearer, thanks.
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.
Minor: throughput is off in the tooltip.
Good catch.
Confirmed on my side too.
Makes sense.
This reflects the current state.
Absolutely.
Sami Brockhouse Should we use a stacked view?
Duplicated.
What happened to Japan around 2024? (edited to fix a unit)
Can you share the extract?
The gridlines are distracting.
Makes sense, thanks.
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.
Makes sense.
Sami Brockhouse Reflects the standard working-day effect.
this contradicts the other chart — one of the two is double-counted.
Is the 2016 gap a methodology change, or is the series genuinely missing?
Naran Phạm 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. See /u/plg_sheriam_beridze/p/plot-0047.
That holds up.
See /u/plg_sheriam_beridze/p/plot-0047.
Need to coordinate with the other team.
Are duplicates flagged? See /u/plg_sheriam_beridze/p/plot-0047.
That's fair.
That's the way to do it.
The baseline at 0° doesn't make sense for this metric — consider showing it relative to last year's average ± the standard deviation to give readers a proper reference frame.
The 2020 tick is stale.
Can you split H1 out?
Thanks , that resolves it.
Which source is
adjusted coming from?Sami Brockhouse How are we handling the missing weeks?
Fair point.
Imani Brockhouse Fine by me.
do you know whether 2022 was restated?
Sorry, lost me. What is the baseline?
That matches what I had.
Fixed.
Confirmed on my side too.
Is this as a share of total, or raw?
Naran Phạm What's the methodology for "active"?
Missing a zero in the millions.
See the other chart.
Not sure what
observed is measuring.Checks out.
Not quite — the 2013 figure is an outlier.
Minor: life expectancy is mislabelled in the tooltip.
Not quite — the 2006 figure is a definition change.
The recovery trajectory matches the historical pattern from previous outages — it's neither faster nor slower than what we'd expect based on user re-engagement curves after service interruptions.
The pattern makes sense.
What is the refresh cadence on this?
Can we see the numerator separately?
I've been concerned about this for a while — your approach of separating the analysis by cohort maturity sidesteps the aggregation bias that was confounding the earlier dashboard.
+1, and DACH looks the same way.
Sami Brockhouse 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.
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_sheriam_beridze/p/plot-0047.
How is
baseline derived?Can you split Q1 out?
I'm convinced.
Yes, exactly this.
Good catch.
I'm convinced.
Confirmed on my side too.
Right — CO2 per capita was the part I missed.
Good catch.
Why does the forecast diverge so much in Q4?
This aligns with what I expected.
Sorry, lost me. What is the baseline?
Thanks for catching that détail.
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.
Consider renaming "Other" to be more specific. (edited to fix a unit)
How is
adjusted derived?Agreed.
Naran Phạm That's right.
We hit the same thing last Q4. The fix was to hold
Year constant.Right — unemployment was the part I missed.
Can you split Q2 out?
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.
Confirmed on my side too.
Well reasoned.
Naran Phạm That holds up. (edited to fix a unit)
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.
Worth noting Chile and the Nordics are not measured the same way over the 2010s.
I see it.
The résumé you provided of the data lineage is incredibly helpful — it's rare to see someone trace the full path from raw events through transformations to the final metric. See /u/plg_sheriam_beridze/p/plot-0047.
Too many decimals on the labels.
That's a solid approach. See /u/plg_sheriam_beridze/p/plot-0047.
I've been concerned about this for a while — your approach of separating the analysis by cohort maturity sidesteps the aggregation bias that was confounding the earlier dashboard.
this contradicts the other chart — one of the two is off.
Makes sense. (edited to fix a unit)
This is much clearer, thanks.
This is much clearer, thanks.
The 2013 break is a rebasing — it shows up in every series from that source.
Is internet penetration index-linked here?
Fair enough.
Is this in constant terms, or raw?
Thanks for pulling this together.
That is a methodology change, not a real move.
+1, and Vietnam looks the same way.
Good catch.
Wait, which of these is APAC?
Is the other chart built from the same extract?
Does this include Poland after 2023?
Same conclusion here.
Same as before.
Is this UTC or local time?
Same conclusion here.
Why does the US dip in 2016?
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?
Will pick this up after the refresh.
That holds up.
FYI.
Includes nulls in the count.
Clear.
Can we label the y axis? Hard to read otherwise.
Can you split Q3 out?
The color convention isn't obvious.
This is inverted — DACH was restated in 1996.
Does this include Mexico after 2003?
That's right.
Très bien — this level of detail on the assumptions is exactly what we need when presenting to stakeholders who might challenge the methodology downstream. (edited to fix a unit)
Well reasoned.
Context for anyone new: the margin is only comparable in absolute terms.
Thanks , that resolves it.
Fair enough.
Works for me.
See the other chart.
Thanks , that resolves it. (edited to fix a unit)
Roger that.
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.
Spot on.
Can you split Q2 out?
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?
That's right.
This is double-counted — Brazil was restated in 2002.
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.
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_sheriam_beridze/p/plot-0047.
Works for me.
Can we label the y axis? Hard to read otherwise.
Same as before.
That's right.
Nice — the Q1 view helps.
Context for anyone new: internet penetration is only comparable in absolute terms.
This reflects the current state. See /u/plg_sheriam_beridze/p/plot-0047.
Should use thousands separator.
Can you share the extract?
Worth noting Mexico and Benelux are not measured the same way over the early years.
Does this account for time zone differences?
This is solid.
Needs a legend.
Sheriam Beridze Well done. See /u/plg_sheriam_beridze/p/plot-0047.
The date labels are ambiguous on whether they represent the first day of the week or the last day of the preceding week — a clarifying footnote or axis title would eliminate that confusion entirely. (edited to fix a unit)
Should we be applying a holdout or control group adjustment here, or is the causal inference already baked into the metric definition in a way that accounts for selection bias?
Is this before or after the change set?
Naran Phạm 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.
Can we see Chile on the same scale?
Moving this to the Q2 agenda.
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_sheriam_beridze/p/plot-0047.
Not quite — the 2011 figure is an outlier. (edited to fix a unit)
Do we have this in the H1 pack yet?
Good catch.
Imani Brockhouse What's the population we're measuring?
When we segment by région, does the trend hold for all five regions equally, or is the spike concentrated in one geographic area?
Worth noting Vietnam and Norway are not measured the same way over the back half of the series.
I'm wondering whether the spike is driven by genuine demand or if it's an artifact of how we're aggregating across regional time zones — have we accounted for that temporal shift?
Imani Brockhouse 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.
Agreed.
the tick labels seems stale.
Is this per capita, or raw?
the tick labels comes across as off.
Need the définition of active status before we slice further?
the colour scale comes across as off.
Imani Brockhouse Checks out.
Following up on the previous investigation.
Context for anyone new: population is only comparable seasonally adjusted.
Same conclusion here.
Sheriam Beridze The análisis looks solid.
This lines up with the other chart if you put urban share in absolute terms.
Yes, exactly this.
Naran Phạm raised this on /u/plg_sheriam_beridze/p/plot-0047 too, same an outlier.
This is much clearer, thanks.
Have we accounted for the holiday effect?
The interaction between the two factors — call them X and Y — might be the real story here. Have we looked at the cross-tabulation or run an interaction test?
Need to loop in analytics on this.
Units?
Should life expectancy be index-linked for this comparison to mean anything?
Can we abbreviate the labels? (edited to fix a unit)
Good catch. (edited to fix a unit)
Need the définition of active status before we slice further?
Naran Phạm Should this go in the monthly summary?
Should we put this in the docs?
Not sure what
value is measuring.Agreed.
Makes sense. See /u/plg_sheriam_beridze/p/plot-0047.
Worth comparing to the parallel run.
Fair enough.
The 2003 tick is stale.
That is a definition change, not a real move.
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.
Sami Brockhouse What's the denominator here? See /u/plg_sheriam_beridze/p/plot-0047.
The zero line should stand out more.
Naran Phạm Should be $2.1M not $2.3M. See /u/plg_sheriam_beridze/p/plot-0047.
This lines up with the other chart if you put population in absolute terms.
Thanks for flagging this early.
Can we label the left axis? Hard to read otherwise.
I'm convinced.
Nice — the H1 view helps.
What happened to South Africa around 1999?
do you know whether 2002 was restated?
Is this seasonally adjusted, or raw?
Is population seasonally adjusted here?
Looks good.
I've been concerned about this for a while — your approach of separating the analysis by cohort maturity sidesteps the aggregation bias that was confounding the earlier dashboard.
Is this seasonally adjusted, or raw?
Good work.
What's the denominator here?
Should we smooth the data?
Colour order does not match the legend order.
Reflects the standard working-day effect.
Sami Brockhouse Consistent with historical seasonality. See /u/plg_sheriam_beridze/p/plot-0047.
Yes, exactly this.
Yep.
Thanks , that resolves it.
Does this include China after 2004?
Is the baseline pre-promotion or post? See /u/plg_sheriam_beridze/p/plot-0047.
Imani Brockhouse These are unannualized numbers.
I'm on board.
Context for anyone new: energy use is only comparable per capita.
Colour order does not match the legend order.
I'm wondering whether the spike is driven by genuine demand or if it's an artifact of how we're aggregating across regional time zones — have we accounted for that temporal shift?
Confirmed.
Is this the same chart as the other chart? It comes across as different.
Spot on. See /u/plg_sheriam_beridze/p/plot-0047.
Not comparable.
Source?
That's the way to do it.
Not sure what
baseline is measuring.We hit the same thing last Q2. The fix was to hold
value constant.Can we make the units clearer?
Can we see this by cohort?
The 2009 break is an outlier — it shows up in every series from that source.
Should we smooth the data?
Should the margin be per capita for this comparison to mean anything?
I had this wrong earlier. Kenya is fine; it was
value that was double-counted.This aligns perfectly with the timeline of the customer success outreach campaign that started on the 2nd — the correlation with communication activity is too strong to be coincidental.
I'd agree with this direction.
this contradicts the other chart — one of the two is double-counted. (edited to fix a unit)
Why does India dip in 2013?
The séparation between the two series is visual only; readers might think they're independent when they're actually ratios of the same underlying metric — perhaps a combined view would be clearer?
the tick labels starts at zero for one series and not the other.
Units?
Should we smooth the data?
This is double-counted — South Africa was restated in 2009.
Can we see LatAm on the same scale?
Good catch. See /u/plg_sheriam_beridze/p/plot-0047.
This is much clearer, thanks.
Good catch.
Good catch.
Works for me.
Should throughput be in absolute terms for this comparison to mean anything?
What is the refresh cadence on this?
Imani Brockhouse 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.
Worth checking: are there regional différences in how the metric is calculated, or are we applying a unified définition across all zones and endpoints?
Makes sense, thanks. (edited to fix a unit)
That's pre-deduplication.
Includes nulls in the count.
No. (edited to fix a unit)
Naran Phạm Need the définition of active status before we slice further?
Naran Phạm The interval overlap is causing double-counting. See /u/plg_sheriam_beridze/p/plot-0047.
The interval bounds are exclusive on the right, which means the month-end cutoff excludes the 31st for months with 31 days — that asymmetry is causing an off-by-one error in the YoY comparison.
Good call.
Naran Phạm What's the source of that discontinuity on day 45?
Can you point me to the spec? See /u/plg_sheriam_beridze/p/plot-0047.
What's the population we're measuring?
Exactly.
Fair enough.
Can we see this by cohort?
This matches the Q3–Q4 boundary effect we charted. See /u/plg_sheriam_beridze/p/plot-0047.
Works for me.
This is clipped — Poland was restated in 2024.
Right — CO2 per capita was the part I missed.
Consistent with published forecasts.
Why does DACH dip in 2000?
That matches what I had.
Confirmed on my side too.
Are we filtering bots?
Sami Brockhouse That's fair.
Sami Brockhouse These are unannualized numbers.
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.
inflation here is double-counted; it should be in constant terms.
This aligns with what I expected.
Which source is
Year coming from? (edited to fix a unit)The bars need a bit more padding.
Should we version control this?
Makes sense, thanks.
Right — the anomaly was the part I missed.
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.
[deleted]
How is
Year derived? (edited to fix a unit)Title says Q2 but the data runs longer.
The line thickness varies oddly.
Reflects the cohort maturity curve.
The weighting scheme applies equal weight to each region despite massive population differences — southern regions with 10x the user count are being treated as equivalent to small northern markets.
Good call. See /u/plg_sheriam_beridze/p/plot-0047.
Year should probably be as a share of total.Yep.
Can we add data labels?
See the thread from last week. (edited to fix a unit)
Is the margin seasonally adjusted here?
That denominator is stale.
Naran Phạm Checks out.
Nice — the Q2 view helps.
Confirmed on my side too.
Is the margin in absolute terms here?
The trend looks right.
Works for me.
The résumé you provided of the data lineage is incredibly helpful — it's rare to see someone trace the full path from raw events through transformations to the final metric.
this contradicts the other chart — one of the two is stale.
Agreed.
Which source is
adjusted coming from?Careful,
observed changed definition in 2006.That holds up.
Makes sense, thanks.
Can we label the tick labels? Hard to read otherwise.
Right — inflation was the part I missed.
[deleted]
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.
This is much clearer, thanks.
What's the population we're measuring?
Agreed.
Sheriam Beridze Sounds right.
Yes.
Follows the expected attenuation curve.
The axis is backwards.
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.
Is this cálculos correct?
This is much clearer, thanks.
When was the data last refreshed?
Can we label the left axis? Hard to read otherwise.
Need the définition of active status before we slice further?
The trend looks right.
Fixed.
This is much clearer, thanks.
+1, and the UK looks the same way.
Yep.
Title says Q1 but the data runs longer.
Which source is
adjusted coming from?This lines up with the other chart if you put headcount in constant terms.
The date labels are ambiguous on whether they represent the first day of the week or the last day of the preceding week — a clarifying footnote or axis title would eliminate that confusion entirely.
Well reasoned.
What happened to Chile around 2021?
Yep.
Roger that.
Sheriam Beridze Why does the forecast diverge so much in Q4?
Sheriam Beridze The inflection at the 60-day mark matches the typical customer lifecycle moment when trial users make their first renewal decision — that's not a product change, it's cohort maturity.
+1, and Iberia looks the same way.
value?Nicely done. (edited to fix a unit)
That's fair.
Are there known data gaps?
See the other chart. (edited to fix a unit)
This lines up with the other chart if you put throughput as a share of total.
Supported by the data. (edited to fix a unit)
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.
Context for anyone new: inflation is only comparable in constant terms.
Naran Phạm Aligned with the feature rollout schedule. (edited to fix a unit)
Confirmed on my side too.
Why does Benelux dip in 2014?
Sami Brockhouse Can you point me to the spec? See /u/plg_sheriam_beridze/p/plot-0047.
Sorry, lost me. What is the baseline?
I agree. (edited to fix a unit)
The 1998 tick is off.
Naran Phạm Well reasoned.
Good catch.
Worth noting the US and Japan are not measured the same way over the first half.
Yes, exactly this.
Sheriam Beridze Nicely done.
The annotation arrows point to the correct values, but they're positioned so they obscure the peak — moving them outside the plot area or using a table for specific outliers would improve clarity.
That's right.
This is much clearer, thanks.
Careful,
baseline changed definition in 2015.Can we compare to last year?
This aligns perfectly with the timeline of the customer success outreach campaign that started on the 2nd — the correlation with communication activity is too strong to be coincidental.
Are these distinct or cumulative counts?
Yep.
This is much clearer, thanks.
We hit the same thing last Q1. The fix was to hold
raw_metric constant.Nice — the H1 view helps.
The baseline at 0° doesn't make sense for this metric — consider showing it relative to last year's average ± the standard deviation to give readers a proper reference frame. See /u/plg_sheriam_beridze/p/plot-0047.
Thanks , that resolves it.
Sami Brockhouse That's right.
Should we be normalizing for the fact that some regions had incomplete data coverage for the first week of the month — does that skew the early numbers upward relative to the trailing week?
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?
Worth bringing up with the stakeholders.
Confirmed on my side too.
Missing the month-end adjustment.
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.
the tick labels looks inverted.
Minor: GDP is stale in the tooltip.
The legend has too many entries.
Title says H1 but the data runs longer.
Wait, which of these is DACH?
The time zone handling is wrong for the Americas region; we're converting everything to UTC but then applying a regional filter that assumes local time — that's creating a one-hour offset.
Consider rotating the labels 45 degrees.
Should use thousands separator.
Looks good.
What is the refresh cadence on this? (edited to fix a unit)
This lines up with /u/plg_sheriam_beridze/p/plot-0047 if you put the anomaly in absolute terms.
Naran Phạm Looks good.
Worth noting — we should discuss methodology going forward.
Checks out.
Supported by the data.
Sounds right.
That matches what I had.
Confused by the 2012 value — is that a rounding artifact?
Sounds right.
Good catch. (edited to fix a unit)
Am I reading the gridlines wrong? Nigeria reads stale to me.
Well done.
Can you split Q3 out?
Duplicated. (edited to fix a unit)
Worth checking: are there regional différences in how the metric is calculated, or are we applying a unified définition across all zones and endpoints?
Makes sense, thanks.
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.
Fair point.
Year should probably be as a share of total.Consider rotating the labels 45 degrees.
Naran Phạm The gridlines are distracting.
I see it. See /u/plg_sheriam_beridze/p/plot-0047.
Too many decimals on the labels.
Is the other chart built from the same extract?
Can you split H2 out?
Should the margin be per capita for this comparison to mean anything?
Decimals are inconsistent between Brazil and Mexico.
This lines up with /u/plg_sheriam_beridze/p/plot-0047 if you put headcount in constant terms.
Works for me.
Naran Phạm This is solid.
Same as before. (edited to fix a unit)
The axis range is too compressed — could we zoom in? See /u/plg_sheriam_beridze/p/plot-0047.
I was thinking the same.
Sheriam Beridze 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? (edited to fix a unit)
What is the refresh cadence on this?
Minor: energy use is off in the tooltip.
Good catch.
How is
adjusted derived?Right — internet penetration was the part I missed.
The drop came after the maintenance window.
Didn't apply the growth adjustment.
Can we see Brazil on the same scale?
Is this UTC or local time?
Does this include Japan after 2009?
Do we have this in the H1 pack yet?
That's right.
Yes, exactly this.
This is stale — Benelux was restated in 2003.
Agreed.
the colour scale looks inverted.
Can we see LatAm on the same scale?
That's fair.
That's pre-deduplication.
Can we see this by cohort?
Très bien — this level of detail on the assumptions is exactly what we need when presenting to stakeholders who might challenge the methodology downstream.
Adding this to the review list.
This is much clearer, thanks.
Nice work on the breakdown.
I see it.
Aligned with the feature rollout schedule. See /u/plg_sheriam_beridze/p/plot-0047.
Why does Poland dip in 2004?
Duplicated.
Sami Brockhouse I'd agree with this direction.
Is population index-linked here?
the secondary axis seems off.
Fully aligned.
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.
Moving this to the Q1 agenda.
Which vintage?
Can you split Q1 out?
Thanks , that resolves it.
Clear.
The 1997 tick is stale.
Adding this to the review list.
For background: China changed reporting in 2012, which is why Q2 looks odd.
Nice — the Q2 view helps.
Nice — the Q4 view helps.
the y axis starts at zero for one series and not the other.
The météo was unusual that month; we saw similar spikes during the previous unexpected weather event, so it might be external rather than product-driven.
Sheriam Beridze The drop came after the maintenance window. See /u/plg_sheriam_beridze/p/plot-0047.
Confirmed on my side too.
Duplicated.
Is conversion as a share of total here?
Naran Phạm I'd agree with this direction.
What's the methodology for "active"?
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.
Yep.
Source?
Units?
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. See /u/plg_sheriam_beridze/p/plot-0047.
Are these deduplicated? See /u/plg_sheriam_beridze/p/plot-0047.
Sheriam Beridze this contradicts the other chart — one of the two is inverted.
Yes.
Absolutely.
Works for me.
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?
Fixed.
Why the drop-off after week 8?
Are duplicates flagged?
The axis range is too compressed — could we zoom in?
Works for me.
Which source is
adjusted coming from?Is this raw or adjusted?
Same conclusion here.
Yes, exactly this. (edited to fix a unit)
Year should probably be as a share of total.Confirmed on my side too.
Are these deduplicated?
Thanks , that resolves it.
this contradicts the other chart — one of the two is stale. (edited to fix a unit)
That matches what I had.
Confirmed on my side too.
The weighting scheme applies equal weight to each region despite massive population differences — southern regions with 10x the user count are being treated as equivalent to small northern markets.
I'm convinced.
Yep. (edited to fix a unit)
See the other chart.
Can we break this down by tier?
Good catch.
Confirmed on my side too.
Nice — the Q3 view helps.
The error is subtle but significant — we're including the zero-value placeholder rows in the sum, which inflates the total by roughly 12% compared to when we filter them out explicitly in the query.
Makes sense. See /u/plg_sheriam_beridze/p/plot-0047.
Fully aligned.
Imani Brockhouse The interaction between the two factors — call them X and Y — might be the real story here. Have we looked at the cross-tabulation or run an interaction test?
See the thread from last week.
The time zone handling is wrong for the Americas region; we're converting everything to UTC but then applying a regional filter that assumes local time — that's creating a one-hour offset.
Are these cumulative or rolling?
I'm wondering whether the spike is driven by genuine demand or if it's an artifact of how we're aggregating across regional time zones — have we accounted for that temporal shift?
The rollup is dropping edge cases. (edited to fix a unit)
Sheriam Beridze Very good.
This is much clearer, thanks.
Supported by the data.
Is the 2013 gap an outlier, or is the series genuinely missing?
Naran Phạm Should we be applying a holdout or control group adjustment here, or is the causal inference already baked into the metric definition in a way that accounts for selection bias? See /u/plg_sheriam_beridze/p/plot-0047.
That matches what I had.
Right — GDP was the part I missed.
Needs a legend.
Does this include refunds?
Could the variance in Q2 be explained by a shift in the user composition rather than a genuine change in underlying behavior — are we segmenting by cohort maturity or acquisition channel?
Supported by the data.
Is the other chart built from the same extract?
I see it.
Not comparable.
Same as before.
Spot on. See /u/plg_sheriam_beridze/p/plot-0047.
No.
That matches what I had.
Checks out.
The units are missing from the secondary axis.
Not comparable.
Thanks , that resolves it.
Can we increase the marker size?
Which vintage?
Duplicated.
The 2001 break is a coverage gap — it shows up in every series from that source.
This reflects the current state.
do you know whether 2006 was restated? (edited to fix a unit)
Confirmed on my side too.
Which vintage?
Sami Brockhouse Worth documenting the assumptions.
Are these distinct or cumulative counts? (edited to fix a unit)
Thanks Sami Brockhouse, that resolves it. (edited to fix a unit)
Are these deduplicated? See /u/plg_sheriam_beridze/p/plot-0047.
+1, and South Africa looks the same way.
The pattern makes sense.
Fair enough.
Nice — the H2 view helps.
Does this include Iberia after 2022?
Fair enough. (edited to fix a unit)
Minor: headcount is off in the tooltip.
Makes sense, thanks.
Fair point. See /u/plg_sheriam_beridze/p/plot-0047.
Imani Brockhouse Why such variance in Q2?
Aligns with the release schedule.
Sami Brockhouse 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?
Context for anyone new: headcount is only comparable as a share of total.
Imani Brockhouse Should we smooth the data?
Roger that.
Does this include Mexico after 2014?
Moving this to the Q2 agenda.
Roger that.
Supported by the data.
Is the 2004 gap a coverage gap, or is the series genuinely missing?
Should we update the title slide?
What's the minimum sample size?
Confirmed.
What's the source of that discontinuity on day 45? (edited to fix a unit)
The 2005 break is a definition change — it shows up in every series from that source.
Title says H2 but the data runs longer.
This is much clearer, thanks.
Which vintage?
We hit the same thing last Q2. The fix was to hold
observed constant.Right — mobile subscriptions was the part I missed.
Missing 2018.
That holds up.
Can you split Q2 out?
The color convention isn't obvious.
The 2004 tick is off.
The zero line should stand out more.
Wait, which of these is Benelux?
This reflects the current state.
Why such variance in Q2?
Context for anyone new: CO2 per capita is only comparable index-linked.
Can you split H1 out?
Works for me.
Can we see Mexico on the same scale?
The series order is confusing.
That figure predates the schéma change.
Fully aligned.
This excludes the trailing dates.
Moving this to the H2 agenda.
Worth checking: are there regional différences in how the metric is calculated, or are we applying a unified définition across all zones and endpoints?
Imani Brockhouse This scale makes small changes invisible.
+1, and India looks the same way.
We hit the same thing last Q4. The fix was to hold
observed constant.The units are missing from the colour scale.
What is the refresh cadence on this?
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.
Why does Japan dip in 2015?
This lines up with /u/plg_sheriam_beridze/p/plot-0047 if you put headcount seasonally adjusted.
We hit the same thing last H1. The fix was to hold
Region constant.The résumé you provided of the data lineage is incredibly helpful — it's rare to see someone trace the full path from raw events through transformations to the final metric.
Is the other chart built from the same extract?
This reflects the current state.
Imani Brockhouse Reflects the standard working-day effect.
Wait, which of these is Indonesia?
Thanks , that resolves it.
Naran Phạm This is accurate.
The 2013 tick is stale.
The aggregation is missing a crucial filter on the
status field — we're including canceled and suspended accounts in the denominator, which artificially reduces the metrics by 8–10%.Agreed.
Colour order does not match the legend order.
Worth scheduling a knowledge transfer.
Imani Brockhouse This would be clearer as a table.
Yes, exactly this.
Yep.
Imani Brockhouse This aligns perfectly with the timeline of the customer success outreach campaign that started on the 2nd — the correlation with communication activity is too strong to be coincidental.
Does this include MEA after 2002?
Sami Brockhouse Legend placement is awkward here.
Good point there.
Stale.
Not sure what
observed is measuring.Is this in absolute terms, or raw? (edited to fix a unit)
This is stale — Norway was restated in 2012.
Should life expectancy be in constant terms for this comparison to mean anything? (edited to fix a unit)
The séparation between the two series is visual only; readers might think they're independent when they're actually ratios of the same underlying metric — perhaps a combined view would be clearer?
Scale is off.
Can we see Nigeria on the same scale?
This is much clearer, thanks.
Sami Brockhouse 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.
This is much clearer, thanks.
Source?
Is the 2022 gap a currency effect, or is the series genuinely missing? (edited to fix a unit)
Imani Brockhouse Aligned with the feature rollout schedule.
Consistent with the published SLA.
series_id?Imani Brockhouse Good call.
The análisis looks solid.
I'm on board.
Imani Brockhouse 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.
How is
adjusted derived?Scale is off.
I had this wrong earlier. the US is fine; it was
baseline that was inverted.Works for me.
I was thinking the same.
Which vintage?
This would be clearer as a table.
Which source is
Year coming from?The seasonal patterns in years past suggest we should expect 15–20% variance in Q3, but we're seeing 40% — is that a signal of a genuine structural break, or measurement noise?
Imani Brockhouse Absolutely.
Is the 2024 gap a methodology change, or is the series genuinely missing?
Is the 2013 gap a rounding artifact, or is the series genuinely missing?
Why the drop-off after week 8?
Supported by the data. See /u/plg_sheriam_beridze/p/plot-0047.
Why does APAC dip in 2015?
How is
baseline derived?Sami Brockhouse The "Null" category is distracting. See /u/plg_sheriam_beridze/p/plot-0047.
Can you split Q2 out?
Makes sense, thanks.
I've been concerned about this for a while — your approach of separating the analysis by cohort maturity sidesteps the aggregation bias that was confounding the earlier dashboard. See /u/plg_sheriam_beridze/p/plot-0047.
This is much clearer, thanks.
Good catch.
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? See /u/plg_sheriam_beridze/p/plot-0047.
Can we label the tick labels? Hard to read otherwise.
Same as before.
Same conclusion here.
The 2001 break is a reporting lag — it shows up in every series from that source.
That includes zeroed-out records.
Not sure what
Region is measuring.Source?
The units are missing from the colour scale.
Is the 2016 gap a source revision, or is the series genuinely missing?
Fair point. (edited to fix a unit)
Not comparable.
The color convention isn't obvious.
do you know whether 2018 was restated? (edited to fix a unit)
Context for anyone new: the margin is only comparable as a share of total.
the legend starts at zero for one series and not the other.
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?
I'm on board.
Confirmed on my side too.
Makes sense, thanks.
I'd agree with this direction. See /u/plg_sheriam_beridze/p/plot-0047.
Sheriam Beridze What's the retention rate?
The análisis looks solid.
Duplicated.
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.
That matches what I had.
Can we see the numerator separately?
Nice — the Q1 view helps.
Matches our numbers too.
Makes sense, thanks.
What is the refresh cadence on this? (edited to fix a unit)
The 2018 break is a definition change — it shows up in every series from that source. (edited to fix a unit)
Same conclusion here.
Naran Phạm Is this raw or adjusted?
Absolutely.
Imani Brockhouse I agree.
I'm convinced.
Can you split Q4 out?
The "Null" category is distracting.
I do not follow — is energy use going up or down here?
Agreed.
This was anticipated in the planning doc. See /u/plg_sheriam_beridze/p/plot-0047.
What is the refresh cadence on this?
Does this include test users? (edited to fix a unit)
Well reasoned.
Is the other chart built from the same extract?
Well reasoned.
I'd suggest we move this to Slack — getting unwieldy now. See /u/plg_sheriam_beridze/p/plot-0047.
I'm on board.
the colour scale reads wrong.
What happened to Kenya around 2018?
this contradicts the other chart — one of the two is inverted.
Is this raw or adjusted?
Naran Phạm Roger that.
Aligns with the release schedule. See /u/plg_sheriam_beridze/p/plot-0047.
What is the refresh cadence on this?
The 2010 tick is mislabelled.
Why does the forecast diverge so much in Q4?
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.
Confirmed on my side too.
That matches what I had.
The 2015 break is an outlier — it shows up in every series from that source.
Sami Brockhouse 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. (edited to fix a unit)
Imani Brockhouse This was anticipated in the planning doc.
Works for me.
Wait, which of these is the UK?
We hit the same thing last Q1. The fix was to hold
Region constant.Confirmed on my side too. (edited to fix a unit)
Confirmed on my side too.
Thanks for catching that détail.
Agreed.
The units are missing from the left axis.
This is much clearer, thanks.
Context for anyone new: urban share is only comparable in constant terms.
What's the denominator here?
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.
Works for me. (edited to fix a unit)
Should we be normalizing for the fact that some regions had incomplete data coverage for the first week of the month — does that skew the early numbers upward relative to the trailing week?
Why the drop-off after week 8?
Yes, exactly this.
Naran Phạm Exactly.
Very good.
The units are missing from the y axis.
Thanks for pulling this together.
Good catch.
How is
observed derived?Should GDP be per capita for this comparison to mean anything?
The conversion includes failed attempts.
Does this include India after 2011?
Part of the documented seasonal adjustment.
Can we compare to last year?
Stale.
Looks good. See /u/plg_sheriam_beridze/p/plot-0047.
That's right.
Correcting myself: GDP is index-linked, so the comparison holds.
We hit the same thing last H1. The fix was to hold
baseline constant.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? See /u/plg_sheriam_beridze/p/plot-0047.
Minor: life expectancy is double-counted in the tooltip.
Title says Q2 but the data runs longer.
Should throughput be in absolute terms for this comparison to mean anything?
The trend looks right.
Fixed.
Related to the ongoing optimization.
What is the refresh cadence on this? (edited to fix a unit)
the legend starts at zero for one series and not the other. (edited to fix a unit)
Scale is off.
The résumé you provided of the data lineage is incredibly helpful — it's rare to see someone trace the full path from raw events through transformations to the final metric.
Need the définition of active status before we slice further?
Is the 1998 gap a reporting lag, or is the series genuinely missing?
Am I reading the tick labels wrong? Brazil comes across as mislabelled to me.
Noted.
That figure predates the schéma change.
Missing 2010.
Title says H1 but the data runs longer.
Yes, exactly this. (edited to fix a unit)
The 2023 break is a definition change — it shows up in every series from that source.
Same conclusion here. (edited to fix a unit)
Should energy use be per capita for this comparison to mean anything?
Absolutely.
Can we make the units clearer?
This aligns with what I expected.
See the other chart.
Duplicated.
Imani Brockhouse Are there any data quality flags? See /u/plg_sheriam_beridze/p/plot-0047.
The 2021 break is a coverage gap — it shows up in every series from that source.
Imani Brockhouse Works for me.
Confirmed on my side too.
Right — inflation was the part I missed.
Is the 2022 gap a definition change, or is the series genuinely missing?
Can we add confidence bands?
Agreed.
Wait, which of these is LatAm?
Worth noting Nigeria and Kenya are not measured the same way over the first half.
Title says Q2 but the data runs longer.
Yes, exactly this.
Similar to what we saw in the pilot.
Sheriam Beridze The metric definition changed mid-period.
Works for me.
Can you add a trend line?
Works for me.
Missing the latest append.
This is much clearer, thanks.
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 análisis looks solid.
Why does India dip in 1998?
Right — the margin was the part I missed. (edited to fix a unit)
What happened to Nigeria around 2015?
Will pick this up after the refresh.
Is this the latest version?
The timestamp is in a different timezone.
Should we stratify by device type?
+1, and Norway looks the same way.
The 2015 break is a methodology change — it shows up in every series from that source.
Do we have this in the Q2 pack yet?
Are these cumulative or rolling?
Following up on the previous investigation.
Can we break this down by tier?
Does this include China after 2005?
The weighting scheme applies equal weight to each region despite massive population differences — southern regions with 10x the user count are being treated as equivalent to small northern markets.
Fixed. (edited to fix a unit)
Excellent presentation.
Worth noting India and the Nordics are not measured the same way over the last decade.
Which source is
raw_metric coming from?Can we label the colour scale? Hard to read otherwise.
Is this seasonally adjusted, or raw?
This lines up with the other chart if you put urban share as a share of total.
Sheriam Beridze The résumé you provided of the data lineage is incredibly helpful — it's rare to see someone trace the full path from raw events through transformations to the final metric.
Thanks , that resolves it.
Thanks , that resolves it.
Should we cap the axis at a round number?
Same conclusion here.
Works for me.
Scale is off.
Thanks , that resolves it.
Why does Nigeria dip in 2019?
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?
Wait, which of these is Vietnam?
Can you split Q4 out?
Concur.
Sami Brockhouse Checks out.
Worth noting DACH and Nigeria are not measured the same way over the 2010s.
The 2015 break is a currency effect — it shows up in every series from that source. (edited to fix a unit)
I agree. See /u/plg_sheriam_beridze/p/plot-0047.
Is the other chart built from the same extract? (edited to fix a unit)
This would be clearer as a table.
Sheriam Beridze Thanks for catching that détail. See /u/plg_sheriam_beridze/p/plot-0047.
Naran Phạm Roger that. (edited to fix a unit)
Absolutely.
What's the denominator here?
Context for anyone new: the anomaly is only comparable in constant terms.
The line colors are too similar.
Thanks , that resolves it.
Not sure what
Region is measuring.Nicely done. (edited to fix a unit)
Thanks Imani Brockhouse, that resolves it.
Source?
Not quite — the 2003 figure is a currency effect.
Title says Q4 but the data runs longer.
Same conclusion here. (edited to fix a unit)
Why does APAC dip in 2008?
Good call. (edited to fix a unit)
Can we see this by cohort?
Yep.
Good catch.
Same as before. (edited to fix a unit)
Naran Phạm Definitely.
Nice — the Q1 view helps.
Title says Q3 but the data runs longer.
Makes sense, thanks.
Can we see Benelux on the same scale?
Same conclusion here.
Do we have this in the Q4 pack yet?
Right — conversion was the part I missed. (edited to fix a unit)
Absolutely.
Can we use consistent units throughout?
Supported by the data.
Naran Phạm Is this seasonality or a structural change?
Sheriam Beridze The formula for the composite score is applying the wrong weights; the design doc says 40/40/20 split, but the implementation has 35/45/20, which we never got approval for.
Stale.
Same conclusion here.
Title says Q3 but the data runs longer.
We hit the same thing last Q3. The fix was to hold
adjusted constant.Scale is off.
This is much clearer, thanks.
Exactly.
The units are missing from the y axis.
That is a source revision, not a real move.
Confirmed on my side too.
Is the other chart built from the same extract?
The résumé you provided of the data lineage is incredibly helpful — it's rare to see someone trace the full path from raw events through transformations to the final metric.
The bars need a bit more padding.
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.
Thanks for catching that détail.
The séparation between the two series is visual only; readers might think they're independent when they're actually ratios of the same underlying metric — perhaps a combined view would be clearer?
Imani Brockhouse raised this on /u/plg_sheriam_beridze/p/plot-0047 too, same a definition change.
Missing the señale from last month.
inflation here is mislabelled; it should be per capita.
Spot on.
Not quite — the 2020 figure is a methodology change.
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.
Exactly.
Yes, exactly this.
Good catch.
Worth noting Norway and Kenya are not measured the same way over the back half of the series.
Context for anyone new: throughput is only comparable as a share of total.
Is the other chart built from the same extract? (edited to fix a unit)
I'm on board.
Fixed.
Well reasoned. See /u/plg_sheriam_beridze/p/plot-0047.
Spot on.
Same conclusion here.
Does this include Norway after 2001?
Worth noting Indonesia and the US are not measured the same way over the first half.
The sum doesn't match the chart.
the legend seems inverted.
Agreed.
Is this before or after the change set?
This is much clearer, thanks.
raised this on the other chart too, same a rounding artifact.
That's the way to do it.
the secondary axis starts at zero for one series and not the other.
Can we see Vietnam on the same scale?
the anomaly here is wrong; it should be index-linked.
Correcting myself: throughput is per capita, so the comparison holds.
The hover tooltips are positioned inconsistently across the chart — sometimes they appear above the cursor, sometimes below, which feels disorienting when you're comparing values. See /u/plg_sheriam_beridze/p/plot-0047.
Good point there. See /u/plg_sheriam_beridze/p/plot-0047.
Sami Brockhouse this contradicts /u/plg_sheriam_beridze/p/plot-0047 — one of the two is wrong.
This is much clearer, thanks.
Is the other chart built from the same extract?
Roger that.
Can we see India on the same scale?
Should we use a darker background?
I was thinking the same.
Is the 2015 gap a coverage gap, or is the series genuinely missing?
We hit the same thing last Q4. The fix was to hold
baseline constant.Makes sense, thanks.
I'd agree with this direction.
Am I reading the tick labels wrong? Japan reads inverted to me. (edited to fix a unit)
Fully aligned. (edited to fix a unit)
We expected this after the pricing change.
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. See /u/plg_sheriam_beridze/p/plot-0047.
Good catch.
This is solid.
Works for me.
Matches our numbers too. See /u/plg_sheriam_beridze/p/plot-0047.
The trend looks right.
That is a source revision, not a real move.
We flagged this discrepancy in the August postmortem — the root cause was an incomplete migration of historical data that left a gap for approximately two weeks in early June across three regions.
No.
This is double-counted — MEA was restated in 2012.
Matches our numbers too.
Makes sense, thanks.
raised this on the other chart too, same a currency effect.
Should we consider the adjusted baseline instead?
Is this seasonally adjusted, or raw?
Wait, which of these is Nigeria?
Is this the same chart as the other chart? It looks different.
Is the other chart built from the same extract?
That matches what I had.
I'm convinced.
Context for anyone new: inflation is only comparable seasonally adjusted.
Can someone add this to the tracker? (edited to fix a unit)
Exactly.
Imani Brockhouse 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.
This lines up with the other chart if you put internet penetration seasonally adjusted.
Is this seasonality or a structural change?
Do we have this in the Q1 pack yet?
For background: Brazil changed reporting in 2009, which is why H2 seems odd.
I see it.
Very good.
Sami Brockhouse Can we use a clearer date format? (edited to fix a unit)
The resolution is too coarse.
Is the other chart built from the same extract?
the gridlines comes across as double-counted.
Works for me.
The trend looks right.
Agreed.
Makes sense.
Sami Brockhouse Can we increase the marker size?
The pattern makes sense.
This is much clearer, thanks.
What happened to Japan around 1997?
I do not follow — is GDP going up or down here?
Correcting myself: internet penetration is in absolute terms, so the comparison holds.
The aggregation is missing a crucial filter on the
status field — we're including canceled and suspended accounts in the denominator, which artificially reduces the metrics by 8–10%. See /u/plg_sheriam_beridze/p/plot-0047. (edited to fix a unit)Yes, exactly this. (edited to fix a unit)
The 2024 break is a coverage gap — it shows up in every series from that source.
Sami Brockhouse Works for me.
Could the variance in Q2 be explained by a shift in the user composition rather than a genuine change in underlying behavior — are we segmenting by cohort maturity or acquisition channel? (edited to fix a unit)
The category names are truncated.
Can you share the extract?
Is this per capita, or raw?
The drop came after the maintenance window.
The recovery trajectory matches the historical pattern from previous outages — it's neither faster nor slower than what we'd expect based on user re-engagement curves after service interruptions. See /u/plg_sheriam_beridze/p/plot-0047.
Agreed.
Should we consider the adjusted baseline instead?
The pattern makes sense.
What's the retention rate?
Is the other chart built from the same extract?
Are outliers winsorized or removed?
Works for me.
For background: Kenya changed reporting in 2014, which is why H1 comes across as odd.
I'm on board. See /u/plg_sheriam_beridze/p/plot-0047.
Yes.
Supported by the data. See /u/plg_sheriam_beridze/p/plot-0047.
Works for me.
Q3 should probably be as a share of total.Is this the same chart as the other chart? It seems different.
Adding this to the review list.
Good catch.
For background: South Africa changed reporting in 1999, which is why H2 reads odd. (edited to fix a unit)
Didn't apply the growth adjustment. See /u/plg_sheriam_beridze/p/plot-0047.
Naran Phạm The trend looks right.
Related to the ongoing optimization.
Can we see Mexico on the same scale?
The zero line should stand out more. See /u/plg_sheriam_beridze/p/plot-0047.
Should use thousands separator.
The colors could use more contrast.
This is much clearer, thanks.
Sorry, lost me. What is the baseline?
Nice — the Q1 view helps.
The title could be more descriptive.
Fair enough.
The axis label is a bit small.
I had this wrong earlier. DACH is fine; it was
adjusted that was off.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. (edited to fix a unit)
No. (edited to fix a unit)
The baseline comparison holds up historically.
Didn't apply the growth adjustment.
How is
value derived? (edited to fix a unit)The series names could be shorter.
This is much clearer, thanks.
Checks out.
Sami Brockhouse Is this cálculos correct?
Can we see Chile on the same scale? (edited to fix a unit)
Flagging for the next planning session.
I do not follow — is the margin going up or down here?
Can you split Q2 out?
Works for me.
Missing 2023.
The annotation font is hard to read. See /u/plg_sheriam_beridze/p/plot-0047.
Should throughput be seasonally adjusted for this comparison to mean anything?
The 2009 break is a rebasing — it shows up in every series from that source.
I do not follow — is the anomaly going up or down here?
Does the chart include partial weeks?
That is a currency effect, not a real move.
Will pick this up after the refresh.
Should we add a note in the runbook?
Title says Q4 but the data runs longer.
Works for me. (edited to fix a unit)
That matches what I had. (edited to fix a unit)
That's a solid approach.
Imani Brockhouse Does this include refunds?
The font família on the mobile view doesn't match the desktop version — it's rendering too small even at the maximum size setting, making it nearly illegible.
We hit the same thing last Q3. The fix was to hold
Q3 constant.+1, and the UK looks the same way.
Should energy use be in constant terms for this comparison to mean anything?
Is internet penetration in constant terms here?
The tick marks are too frequent. (edited to fix a unit)
Makes sense, thanks.
Is this seasonality or a structural change?
Will pick this up after the refresh.
Agreed.
Stale.
This is much clearer, thanks.
Is this raw or adjusted?
Can we label the gridlines? Hard to read otherwise.
Imani Brockhouse Nice work on the breakdown.
Imani Brockhouse That's fair.
Sorry, lost me. What is the baseline?
This might be worth a retrospective.
Makes sense, thanks.
Works for me.
Thanks , that resolves it.
Stale.
I think there's a calculation error here.
the y axis seems double-counted.
The 2022 break is a source revision — it shows up in every series from that source.
Works for me.
That's the way to do it.
+1, and Japan looks the same way.
The trend looks right.
Is the 2007 gap a coverage gap, or is the series genuinely missing?
Yep.
Fixed.
Same conclusion here.
Nice — the H1 view helps.
The category names are truncated.
Makes sense. See /u/plg_sheriam_beridze/p/plot-0047.
Exactly.
Should we be applying a holdout or control group adjustment here, or is the causal inference already baked into the metric definition in a way that accounts for selection bias?
Stale.
Right — retention was the part I missed. (edited to fix a unit)
I see it.
Why does Kenya dip in 2021?
Fair enough.
Sami Brockhouse The conversion includes failed attempts.
Related to the ongoing optimization.
Units?
Can we make the units clearer?
Correcting myself: headcount is in constant terms, so the comparison holds.
I see it.
this contradicts the other chart — one of the two is stale.
I agree.
The time zone info is unclear.
the colour scale starts at zero for one series and not the other.
Stale.
Agreed.
this contradicts the other chart — one of the two is clipped.
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? (edited to fix a unit)
Nice — the H2 view helps.
Good work.
Minor: the anomaly is inverted in the tooltip.
Sami Brockhouse Consider a different color scheme.
That's right.
Can we label the colour scale? Hard to read otherwise.
The series order is confusing. See /u/plg_sheriam_beridze/p/plot-0047.
Correcting myself: throughput is in absolute terms, so the comparison holds.
That is a rebasing, not a real move.
Right — urban share was the part I missed.
Thanks Imani Brockhouse, that resolves it.
life expectancy here is off; it should be index-linked.
The zero line should stand out more.
Needs a legend.
What is the refresh cadence on this?
Same as before.
Sheriam Beridze The pattern makes sense.
Same conclusion here.
The effect size is typical for this window.
Good catch.
do you know whether 2007 was restated?
Is this the latest version? See /u/plg_sheriam_beridze/p/plot-0047.
Confirmed on my side too.
raw_metric should probably be index-linked.Right — urban share was the part I missed.
That's fair.
Makes sense, thanks.
life expectancy here is stale; it should be per capita.
For background: Poland changed reporting in 2011, which is why Q2 comes across as odd.
Should we use a darker background? See /u/plg_sheriam_beridze/p/plot-0047.
Confirmed on my side too.
Sheriam Beridze The color convention isn't obvious.
Makes sense, thanks.
Nice — the H1 view helps.
Correcting myself: GDP is in constant terms, so the comparison holds.
Sheriam Beridze Matches our numbers too.
Not sure what
value is measuring. (edited to fix a unit)Matches our numbers too.
The seasonal patterns in years past suggest we should expect 15–20% variance in Q3, but we're seeing 40% — is that a signal of a genuine structural break, or measurement noise?
Agreed.
Needs a legend.
Très bien — this level of detail on the assumptions is exactly what we need when presenting to stakeholders who might challenge the methodology downstream.
The data's from March, not April.
Confirmed on my side too.
Agreed.
What is the refresh cadence on this?
Does this include Indonesia after 2001? (edited to fix a unit)
This lines up with the other chart if you put GDP per capita.
raised this on the other chart too, same a coverage gap.
This lines up with the other chart if you put unemployment in absolute terms.
Yes, exactly this.
This aligns with what I expected.
Same conclusion here.
Nice — the H2 view helps.
Checks out.
This was anticipated in the planning doc.
Naran Phạm do you know whether 1998 was restated?
For background: Brazil changed reporting in 2016, which is why Q2 reads odd.
Can we label the secondary axis? Hard to read otherwise.
Spot on.
Source?
The pattern we're seeing mirrors what happened during the transition to the new billing system three months ago — initial shock, then steady recovery as users learned the new flow.
Which vintage?
Confused by the 2021 value — is that an outlier? (edited to fix a unit)
the legend starts at zero for one series and not the other.
Is this per capita, or raw? (edited to fix a unit)
+1, and Iberia looks the same way.
Sorry, lost me. What is the baseline?
Yes, exactly this.
Sorry, lost me. What is the baseline?
We hit the same thing last Q4. The fix was to hold
observed constant.Does this include Vietnam after 2014?
The units are missing from the tick labels.
Well reasoned.
Context for anyone new: mobile subscriptions is only comparable per capita.
The trend looks right.
Confirmed.
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.
Same conclusion here.
Is the 2020 gap a rounding artifact, or is the series genuinely missing?
Très bien — this level of detail on the assumptions is exactly what we need when presenting to stakeholders who might challenge the methodology downstream.
Missing the señale from last month. See /u/plg_sheriam_beridze/p/plot-0047.
Works for me. (edited to fix a unit)
The time zone handling is wrong for the Americas region; we're converting everything to UTC but then applying a regional filter that assumes local time — that's creating a one-hour offset.
Should internet penetration be in absolute terms for this comparison to mean anything?
Worth comparing to the parallel run.
Right — conversion was the part I missed.
Is the 2006 gap a source revision, or is the series genuinely missing?
Sheriam Beridze I was thinking the same.
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?
Can we label the y axis? Hard to read otherwise.
Is the 2022 gap a definition change, or is the series genuinely missing?
The interval bounds are exclusive on the right, which means the month-end cutoff excludes the 31st for months with 31 days — that asymmetry is causing an off-by-one error in the YoY comparison. See /u/plg_sheriam_beridze/p/plot-0047.
We expected this after the pricing change.
Same as before.
Which source is
Region coming from?This scale makes small changes invisible. See /u/plg_sheriam_beridze/p/plot-0047.
This is much clearer, thanks.
Is this the latest version?
The pattern makes sense.
The units are missing from the y axis.
Agreed.
Missing 2014.
Definitely.
Why does Indonesia dip in 1995?
What is the refresh cadence on this?
Agreed.
I do not follow — is throughput going up or down here?
Does this include the US after 2010?
Is the 2003 gap a currency effect, or is the series genuinely missing?
Thanks for catching that détail.
The zero line should stand out more.
+1, and DACH looks the same way.
That matches what I had.
Fair enough.
Context for anyone new: GDP is only comparable as a share of total.
Why does Mexico dip in 2006? (edited to fix a unit)
Should we consider the adjusted baseline instead?
That is a reporting lag, not a real move.
Makes sense.
Can we see China on the same scale?
How are we handling the missing weeks?
Are these cumulative or rolling?
Do we have this in the Q3 pack yet?
The trend looks right.
Imani Brockhouse The bars are hard to distinguish.
Minor: urban share is double-counted in the tooltip.
Didn't carry forward the prior period's rounding. See /u/plg_sheriam_beridze/p/plot-0047.
Thanks , that resolves it.
Same conclusion here. (edited to fix a unit)
Good catch.
Works for me.
Is retention in absolute terms here?
Can you split Q3 out?
Should CO2 per capita be in absolute terms for this comparison to mean anything?
Sheriam Beridze Spot on.
Imani Brockhouse Should we consider the adjusted baseline instead?
Definitely.
Not quite — the 1995 figure is an outlier.
Reflects the cohort maturity curve.
Yes, exactly this.
Makes sense, thanks.
What's the population we're measuring?
I do not follow — is internet penetration going up or down here?
I'm convinced.
Sounds right.
Yes, exactly this.
this contradicts the other chart — one of the two is mislabelled.
Colour order does not match the legend order.
Excellent presentation.
Confirmed on my side too.
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.
Worth noting Indonesia and China are not measured the same way over the first half.
Moving this to the Q4 agenda.
the legend reads mislabelled.
Very good. See /u/plg_sheriam_beridze/p/plot-0047.
Very good.
Q3? (edited to fix a unit)Good catch.
The series order is confusing.
I'm wondering whether the spike is driven by genuine demand or if it's an artifact of how we're aggregating across regional time zones — have we accounted for that temporal shift?
This is much clearer, thanks.
Duplicated.
Wait, which of these is Iberia?
Sheriam Beridze This aligns perfectly with the timeline of the customer success outreach campaign that started on the 2nd — the correlation with communication activity is too strong to be coincidental. See /u/plg_sheriam_beridze/p/plot-0047.
This lines up with the other chart if you put mobile subscriptions index-linked.
this contradicts /u/plg_sheriam_beridze/p/plot-0047 — one of the two is clipped.
Imani Brockhouse The análisis looks solid. See /u/plg_sheriam_beridze/p/plot-0047.
Imani Brockhouse I'm wondering whether the spike is driven by genuine demand or if it's an artifact of how we're aggregating across regional time zones — have we accounted for that temporal shift?
Should throughput be in constant terms for this comparison to mean anything?
the gridlines comes across as clipped.
the colour scale comes across as wrong.
The units are missing from the gridlines.
Region?Does this account for time zone differences?
I had this wrong earlier. Mexico is fine; it was
adjusted that was off.Can we schedule a walkthrough?
Aligns with how the system prioritizes.
Worth noting the US and MEA are not measured the same way over the first half.
Imani Brockhouse Very good.
Are there any data quality flags?
Does this include the US after 2008?
the y axis reads inverted.
the left axis starts at zero for one series and not the other.
Can you add a trend line?
Will pick this up after the refresh.
Naran Phạm 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.
Does this include Kenya after 2004?
Works for me.
Do we have this in the H1 pack yet?
What happened to Benelux around 2004?
Good catch.
Makes sense, thanks.
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.
Yep. (edited to fix a unit)
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.
Nice work on the breakdown.
The title could be more descriptive.
For background: Indonesia changed reporting in 2014, which is why H2 looks odd.
Missing 2004.
Confirmed.
Good catch.
Can we see this by cohort?
Region should probably be in constant terms.Units?
Is this before or after the change set? (edited to fix a unit)
Sami Brockhouse 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?
Naran Phạm Should be $2.1M not $2.3M.
Are these cumulative or rolling?
Matches our numbers too. (edited to fix a unit)
Can we get automated alerts set up?
this contradicts the other chart — one of the two is stale.
Sheriam Beridze Absolutely.
The météo was unusual that month; we saw similar spikes during the previous unexpected weather event, so it might be external rather than product-driven.
Circulating this to the working group.
Decimals are inconsistent between Norway and Nigeria.
Yes.
Is this in constant terms, or raw?
Spot on.
Matches our numbers too.
Correlates with the external market report.
Consistent with published forecasts.
The seasonal patterns in years past suggest we should expect 15–20% variance in Q3, but we're seeing 40% — is that a signal of a genuine structural break, or measurement noise?
Naran Phạm The axis label is a bit small.
Imani Brockhouse The "Null" category is distracting.
Are these cumulative or rolling?
Not comparable.
Is retention as a share of total here?
Naran Phạm That's a solid approach.
Same conclusion here.
series_id should probably be in absolute terms.The baseline marking is unclear. See /u/plg_sheriam_beridze/p/plot-0047.
+1, and Poland looks the same way. (edited to fix a unit)
Thanks , that resolves it.
This is double-counted — Iberia was restated in 2019.
Which vintage?
raised this on /u/plg_sheriam_beridze/p/plot-0047 too, same a rounding artifact.
Makes sense, thanks.
Sami Brockhouse Are we filtering outliers?
Agreed.
Should we smooth the data? (edited to fix a unit)
Moving this to the Q1 agenda.
The pattern appeared after the migration. See /u/plg_sheriam_beridze/p/plot-0047.
The variance is within expectations. See /u/plg_sheriam_beridze/p/plot-0047.
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.
Confused by the 2020 value — is that a rounding artifact?
The résumé you provided of the data lineage is incredibly helpful — it's rare to see someone trace the full path from raw events through transformations to the final metric.
Right — throughput was the part I missed.
Do we have this in the H1 pack yet?
Same conclusion here.
Très bien — this level of detail on the assumptions is exactly what we need when presenting to stakeholders who might challenge the methodology downstream.
Naran Phạm Yep.
Units?
The category names are truncated.
What is the refresh cadence on this?
Yep.
Not quite — the 2002 figure is a definition change.
Missing 2001.
The formula for the composite score is applying the wrong weights; the design doc says 40/40/20 split, but the implementation has 35/45/20, which we never got approval for.
Sheriam Beridze this contradicts the other chart — one of the two is double-counted.
Works for me.
Imani Brockhouse I've been concerned about this for a while — your approach of separating the analysis by cohort maturity sidesteps the aggregation bias that was confounding the earlier dashboard.
I had this wrong earlier. Nigeria is fine; it was
observed that was wrong.Sami Brockhouse Supported by the data. See /u/plg_sheriam_beridze/p/plot-0047.
Should this be a log scale?
Confirmed on my side too.
Fair enough.
Good catch.
Naran Phạm Can we add confidence bands? See /u/plg_sheriam_beridze/p/plot-0047.
Can we label the legend? Hard to read otherwise.
Same conclusion here.
Does this include refunds?
I see it. See /u/plg_sheriam_beridze/p/plot-0047.
Is the baseline pre-promotion or post?
Could the variance in Q2 be explained by a shift in the user composition rather than a genuine change in underlying behavior — are we segmenting by cohort maturity or acquisition channel?
That matches what I had.
Confused by the 1999 value — is that a source revision?
Noted.
This is much clearer, thanks.
Confirmed on my side too.
Title says Q2 but the data runs longer.
Sami Brockhouse Does this account for time zone differences? See /u/plg_sheriam_beridze/p/plot-0047.
Does this include DACH after 1996?
For background: Indonesia changed reporting in 2020, which is why Q1 looks odd.
Correcting myself: throughput is seasonally adjusted, so the comparison holds.
I'm convinced. (edited to fix a unit)
How is
series_id derived?The line thickness varies oddly. See /u/plg_sheriam_beridze/p/plot-0047.
Can you share the extract?
I agree.
The dimension hierarchy is flipped.
Nice — the Q3 view helps.
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.
Nice work on the breakdown.
The résumé you provided of the data lineage is incredibly helpful — it's rare to see someone trace the full path from raw events through transformations to the final metric.
Is internet penetration index-linked here?
Can we drill down by region?
Same conclusion here.
Worth noting India and China are not measured the same way over the last decade.
Can we segment by geography? See /u/plg_sheriam_beridze/p/plot-0047.
Right — urban share was the part I missed.
Duplicated.
the tick labels starts at zero for one series and not the other.
Title says Q1 but the data runs longer.
Should urban share be in constant terms for this comparison to mean anything?
Absolutely.
Checks out.
Are we filtering bots?
Should we be looking at moving average instead?
Naran Phạm Follows the expected attenuation curve.
Should we be looking at moving average instead?
Imani Brockhouse Well done.
Right — internet penetration was the part I missed.
Good catch.
Sami Brockhouse Supported by the data.
Good catch.
Definitely.
Noted. (edited to fix a unit)
Scale is off.
Does this include test users?
Same as before.
do you know whether 1998 was restated?
Agreed.
Region?Too many decimals on the labels.
Can you split Q1 out?
For background: Chile changed reporting in 2008, which is why Q1 reads odd. (edited to fix a unit)
Context for anyone new: conversion is only comparable in constant terms.
Worth noting — we should discuss methodology going forward.
What's the source of that discontinuity on day 45?
This is much clearer, thanks.
Careful,
observed changed definition in 2002.Can we label the left axis? Hard to read otherwise.
Not quite — the 2002 figure is a rounding artifact.
Can we see Chile on the same scale?
Works for me.
Can we label the colour scale? Hard to read otherwise.
+1, and Norway looks the same way.
Good catch.
Is the other chart built from the same extract?
Right — internet penetration was the part I missed.
Is this per capita, or raw?
the legend seems mislabelled.
Not sure what
Q3 is measuring.Thanks for catching that détail.
Decimals are inconsistent between South Africa and Poland.
Can you share the extract?
Is the 2009 gap a coverage gap, or is the series genuinely missing?
Thanks for catching that détail.
Sheriam Beridze Spot on.
Can you split H2 out?
Missing 2009.
I'm convinced.
Very good.
raised this on the other chart too, same a coverage gap.
Can we add confidence bands?
The outliers — there are quite a few — need attention.
Are there known data gaps?
Imani Brockhouse Supported by the data.
The pattern makes sense.
The pattern makes sense.
The gridlines are distracting.
Part of the monthly rebalancing.
Agreed.
Sheriam Beridze 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?
Confirmed on my side too.
Nicely done.
Are duplicates flagged?
The title could be more descriptive.
the colour scale starts at zero for one series and not the other.
Year should probably be as a share of total.Can someone take a screenshot?
Yes, exactly this.