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漢字でGOGO!

Micelle · 2026 · $1.50 · Simulation · deal grade B

Measured on 2026-09-29 21:57 UTC, 6 days ago. The whole catalog is re-measured daily; a game that just launched can move fast between passes.

  • Estimated net residual revenue: $304 to $456 per month
  • Opportunity: $494 per month at x1.30 dormancy
  • Estimated lifetime owners: 3.2k
  • 93% positive across 82 reviews · 30.2 reviews/month

Why it's flagged

  • Developer active, last post 3 months ago
  • Store page localized in only 1 language(s)
  • Last discounted 3 months ago, 1 sales in the last 12 months

Analyst notes (AI-assisted)

Note written 2026-09-11; the live figures above take precedence over any numbers in it.

A hand-drawn kanji mastery game that packages Japanese character study as a skill-based challenge arcade, with progressive difficulty modes and lenient stroke recognition.

This is a quiet educational title with uncommonly strong player sentiment (92%+ positive) and solid engagement depth relative to its user base. The developer is active post-launch, the game sustains modest but consistent residuals, and it holds genuine appeal to a specific but global cohort: Japanese learners seeking gamified accountability. The pitch is not scale but focus: this title works best as a catalog acquisition by a publisher already serving language-learning communities, or as a template for a similar mechanics game in another writing system. Most realistic play: publishing.

  • Risk: Addressable audience is inherently limited to Japanese learners; no pathway to mainstream casual or hardcore crossover without a substantial remake.
  • Risk: Input recognition (stroke validation on mouse, trackpad, stylus) remains a friction point even with current leniency; further refinement could unlock tablet and mobile ports but requires ongoing engineering investment.
  • Risk: Single-title studio with minimal community engagement (two posts in the last year); acquisition would require developer continuity or internal hire to maintain the game's reputation for fair difficulty calibration.

This case as Markdown · Estimates carry ±30-50% error per title.