Project 19: Wishlist Funnel and Market Analysis

A data backed market memo with genre benchmarks, price hypotheses, and wishlist growth experiments.

Quick Reference

Attribute Value
Difficulty Level 2
Time Estimate Weekend
Main Programming Language C# (.NET 8) + MonoGame
Alternative Programming Languages F#, C++ (raylib), Godot C#
Coolness Level Level 3
Business Potential Level 3
Prerequisites Deterministic loop basics, debugging discipline, content pipeline fundamentals
Key Topics Audience segmentation, Pricing experiments, Signal to noise filtering

1. Learning Objectives

  1. Translate one concrete production question into a testable implementation plan.
  2. Implement and validate the feature in a MonoGame runtime context.
  3. Instrument success and failure paths with actionable diagnostics.
  4. Produce a repeatable demo artifact for portfolio or interview use.

2. All Theory Needed (Per-Concept Breakdown)

Audience segmentation

Fundamentals Audience segmentation is central to this project because it defines the non-negotiable behavioral contract for the feature. You should be able to describe valid inputs, legal state transitions, and expected outputs under normal and failure conditions.

Deep Dive into the concept Treat Audience segmentation as a boundary-setting mechanism. Start by defining the smallest deterministic scenario that proves the feature works. Stress that scenario under altered timing, altered content inputs, and altered user actions. If behavior changes unexpectedly, document hidden coupling and sequence assumptions. Keep transitions explicit and observable via logs or debug panels. Connect each transition to an event record so regression analysis is possible after refactors.

Pricing experiments

Fundamentals Pricing experiments ensures the project scales from local prototype behavior to repeatable system behavior.

Deep Dive into the concept Use Pricing experiments to reason about data flow ownership and mutation timing. Document where writes occur, when validation runs, and how rollback behaves if a write fails.

Signal to noise filtering

Fundamentals Signal to noise filtering connects this project to shipping reality by forcing you to think about operational constraints early.

Deep Dive into the concept Define one production-like failure mode related to Signal to noise filtering and build a mitigation checklist. The solution is complete when you can demonstrate both a golden path and a controlled failure path.

3. Project Specification

3.1 What You Will Build

A market-analysis workspace that synthesizes competitor benchmarks into a wishlist-funnel model and prioritized growth experiments.

Visible game deliverable:

  • Competitor matrix with price, review score, and feature deltas
  • Funnel model board with CTR and wishlist conversion assumptions
  • Experiment backlog panel prioritized by expected impact

3.2 Functional Requirements

  1. Ingest competitor data into normalized comparison table.
  2. Compute funnel assumptions and sensitivity scenarios.
  3. Rank experiments by expected wishlist impact and effort.
  4. Generate decision memo with explicit assumptions and risks.

3.3 Non-Functional Requirements

  • Performance: Must remain inside project-appropriate frame budget.
  • Reliability: Must recover from at least one injected failure mode.
  • Usability: Outcome must be observable by a reviewer in under two minutes.

3.4 Example Usage / Output

[MARKET] competitors=12 normalized=PASS
[FUNNEL] ctr=3.8% wishlist_cvr=14.2%
[PLAN] experiments_ranked=3 high_priority=2

3.5 Data Formats / Schemas / Protocols

  • Event record: {timestamp, module, action, result}
  • Feature state snapshot: {version, state, counters, flags}

3.6 Edge Cases

  • Incomplete competitor data for key titles.
  • Assumption sensitivity too high for decision confidence.
  • Experiment ideas without measurable success criteria.

3.7 Real World Outcome

This is a game-facing outcome you can see and play immediately.

What you will see in the game window:

  • Competitor matrix with price, review score, and feature deltas
  • Funnel model board with CTR and wishlist conversion assumptions
  • Experiment backlog panel prioritized by expected impact

Project 19 Wishlist Funnel and Market Analysis Window Mockup

How you interact:

  • Load benchmark dataset
  • Recalculate funnel model
  • Promote experiment to sprint

3.7.1 How to Run (Copy/Paste)

$ dotnet restore
$ dotnet build
$ dotnet run --project src/Game -- --scene market-analysis

3.7.2 Golden Path Demo (Deterministic)

  1. Start the scene and confirm all HUD panels load.
  2. Perform the three core interactions listed above.
  3. Verify the success signal appears without warnings.

3.7.3 If CLI: exact transcript

$ dotnet run --project src/Game -- --scene market-analysis
[MARKET] competitors=12 normalized=PASS
[FUNNEL] ctr=3.8% wishlist_cvr=14.2%
[PLAN] experiments_ranked=3 high_priority=2

3.7.7 If GUI / Desktop

+------------------------------------------------------+
| market-analysis                                   [F1 HUD] |
|------------------------------------------------------|
| PLAYFIELD: gameplay objects and interactions         |
| HUD: key metrics + status badges                    |
| STATUS: success/failure cues and prompts            |
+------------------------------------------------------+

4. Solution Architecture

4.1 High-Level Design

Benchmark Data -> Normalization -> Funnel Model -> Experiment Prioritizer -> Strategy Memo

Benchmark Data -> Normalization -> Funnel Model -> Experiment Prioritizer -> Strategy Memo

4.2 Key Components

Component Responsibility Key Decisions
CompetitorMatrix Stores comparable market benchmark fields Normalize by genre and price context
FunnelModeler Builds acquisition-to-wishlist assumptions Sensitivity analysis for weak assumptions
ExperimentPlanner Prioritizes actionable growth tests Impact vs effort scoring framework

4.4 Algorithm Overview

  1. Validate preconditions.
  2. Apply deterministic transition.
  3. Emit feedback and telemetry.
  4. Persist if required.

5. Implementation Guide

5.3 The Core Question You’re Answering

“How do you decide if your game has a realistic market wedge?”

5.4 Concepts You Must Understand First

  1. Audience segmentation
  2. Pricing experiments
  3. Signal to noise filtering

5.5 Questions to Guide Your Design

  1. Which funnel assumption has the highest uncertainty?
  2. How do you prevent vanity metrics from driving roadmap?
  3. What defines success for each pre-launch experiment?

5.6 Thinking Exercise

Trace one full success path and one failure path on paper before implementation.

5.7 The Interview Questions They’ll Ask

  1. Why did you pick this architecture boundary?
  2. Which failure mode did you prioritize first and why?
  3. How does your instrumentation accelerate debugging?
  4. How would you scale this feature to a larger game?

5.8 Hints in Layers

  • Hint 1: Stabilize one invariant before feature expansion.
  • Hint 2: Add diagnostics before optimization.
  • Hint 3: Keep platform calls at system boundaries.
  • Hint 4: Re-run deterministic scenario after each refactor.

5.9 Books That Will Help

Topic Book Chapter
Core concept “Obviously Awesome by April Dunford” Relevant concept chapter
Reliability “Release It!” Failure handling chapters
Architecture “Clean Architecture” Boundary and dependency chapters

6. Testing Strategy

  1. Golden path completes and emits success signal.
  2. Injected failure path recovers without crash.
  3. Re-run scenario after restart and confirm consistency.

7. Common Pitfalls & Debugging

  • Hidden initialization order coupling
  • Time-coupled behavior tied to render rate
  • Missing fallback behavior on platform call failure

8. Extensions & Challenges

  • Beginner: add one extra diagnostics panel metric.
  • Intermediate: add replay capture for event flow.
  • Advanced: add automated stress test harness.

9. Real-World Connections

This project mirrors shipping feature-module work in real indie and mid-size game teams.

10. Resources

  • Steamworks official docs
  • MonoGame docs
  • Gemini image generation docs (for asset-related projects)

11. Self-Assessment Checklist

  • I can explain the feature invariant and prove it in a demo.
  • I can trigger and handle one deterministic failure scenario.
  • I can describe tradeoffs and future scaling choices.

12. Submission / Completion Criteria

Minimum Viable Completion:

  • Feature works in deterministic golden path.
  • One controlled failure path is handled gracefully.
  • Core diagnostics are visible and documented.

Full Completion:

  • All minimum criteria plus edge-case coverage and regression checks.

Excellence:

  • Includes polished instrumentation and clear productionization notes.