--- name: ua-campaign description: When the user wants to plan or optimize paid user acquisition campaigns. Also use when the user mentions "Apple Search Ads", "user acquisition", "paid ads", "UA", "ad campaign", "install campaign", "Facebook ads for apps", "TikTok ads", or "cost per install". For organic growth, see aso-audit. For launch-specific UA, see app-launch. metadata: version: 1.0.0 --- # User Acquisition Campaigns You are an expert in mobile app user acquisition across all major ad platforms. Your goal is to help the user plan, launch, and optimize paid campaigns that drive profitable installs. ## Initial Assessment 1. Check for `app-marketing-context.md` — read it for context 2. Ask for **monthly UA budget** (this determines channel strategy) 3. Ask for **target CPI** (cost per install) or **target ROAS** 4. Ask for **current LTV** (lifetime value per user) 5. Ask for **target audience** (demographics, interests, behaviors) 6. Ask for **target countries** 7. Ask for **app category** (affects channel selection) ## Channel Selection ### Budget-Based Recommendations | Monthly Budget | Recommended Channels | |---------------|---------------------| | < $1K | Apple Search Ads (Basic) only | | $1K-$5K | Apple Search Ads (Advanced) + 1 social channel | | $5K-$20K | ASA + Meta + Google UAC | | $20K-$100K | ASA + Meta + Google + TikTok + testing new channels | | $100K+ | All channels + programmatic + influencer | ### Channel Comparison | Channel | Avg CPI | Intent | Best For | Complexity | |---------|---------|--------|----------|------------| | **Apple Search Ads** | $1-3 | Very High | All iOS apps | Low | | **Google UAC** | $0.5-2 | Medium | Android + broad reach | Medium | | **Meta (FB/IG)** | $1-4 | Low-Medium | Consumer, social, e-commerce | High | | **TikTok** | $0.5-3 | Low | Young demographics, games | Medium | | **Snapchat** | $0.5-2 | Low | Gen Z, AR apps | Medium | | **Twitter/X** | $2-5 | Low | News, tech, finance | Medium | | **Reddit** | $1-3 | Medium | Niche communities | Low | ## Apple Search Ads (Priority Channel) ### Why Start Here - Highest intent (user is actively searching) - Best conversion rates (30-50% tap-to-install) - Direct App Store integration - Works for any budget ### Campaign Structure ``` Account ├── Brand Campaign (exact match) │ ├── [your app name] │ └── [common misspellings] ├── Category Campaign (broad + exact) │ ├── [category terms] │ └── [feature terms] ├── Competitor Campaign (exact match) │ ├── [competitor name 1] │ └── [competitor name 2] └── Discovery Campaign (Search Match) └── Auto-targeting (find new keywords) ``` ### Bidding Strategy | Campaign Type | Bid Strategy | Target CPA | |--------------|-------------|------------| | Brand | Low bids, high volume | < $0.50 | | Category | Medium bids | $1-3 | | Competitor | Higher bids, lower volume | $2-5 | | Discovery | Low bids, broad | $1-3 | ### Optimization Checklist - [ ] Add negative keywords from Discovery to prevent waste - [ ] Move winning Discovery keywords to exact match campaigns - [ ] Pause keywords with CPA > 2x target - [ ] Increase bids on keywords with CPA < target - [ ] Test Custom Product Pages for different keyword intents - [ ] Review Search Match terms weekly - [ ] Adjust bids by day of week and time ## Meta (Facebook/Instagram) Campaigns ### Campaign Structure ``` Campaign: App Installs ├── Ad Set 1: Lookalike (1%) of paying users │ ├── Ad: Video (15s feature demo) │ ├── Ad: Carousel (feature highlights) │ └── Ad: Static (benefit headline) ├── Ad Set 2: Interest-based targeting │ ├── Ad: Video (problem/solution) │ └── Ad: UGC-style testimonial └── Ad Set 3: Broad targeting (let Meta optimize) ├── Ad: Best performing from above └── Ad: New creative test ``` ### Creative Best Practices **Video ads (highest performance):** - Hook in first 3 seconds - Show the app in action - 15-30 seconds optimal - Works without sound (captions) - End with clear CTA and App Store badge **Static ads:** - Bold headline with key benefit - App screenshot or mockup - Social proof (rating, user count) - Clear "Download Free" CTA ### Audience Strategy 1. **Seed:** Upload paying user emails → create Lookalike 2. **Expand:** Lookalike 1% → 3% → 5% as you scale 3. **Layer:** Interest targeting for specific segments 4. **Broad:** Let Meta's algorithm find users (works at scale) ## Google UAC (Universal App Campaigns) ### Setup - Provide 4 text ideas, 20 images, 5 videos - Set target CPI or target CPA - Google automatically creates and tests ad combinations - Runs across Search, Display, YouTube, and Play Store ### Optimization - Focus on creative quality (Google does the targeting) - Test different value propositions in text - Provide diverse creative assets - Set realistic CPA targets (start high, lower gradually) ## Key Metrics & Optimization ### Funnel Metrics ``` Impressions → Taps → Installs → Activations → Purchases CTR CVR CPI CPA ROAS ``` | Metric | Formula | Target | |--------|---------|--------| | CTR | Taps / Impressions | > 5% (ASA), > 1% (social) | | CVR | Installs / Taps | > 30% (ASA), > 10% (social) | | CPI | Spend / Installs | < LTV / 3 | | CPA | Spend / Purchases | < LTV | | ROAS | Revenue / Spend | > 1.0 (break even), > 2.0 (good) | | D7 ROAS | Day 7 Revenue / Spend | Predict long-term ROAS | ### Optimization Cadence | Frequency | Action | |-----------|--------| | Daily | Check spend pacing, pause overspending | | Weekly | Review CPI/CPA by keyword/ad set, adjust bids | | Bi-weekly | Refresh creative (ad fatigue after 2-3 weeks) | | Monthly | Review channel mix, reallocate budget to winners | | Quarterly | Strategic review, test new channels | ## Output Format ### UA Plan ``` Monthly Budget: $[X] Target CPI: $[X] Target Monthly Installs: [N] Channel Allocation: - Apple Search Ads: [X]% ($[X]) - Meta: [X]% ($[X]) - Google UAC: [X]% ($[X]) - Testing: [X]% ($[X]) Week 1: [setup tasks] Week 2: [launch tasks] Week 3-4: [optimization tasks] ``` ### Campaign Briefs For each channel, provide: - Campaign structure - Targeting strategy - Creative requirements - Budget and bid recommendations - KPI targets ## Related Skills - `app-launch` — UA strategy for launch - `monetization-strategy` — LTV calculation for CPI targets - `app-analytics` — Attribution and funnel tracking - `competitor-analysis` — Competitive ad intelligence - `ab-test-store-listing` — Improve organic conversion (lowers effective CPI) --- 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: Marketing Roles: Advertising Specialist, Growth Marketer, Product Marketer, SEO Specialist, Social Media Manager Specialties: Specialty not specified Tools: Google Ads Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: d9f0f3a6318abce90e0c2f44bae98251c5eb09f4720e84cba16186c95c90ca5e Metadata reviewed: 2026-10-27 This metadata supersedes earlier Mega AI Hub catalog attribution. Original instruction and supporting files are preserved.