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Analytics May 3, 2026 5 min read

Defining Product-Active Users Across an Arena

A small analytics methodology for counting real product activity across practice, puzzle rush, solved problems, assessments, and classroom sessions.

AlgoArena Research

Defining Product-Active Users Across an Arena

5
Signals
UIDs
Scope
monthly
Cadence

Evidence Shape

A product-active metric should follow actual learning and evaluation work, not just sign-ins or page views.

Practicecode submissions and solves
Classroomhost and player activity
Assessmentsstarted OA sessions
01Collect event families
02Exclude internal/test users
03Deduplicate UIDs
04Export chart and JSON
Signals: 5 (submissions, puzzle rush, solved problems, OA, classroom) | Scope: UIDs (distinct Firebase users after exclusions) | Cadence: monthly (UTC calendar month aggregation)

Page views are a weak proxy for whether a coding product is working.


AlgoArena has several ways a person can do real work: submit a practice solution, play Puzzle Rush, solve a problem, start an assessment, host a classroom, or answer in a classroom session. A useful product-active metric has to count across those lanes without double-counting the same person.


The definition


For each UTC calendar month, the report counts distinct non-bot Firebase UIDs who did any of:


  • submitted code
  • submitted a Puzzle Rush answer
  • solved a problem
  • started an OA session with a real UID
  • hosted or played in a classroom game created, started, or ended in that month

  • Internal and test accounts are excluded before the final count.


    Why this is better than sign-ins


    A sign-in can mean curiosity, debugging, or a forgotten tab. A product-active event means the user touched the learning or evaluation loop.


    That does not make the metric perfect. It still misses qualitative depth. But it is much closer to the product promise: people should be practicing, competing, teaching, or being assessed.


    What the script outputs


    The report produces a JSON sidecar and an HTML visualization. That makes it useful both for quick founder review and for later product notes where a chart needs to match the exact query that produced it.