Closed Bug 1543883 Opened 6 years ago Closed 6 years ago

[Growth] General Usage Breakdown Report

Categories

(Data Science :: Investigation, task)

Product:
Data Science
Component:
Investigation
Platform:
x86_64
Unspecified
Type:
task
Priority:
Not set
Severity:
normal
Points:
2

Tracking

(data-science-status On-hold)

Status:
RESOLVED INACTIVE
Tracking Flags:
Tracking Status
data-science-status --- On-hold

People

(Reporter: shong, Assigned: shong)

Details

Brief description of the request:

Write a report that looks at usage contributions of "new profiles" to overall usage over a time period.

  • Possible framing: Look at new profiles in 2017. How did they contribute to usage in 2018?

Broad, open ended report, but generally looking at how usage breaks down for different groups.

Link to any assets:

N/A

Is there a specific data scientist you would like or someone who has helped to triage this request:

Requested by myself for myself. I'm just doing it because it'll be useful context and possibly fun (because of pie charts).

Status: NEW → ASSIGNED
data-science-status: --- → On-hold

More things to look at:

  • What channels are multi-channelers using (mostly beta? mostly nightly? mostly ESR? )
  • How many profiles do people have?
  • scalar_parent_startup_profile_selection_reason: what's the distribution?
    • how much are argument / CL runs?
    • how many are "restarts"?
    • how many use profile managers?
    • do people mostly use one way? or do they have multple ways? (i.e. if they're CL runs, are they always CL runs? )
      • if so, lets isolate those. does it make sense to filter them out as "bots" here? specifically, if they run like this, do they come BACK?
  • for a given DAU/MAU/WAU, what's the breakdown in age of profile (using PCD or first appear)? In categories (within 30 days, 6 months, etc.)

closing as inactive for now, will re-open when I get more bandwidth.

Status: ASSIGNED → RESOLVED
Closed: 6 years ago
Resolution: --- → INACTIVE
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