Project 29: LTV Modeling and Revenue Cohort Analytics

Build a production-ready system for LTV Modeling and Revenue Cohort Analytics inside a Roblox live-ops workflow.

Quick Reference

Attribute Value
Difficulty Level 4: Advanced
Time Estimate 1-2 weeks
Main Programming Language Luau + analytics exports
Alternative Programming Languages Python, TypeScript, R
Coolness Level Level 4
Business Potential Level 5
Prerequisites Roblox Studio basics, analytics fundamentals, spreadsheet modeling
Key Topics ARPDAU and LTV modeling, whale segmentation, revenue cohort tracking, and break-even analysis for updates and acquisition.

1. Learning Objectives

By completing this project, you will:

  1. Translate strategy into explicit operating rules.
  2. Define primary metrics and guardrails for this domain.
  3. Execute at least one scale, iterate, or kill decision cycle.
  4. Produce reusable artifacts for future launches.

2. All Theory Needed (Per-Concept Breakdown)

Concept: Strategy-to-Operations Translation

Fundamentals: This concept turns broad intent into repeatable execution. Instead of discussing ideas abstractly, you define hypotheses, data sources, thresholds, owners, and fallback actions. That structure makes decisions faster and safer.

Deep Dive: Roblox teams that skip operational framing usually fail from ambiguity, not lack of effort. You can avoid this by enforcing a fixed weekly rhythm: define one hypothesis, map to a primary KPI and two guardrails, run changes, and review with explicit thresholds. Keep an invariant that every major decision has one owner and one timestamped memo. This prevents decision drift and helps postmortems. Use a scale, iterate, kill tree so outcomes are predictable under pressure. The result is higher confidence, clearer accountability, and better retention and monetization outcomes over time.

How this fits on projects: Primary for Project 29; reused in Projects 32, 33, and 34.

Definitions and key terms:

  • Hypothesis: testable claim about impact.
  • Guardrail: safety metric that limits risky optimization.
  • Threshold: numeric condition that triggers a specific decision branch.

Mental model diagram: Hypothesis -> Instrumentation -> Weekly Review -> Decision Branch -> Action -> Memo

How it works:

  1. Define hypothesis.
  2. Attach metrics and thresholds.
  3. Run implementation cycle.
  4. Review against thresholds.
  5. Execute decision branch.

Minimal concrete example: Decision flow pseudocode: if primary KPI exceeds scale threshold and guardrails are healthy, then scale; else if KPI exceeds iterate threshold, then iterate; else kill or reframe.

Common misconceptions:

  • More dashboards automatically means better decisions.
  • Fast shipping is always better than disciplined iteration.

Check-your-understanding questions:

  1. Why are guardrails mandatory?
  2. What causes decision ambiguity?
  3. What artifact preserves team learning?

Check-your-understanding answers:

  1. Guardrails protect trust and long-term retention.
  2. Missing thresholds and ownership.
  3. Timestamped decision memos.

Real-world applications:

  • Growth strategy.
  • Monetization policy.
  • Live operations planning.

Where you will apply it:

  • Project 29 plus multi-project capstone planning.

References:

  • Roblox Creator Docs (Discovery, Monetization, Data, Open Cloud)
  • The Art of Game Design, Jesse Schell
  • Designing Data-Intensive Applications, Martin Kleppmann

Key insight: Operational clarity beats intuition under live-service pressure.

Summary: This framework turns strategic discussion into reliable execution.

Homework:

  1. Write one hypothesis and two guardrails.
  2. Define thresholds for scale, iterate, kill.
  3. Draft a one-page decision memo template.

Solutions:

  1. Use measurable and falsifiable statements.
  2. Use stable thresholds for one full cycle.
  3. Include owner, date, metrics, decision, and next action.

3. Project Specification

3.1 What You Will Build

A reusable operating package for LTV Modeling and Revenue Cohort Analytics: metric dictionary, decision thresholds, weekly review ritual, and one completed iteration cycle.

3.2 Functional Requirements

  1. Define one primary KPI and two guardrails.
  2. Create a weekly decision cadence with named owners.
  3. Run one complete cycle and document the decision.
  4. Record fallback and rollback triggers.

3.3 Non-Functional Requirements

  • Performance: reporting is available inside review cadence.
  • Reliability: definitions remain stable for the full cycle.
  • Usability: another teammate can run the process with your docs.

3.4 Example Usage or Output

Week 1: hypothesis logged, metrics tracked, review held. Decision: iterate. Week 2: controlled adjustment and follow-up review.

3.5 Data Formats or Schemas

  • KPI dictionary: name, owner, source, thresholds.
  • Weekly memo: hypothesis, metrics, decision, next action.
  • Risk log: failure mode, impact, mitigation, rollback trigger.

3.6 Edge Cases

  • Missing data in reporting window.
  • Primary KPI up but guardrails down.
  • Sudden external traffic spike.

3.7 Real World Outcome

You can run the full loop independently and defend one evidence-based decision.

4. Solution Architecture

4.1 High-Level Design

Data sources -> KPI aggregation -> Review ritual -> Decision branch -> Action queue

4.2 Key Components

Component Responsibility Key Decisions
KPI Dictionary Metric definitions Keep versions stable
Review Ritual Cadence and accountability One owner per cycle
Action Queue Scale, iterate, kill execution Require rollback trigger

4.3 Data Structures

Decision memo structure:

  • date
  • owner
  • hypothesis
  • primary KPI
  • guardrails
  • decision
  • next action

4.4 Algorithm Overview

  1. Normalize KPI and guardrails.
  2. Score decision confidence.
  3. Choose scale, iterate, or kill path.

Complexity:

  • Time O(n) for n tracked metrics.
  • Space O(n).

5. Implementation Guide

5.1 Development Environment Setup

  • Roblox Creator Dashboard access.
  • Spreadsheet or notebook environment.
  • Shared docs for runbooks and decision logs.

5.2 Project Structure

project-29

  • docs
  • dashboards
  • runbooks

5.3 The Core Question You Are Answering

How do we make LTV Modeling and Revenue Cohort Analytics measurable, repeatable, and safe in production?

5.4 Concepts You Must Understand First

  1. KPI design and guardrails.
  2. Experiment interpretation.
  3. Rollout and rollback governance.

5.5 Questions to Guide Your Design

  1. Which metric drives decisions here?
  2. Which guardrails block harmful optimization?
  3. What evidence is required before scale?

5.6 Thinking Exercise

Sketch your decision tree and simulate one failure case where growth rises but trust falls.

5.7 The Interview Questions They Will Ask

  1. How do you prevent metric gaming?
  2. What triggers rollback?
  3. How do you know when to kill a feature?
  4. Which guardrails preserve trust?
  5. How does this process scale with team size?

5.8 Hints in Layers

Hint 1: Start with one KPI and two guardrails. Hint 2: Freeze thresholds for one cycle. Hint 3: Use one-page memos for every decision. Hint 4: Run a pre-mortem before rollout.

5.9 Books That Will Help

Topic Book Chapter
Game system thinking The Art of Game Design Relevant lenses for retention and motivation
Data reliability Designing Data-Intensive Applications Chapters 1 and 11
Metrics and growth Lean Analytics Stage-fit metric chapters

5.10 Common Pitfalls and Debugging

Problem: thresholds change every meeting.

  • Why: no versioned policy.
  • Fix: lock thresholds for one cycle.
  • Quick test: confirm threshold changelog exists.

Problem: no clear decision owner.

  • Why: diffuse accountability.
  • Fix: assign one owner per cycle.
  • Quick test: each memo includes approver name.

5.11 Definition of Done

  • KPI dictionary is documented.
  • One full cycle is completed.
  • Decision policy is versioned.
  • Learning memo is archived.

6. Validation and Testing Strategy

  1. Ask another teammate to run the process from your docs.
  2. Compare their conclusion with yours.
  3. Inject one synthetic anomaly and verify guardrail triggers.

7. Next Steps

  1. Automate metric extraction.
  2. Add anomaly alerts.
  3. Integrate this workflow into seasonal planning.