--- name: onboarding-optimization description: When the user wants to improve their app's onboarding experience, increase activation rate, reduce Day 1 drop-off, or optimize the first-run flow. Use when the user mentions "onboarding", "first-run", "activation", "tutorial", "day 1 retention", "new user flow", "permission prompts", "sign-up conversion", "onboarding funnel", or "users dropping off early". For overall retention strategy, see retention-optimization. For paywall placement, see monetization-strategy. metadata: version: 1.0.0 --- # Onboarding Optimization You optimize the first-run experience to maximize activation — the moment a new user completes the core action that predicts long-term retention. ## The Activation Principle **Activation ≠ sign-up.** Activation is the first time the user gets real value from your app. Identify it before anything else. | App Type | Activation Event | |----------|-----------------| | Fitness | First workout completed | | Productivity | First task or project created | | Social | First connection made or content posted | | Finance | First account linked or budget set | | Games | First level or match completed | | Meditation | First session completed | | Photo/Video | First photo edited or exported | **Rule:** Everything in onboarding should funnel toward that one activation event as fast as possible. ## Initial Assessment 1. Check for `app-marketing-context.md` 2. Ask: **What is your activation event?** 3. Ask: **What % of new users reach it within 24 hours?** (baseline) 4. Ask: **Where do users drop off?** (which step, if known) 5. Ask: **How long does your current onboarding take?** (steps, screens) 6. Ask: **Do you have Firebase/Mixpanel funnels set up?** ## Onboarding Audit Framework ### Step 1 — Map the Current Flow List every screen from app open to activation: ``` App open → [Screen 1] → [Screen 2] → ... → Activation event ``` Flag each screen: **Required** | **Value-adding** | **Friction only** Remove or defer everything that is friction-only. ### Step 2 — Score Each Screen | Factor | Question | Score | |--------|---------|-------| | **Necessity** | Can the user reach activation without this? | 0 = skip it | | **Timing** | Is this the right moment for this ask? | | | **Value exchange** | Does the user understand why this benefits them? | | | **Cognitive load** | How many decisions does this require? | | ### Step 3 — Permission Prompt Timing Permissions are the #1 drop-off point. Rules: | Permission | When to ask | Never ask | |-----------|------------|-----------| | Push notifications | After activation, not before | On cold open | | Location | When the feature needs it | During sign-up | | Camera/microphone | Contextually, when used | Before any value | | Contacts | When the social feature is used | In onboarding | | Tracking (ATT) | After user is invested | On first open | **The pre-permission screen:** Always show a native-looking explanation screen before the system prompt. Users who understand the "why" grant at 2–3× the rate. ### Step 4 — Sign-Up Friction | Pattern | Impact | Recommendation | |---------|--------|---------------| | Required sign-up before value | High drop-off | Defer to post-activation | | Only email+password | Medium drop-off | Add Sign in with Apple + Google | | Long profile setup | High drop-off | Ask 1 question max, defer rest | | Email verification required | Kills momentum | Defer or make optional | **Guest mode / try before sign-up:** Allow users to experience the core value before requiring an account. Conversion from guest → registered is typically 40–60% vs. a hard gate at 15–30%. ## Onboarding Patterns by App Type ### Value-First (recommended for most apps) ``` Open → Core feature demo / interactive preview → Activation moment → "Save your progress" → Sign-up → Permission asks → Personalization ``` ### Personalization-First (works for health, fitness, AI apps) ``` Open → 3–5 personalization questions (show progress bar) → "Your plan is ready" reveal moment → Sign-up gate (invested now) → Activation ``` ### Social-First (social apps) ``` Open → Sign in with Apple/Google (single tap) → Find friends / follow suggestions → First feed with content → Activation (post, comment, react) ``` ## Funnel Benchmarks | Step | Benchmark | Poor | |------|-----------|------| | App open → first interaction | > 85% | < 70% | | Sign-up conversion | > 60% | < 40% | | Push permission grant | > 50% | < 30% | | Activation (D0) | > 40% | < 20% | | Day 1 retention | > 30% | < 15% | ## Personalization Questions If you include personalization, follow these rules: - Maximum **3–5 questions** in onboarding - Each question must visibly affect the experience - Show a progress indicator (step 1 of 3) - Use visual selections, not text inputs - Never ask for data you won't use immediately ## Paywall Placement in Onboarding **Rule:** Show value before the paywall. | Placement | Works When | |-----------|-----------| | Before activation | Almost never — user has no reference for value | | At activation | Strong — user just felt the value | | Post-activation, D1 | Strongest for subscription apps | | Contextual (feature gate) | Good for feature-based paywall | See `monetization-strategy` for paywall design details. ## Output Format ### Onboarding Audit ``` Current flow: [Screen 1] — Required / friction [Screen 2] — Value-adding [Screen 3] — Required / friction ... [Activation event] — Step N Drop-off analysis: Biggest drop: [screen] ([X]% exit rate if known) Estimated cause: [hypothesis] Recommended changes: 1. [Remove / defer X] — Expected impact: [lift in activation] 2. [Reorder Y before Z] — Expected impact: [rationale] 3. [Add pre-permission screen for Z] — Expected impact: [grant rate improvement] Revised flow: Open → [Screen] → [Screen] → Activation → Sign-up → Permissions Estimated steps removed: [N] Estimated time to activation: [Xs → Xs] ``` ### Permission Screen Copy Template ``` [Icon representing the permission] [Benefit headline — what the user gets] e.g., "Get notified when your goal is complete" [One-line explanation] e.g., "We'll only send you reminders you set — no spam." [Allow button] [Not now] ``` ## Related Skills - `retention-optimization` — Day 7/30 retention strategy - `monetization-strategy` — Paywall placement and trial design - `ab-test-store-listing` — Test onboarding variants - `app-analytics` — Set up activation funnel tracking - `rating-prompt-strategy` — When to ask for a rating post-activation --- Source attribution: Eronred Original project: https://github.com/Eronred/aso-skills License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 SkillsHub Contributors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: Eronred Original project: https://github.com/Eronred/aso-skills Attribution basis: Original repository namespace preserved by package mirror Fields of work: Design, Marketing, Product Management, Customer Support Roles: Customer Experience, Growth Marketer, Product Manager, Product Marketer, UX Designer Specialties: Cross Platform Tools: Tool not specified Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 60f4bea47edf212391ef03afed5b6d5f86ef2f743e4a26af9074ec4cf7d29752 Metadata reviewed: 2026-10-27 This metadata supersedes earlier Mega AI Hub catalog attribution. Original instruction and supporting files are preserved.