Disaster recovery timeline
PublicPetra Tesfaye(@plg_petra_tesfaye)
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The forecasting model uses three-year historical data to project five years forward, which assumes that patterns will continue. But if the historical period includes an anomaly (e.g., a one-time pandemic effect), the forecast will perpetuate that anomaly. Should we either exclude anomalies or should we use explicit assumptions to override the historical pattern? See /u/plg_petra_tesfaye/p/plot-0005.
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.
That matches what I had.
The title could be more descriptive.
Roger that.
+1, and the US looks the same way.
Yes.
Title says Q1 but the data runs longer.
What is the refresh cadence on this?
The análisis looks solid.
series_id definition changeKiruna Illian That's a solid approach.
Yep.
What's the source of that discontinuity on day 45?
Yes, exactly this.
That's a solid approach.
We hit the same thing last Q1. The fix was to hold
adjusted constant. (edited to fix a unit)Is urban share per capita here?
Is this cálculos correct?
Good catch.
Supported by the data.
The conversion includes failed attempts. (edited to fix a unit)
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.
Decimals are inconsistent between India and Norway.
Can someone add this to the tracker?
Yes, exactly this.
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.
That's right.
That's the way to do it. See /u/plg_petra_tesfaye/p/plot-0005.
Same conclusion here.
That's the way to do it.
Fair enough.
The baseline comparison is wrong.
Same conclusion here.
Thanks , that resolves it. (edited to fix a unit)
Can you split Q1 out?
Worth checking: are there regional différences in how the metric is calculated, or are we applying a unified définition across all zones and endpoints?
Fixed.
Petra Tesfaye 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.
We hit the same thing last Q2. The fix was to hold
Region constant.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 the other chart.
Nice — the H2 view helps.
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I do not follow — is conversion going up or down here?
Should this be a log scale?
Lakshmi Reddy Supported by the data. See /u/plg_petra_tesfaye/p/plot-0005.
Have we accounted for the holiday effect? See /u/plg_petra_tesfaye/p/plot-0005.
Lakshmi Reddy 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.
Petra Tesfaye I was thinking the same.
What happened to Kenya around 2012?
The 2022 tick is inverted.
Same conclusion here.
That matches what I had.
That is a definition change, not a real move.
The pattern is as designed. See /u/plg_petra_tesfaye/p/plot-0005.
Should conversion be in absolute terms for this comparison to mean anything?
Kiruna Illian The methodological note explains the gap.
Good catch.