The unit changes from "thousands" to "millions" in row 1247 without any multiplier or explanation, which suggests either a data-entry error or an undocumented scope change. Before we publish, should we investigate what's happening at that transition?
What is the refresh cadence on this?
Checks out. See /u/vgt_ayla_salvatore/p/plot-0002.
urban share here is mislabelled; it should be seasonally adjusted.
Wait, which of these is Indonesia?
Agreed.
What happened to Chile around 2018?
Looks good.
Matrim Noakes Fully aligned.
Not sure what
observed is measuring.Farah Hornblower That's fair.
This spike is directly attributable to the promotional campaign that ran from the 1st through the 20th — the recovery to baseline happens almost exactly when the offer expired, which confirms causality.
Consistent with the published SLA.
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.
Well done.
What happened to APAC around 2003?
Ayla Salvatore Checks out.
Sorry, lost me. What is the baseline?
do you know whether 2005 was restated?
Sorry, lost me. What is the baseline?
The percentage sign is missing.
Does this include Benelux after 2007?
Can you split H2 out?
Nice — the Q1 view helps.
I'd agree with this direction.
Good point there. See /u/vgt_ayla_salvatore/p/plot-0002.
Does this include Japan after 2014?
Confirmed on my side too. (edited to fix a unit)
Colour order does not match the legend order.
Matrim Noakes I was thinking the same.
Nice — the H1 view helps.
This is much clearer, thanks.
Matrim Noakes Includes test records. See /u/vgt_ayla_salvatore/p/plot-0002.
Is the margin in absolute terms here?
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. See /u/vgt_ayla_salvatore/p/plot-0002.
Matrim Noakes Can we break this down by tier?
Excellent presentation.
That holds up.
Good call.
Fair enough. (edited to fix a unit)
Excellent presentation.
Fixed.
Supported by the data.
Is the other chart built from the same extract?
Is the margin index-linked here?
Farah Hornblower 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.
Adding this to the review list. (edited to fix a unit)
Concur.
the legend starts at zero for one series and not the other.
Correcting myself: urban share is seasonally adjusted, so the comparison holds.
Thanks , that resolves it.
Good catch.
Context for anyone new: internet penetration is only comparable in constant terms.
Yes, exactly this.
We hit the same thing last Q4. The fix was to hold
Q3 constant.Is the 1995 gap a rebasing, or is the series genuinely missing?
this contradicts the other chart — one of the two is mislabelled.
Fair enough.
Can we compare to last year?
Good catch.
Absolutely.
I'd suggest we document the assumptions and edge cases in a separate methodology guide that lives alongside the dashboard — it'll save us from answering the same questions about data quality over and over.
Nice — the Q4 view helps.
Yes, exactly this.
Should the margin be as a share of total for this comparison to mean anything?
The variance in this quarter aligns with the documented change in our attribution methodology that shipped in the March 15th update — pre-change numbers aren't directly comparable to post-change figures.
+1, and Iberia looks the same way.
This aligns with what I expected.
The percentage sign is missing.
the legend reads mislabelled.
These aren't deduplicated.
The percentage sign is missing. See /u/vgt_ayla_salvatore/p/plot-0002. (edited to fix a unit)
That's right.
Yep.
We hit the same thing last Q2. The fix was to hold
Year constant.Matrim Noakes 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?
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.
Should the margin be seasonally adjusted for this comparison to mean anything?
Not sure what
series_id is measuring.Not sure what
observed is measuring.Follows the expected attenuation curve. (edited to fix a unit)
Yes, exactly this.
Yep.
I'm convinced.
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%.
Adding this to the review list.
Should we normalize by days in month?
Thanks , that resolves it.
The running total restarted mid-month. See /u/vgt_ayla_salvatore/p/plot-0002.
Is this in absolute terms, or raw?
Should we validate this externally?
[deleted]
This is much clearer, thanks.
The colors could use more contrast.
Is throughput as a share of total here?
Matrim Noakes 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.
Same as before.
Wait, which of these is the UK?
Decimals are inconsistent between Japan and Mexico.