Case Study

Train Sort Jam Puzzle

Full-Cycle Puzzle Development with Analytics-Tuned Difficulty

Train Sort Jam Puzzle key art

Train Sort Jam is a color-sorting puzzle where players route trains onto matching tracks, easy to read, deviously hard to master. Delivered full-cycle with a level pipeline built for post-launch content velocity.

ENGAGEMENT MODEL
Full-Cycle
ENGINE
Unity (C#)
PLATFORMS
Android
GENRE
Casual Puzzle
FOCUS AREAS
Level design pipeline, difficulty tuning, rewarded ad economy

Sorting puzzlers are a crowded genre where retention is decided by the difficulty curve: too flat and players churn from boredom, too spiky and they churn from frustration. The brief demanded a level pipeline that could be tuned from data, not designer intuition.

KEY REQUIREMENTS

  • A level format designers could author and tune without engineering time
  • Difficulty curve instrumented per level: attempts, time, quits, skips
  • 60+ launch levels with post-launch batches sustainable at low cost
  • Rewarded ads as the primary monetization without paywalling progress
  • Data-driven level format (ScriptableObject-based) with an in-editor validator that catches unsolvable configurations before they ship.
  • Per-level funnel analytics, attempts, completion time, quit points, skip usage, feeding a difficulty dashboard.
  • Post-launch difficulty re-tuning: levels with outlier quit rates were rebalanced in the first update cycle, smoothing the retention curve.
  • Rewarded ad economy centered on skips and hints, monetization that players opt into precisely at the difficulty spikes the data reveals.
  • Color-blind safe palette verified across all track/train combinations.
Train Sort Jam Puzzle screenshot 1
  • Complete Unity puzzle game with 60+ levels
  • Designer-facing level authoring + validation tooling
  • Per-level analytics instrumentation and difficulty dashboard
  • Rewarded ad economy integration
  • Post-launch level batch pipeline
UnityC#GameAnalyticsAppLovin MAXFirebasePhotoshop

Live on Google Play. First-cycle difficulty re-tuning from the analytics dashboard measurably smoothed the level-quit funnel, and the level pipeline lets new content batches ship without engineering involvement.

How do you tune puzzle difficulty with data?
Every level reports attempts, completion time, quit rate, and skip usage. Outliers against the target curve get rebalanced in the next update. The designer sets the intended curve; the data shows where reality disagrees.
Why rewarded ads instead of paywalls for puzzles?
Puzzle players tolerate opt-in help (skips, hints) far better than forced gates. Rewarded placements at difficulty spikes convert exactly when motivation is highest, and the same data that finds the spikes places the offers.
How fast can new level batches ship?
With the authoring pipeline, a 20-level batch is a design-and-QA task, not an engineering one, typically inside a two-week update cycle.
Could you build a sorting puzzler for us?
Yes, the genre pipeline (level tooling, difficulty analytics, rewarded economy) is proven and reusable. From $10k for a launch-ready title.

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