# Roblox growth design Human Guide

## What This Is For
Use for growth diagnosis, discovery, retention, onboarding, experiments, LiveOps, and packaging. It gives the agent a clearer input/output frame for growth marketing: what context to ask for, what decisions to make, and what usable artifact to return.

Use this as a human-readable version of the Roblox growth design agent skill. It is meant for marketers, operators, founders, and other non-coders who want the workflow without reading agent-specific implementation instructions.

## When To Use This
- Use this when you need a repeatable process for roblox growth design.
- Use this when the task needs judgment, examples, constraints, or a clear output format rather than a one-off prompt.
- Use this when you want to hand an AI assistant enough context to produce a usable marketing artifact.

## When Not To Use This
- Do not use this when you only need a quick factual answer.
- Do not use this when the work depends on private data you cannot share with the assistant.
- Do not use this as a replacement for legal, compliance, financial, or medical review.

## What You Need Before Starting
- The goal or business outcome you want.
- The audience, customer segment, or market context.
- Any source material the assistant should respect, such as notes, briefs, examples, URLs, or brand guidance.
- Constraints such as tone, length, channel, deadline, region, or approval requirements.
- A clear definition of what a good final answer should look like.

## Step-By-Step Workflow
1. State the job clearly: "Use the Roblox growth design guide to help me with..."
2. Add context: audience, goal, offer, channel, source material, and constraints.
3. Ask the assistant to identify missing inputs before producing the final output.
4. Have the assistant follow the skill-specific guidance below.
5. Review the result against the final checklist and ask for revisions where needed.

## Skill-Specific Guidance
- Ask for dashboard, release, acquisition, and session evidence. Never invent metrics.
- Find the narrowest broken transition: impression→play, join→control, action→payoff, return, or purchase.
- Write a falsifiable hypothesis: **If Y changes because evidence Z, X should move without harming G.**
- Prefer one test with a primary metric, counter-metrics, MDE, and decision rule.
- Stop for safety, severe regressions, or invalid instrumentation; avoid significance peeking.
- Low Play-Through Rate (PTR, was QPTR): packaging readability, promise accuracy, or audience mismatch. Also check Experience Detail Page CTR.
- High First-Play Bounce Rate (negative stat): misleading package/clickbait, join failure, performance, confusing FTUE, or delayed payoff.
- Low D1: core loop, onboarding, stability, goals, or first payoff.
- Low D7/D30: progression, variety, social value, LiveOps, endgame, or exhaustion.
- Low conversion/ARPPU: product fit, value communication, friction, catalog depth, or concentration.
- Low 7-day spend days / play days per user: consumables, purchase pathways, or event cadence.
- Cold traffic: ads are the cheapest, least-qualified players; Home algo ranks the qualified audience. Launch stats look bad on ad players. Don't panic.

## Decision Points And Nuance
The original skill emphasizes: When to Load, Quick Reference, Diagnose before prescribing, Home diagnosis, Algorithm hotspots (2026), Monetization mental model (practitioner), Guardrails, Supporting file: references/full.md, Operating Model, Evidence hierarchy.

Use these questions to steer the work:
- What is the intended audience or buyer?
- What source material must be preserved?
- What should the assistant optimize for: clarity, persuasion, accuracy, speed, creativity, or conversion?
- What examples represent the desired quality bar?
- What should the assistant avoid?

## Common Mistakes
- Ask for dashboard, release, acquisition, and session evidence. Never invent metrics.
- Stop for safety, severe regressions, or invalid instrumentation; avoid significance peeking.
- Cold traffic: ads are the cheapest, least-qualified players; Home algo ranks the qualified audience. Launch stats look bad on ad players. Don't panic.
- Ads do not buy Home placement; meaningful stat-improving updates do. 28-day signals (D1, D2–7, D8–28); updates take days to show.
- Products must be must-haves that solve a pain (effort grind, losing progress AFK, FOMO, status). Limiteds with real scarcity outsell unlimited cosmetics.
- Use accurate metadata. Avoid dark patterns, deceptive odds, coercive scarcity, and "whale" targeting. Treat moderation, abuse, accessibility, localization, performance, and economy health as constraints.
- Do not jump from a weak metric to a feature prescription. A metric is an observation. Several causes can produce the same observation, and one change can move several metrics.
- The lower levels generate hypotheses. They do not prove causation.

## Copy-And-Paste Prompt
```text
Use the Roblox growth design human guide.

My goal:
[Describe the business outcome]

Audience:
[Describe who this is for]

Context and source material:
[Paste notes, examples, links, or existing copy]

Constraints:
[Tone, length, channel, timeline, must-include items, must-avoid items]

Before producing the final output, ask me for any missing information that would materially improve the result.
```

## Final Checklist
- [ ] The output matches the original goal.
- [ ] The audience and context are reflected in the answer.
- [ ] Important constraints and source material were preserved.
- [ ] The assistant made the relevant decisions explicit.
- [ ] The final artifact is ready to use, review, or hand to the next person.

## Source
This guide was generated from the tabooharmony/roblox-brain skill entry for `roblox-growth-design`.

## Source Skill Notes
These notes preserve the nuance from the original skill. Use them as supporting reference when the workflow above feels too generic.

# Roblox Growth Design

## When to Load

Load for growth, discovery, retention, onboarding, packaging, or LiveOps; route implementation to domain skills.

## Quick Reference

### Diagnose before prescribing

1. Ask for dashboard, release, acquisition, and session evidence. Never invent metrics.
2. Find the narrowest broken transition: impression→play, join→control, action→payoff, return, or purchase.
3. Write a falsifiable hypothesis: **If Y changes because evidence Z, X should move without harming G.**
4. Prefer one test with a primary metric, counter-metrics, MDE, and decision rule.
5. Stop for safety, severe regressions, or invalid instrumentation; avoid significance peeking.


### Home diagnosis

- Low Play-Through Rate (PTR, was QPTR): packaging readability, promise accuracy, or audience mismatch. Also check Experience Detail Page CTR.
- High First-Play Bounce Rate (negative stat): misleading package/clickbait, join failure, performance, confusing FTUE, or delayed payoff.
- Low D1: core loop, onboarding, stability, goals, or first payoff.
- Low D7/D30: progression, variety, social value, LiveOps, endgame, or exhaustion.
- Low conversion/ARPPU: product fit, value communication, friction, catalog depth, or concentration.
- Low 7-day spend days / play days per user: consumables, purchase pathways, or event cadence.

These are hypotheses, not one-to-one causes. Segment and test. Per-stat tactics in full.md §3–§6.

### Algorithm hotspots (2026)

- Cold traffic: ads are the cheapest, least-qualified players; Home algo ranks the qualified audience. Launch stats look bad on ad players. Don't panic.
- Ranking: genre-wide first, then against "experiences with similar players." That second benchmark is the real competition.
- Ads do not buy Home placement; meaningful stat-improving updates do. 28-day signals (D1, D2–7, D8–28); updates take days to show.
- Beta mode hides a game from Home; tune metrics on cheap ad traffic before opening to the algo.
- The 500 highly-engaged-player requirement (official): all-ages games start 16+ only until 500 highly engaged age-checked plays within 60 days. Definition (official, evolving): account tenure + playtime in your game + a purchase anywhere on Roblox in the last 60 days. 100 = publishing-fee refund threshold (separate). Ads serve 16+ automatically. Home impressions accelerate the count fastest.
- Experience detail page matters: put your best thumbnails and a gameplay description there; it feeds overall play-through.

### Monetization mental model (practitioner)

- Convert valuable one-time game passes into consumables (repeat-purchase dev products): they pump 7-day spend days per user and solve recurring pain.
- Products must be must-haves that solve a pain (effort grind, losing progress AFK, FOMO, status). Limiteds with real scarcity outsell unlimited cosmetics.
- Place products at points of interest with high foot traffic, and time prompts at the decision moment (e.g., 2x offline earnings at the collect point).
- Give players multiple purchase pathways: HUD shop icon, "+" next to currency, and a cash-shop popup after repeated failed buys.
- After shipping, check collateral damage: playtime, D1, D7, FTUE, and in-game economy.

### Guardrails

Use accurate metadata. Avoid dark patterns, deceptive odds, coercive scarcity, and "whale" targeting. Treat moderation, abuse, accessibility, localization, performance, and economy health as constraints.

> Full signal definitions, positioning, FTUE, packaging, social design, monetization mental model, algorithm mechanics, LiveOps, and audit workflow: [references/full.md](references/full.md)

---

## Supporting file: references/full.md

# Roblox Growth Design: Full Reference

> Examples, thresholds, and practitioner playbooks are starting hypotheses, not universal laws. Verify platform behavior against the dated sources, adapt to the game and audience, and test before broad rollout.

This reference combines Roblox's published discovery and analytics guidance with original practitioner synthesis. Sections labeled **Official** restate current Creator Hub behavior. Sections labeled **Heuristic** are diagnostic lenses, not claims about the recommendation algorithm. §8.3 practitioner heuristics are original synthesis derived from the submitted draft and operator experience; treat them as experience, not official guidance.

## 1. Operating Model

A game-design diagnosis should connect four layers:

1. **Promise:** what audience the title, icon, thumbnails, and premise attract.
2. **First session:** join reliability, comprehension, time-to-fun, and the first core-loop payoff.
3. **Long-term game:** progression, variety, social value, identity, mastery, and LiveOps.
4. **Business:** transparent products that add player value without damaging trust or the economy.

Do not jump from a weak metric to a feature prescription. A metric is an observation. Several causes can produce the same observation, and one change can move several metrics.

### Evidence hierarchy

Use the strongest available evidence:

1. Roblox Experiments with an adequate minimum detectable effect (MDE), full planned duration, confidence intervals, and stable variants.
2. Cohort or release comparisons with acquisition source, platform, locale, player age, and seasonality controlled where practical.
3. Funnels, session traces, errors, performance reports, economy sources/sinks, and behavioral telemetry.
4. Moderated playtests, player observation, support reports, surveys, and community feedback.