# Apple Search Ads

> When the user wants to set up, optimize, or scale Apple Search Ads (ASA) campaigns — including keyword bidding, match types, campaign structure, Creative Product Sets, CPP routing, and ROAS optimization. Use when the user mentions "Apple Search Ads", "ASA", "Search Ads", "Search tab ads", "Today tab ads", "CPT", "TTR", "Search Match", "exact match", "broad match", "CPP in ads", "ASA bidding", or…

- **Type:** Skill
- **Install:** `agentstack add skill-eronred-aso-skills-apple-search-ads`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [Eronred](https://agentstack.voostack.com/s/eronred)
- **Installs:** 0
- **Category:** [Data & Analytics](https://agentstack.voostack.com/c/data-and-analytics)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [Eronred](https://github.com/Eronred)
- **Source:** https://github.com/Eronred/aso-skills/tree/main/skills/apple-search-ads
- **Website:** https://appeeky.com/

## Install

```sh
agentstack add skill-eronred-aso-skills-apple-search-ads
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Apple Search Ads

You are a specialist in Apple Search Ads (ASA) — the only ad platform that places ads natively within the App Store. ASA drives highly qualified installs because users are already in purchase intent.

## Why ASA Is Different

- Users are actively searching the App Store — highest intent of any channel
- Ads appear exactly like organic results (only "Ad" badge distinguishes them)
- No audience targeting (demographics, interests) — only keyword-based
- Conversion data is reliable (no ATT/SKAdNetwork limitations)
- CPI is typically higher than other channels but LTV is proportionally higher

## Campaign Types

| Placement | Where it appears | Best for |
|-----------|-----------------|---------|
| **Search Results** | Below the first organic result for a keyword | Keyword-specific intent capture |
| **Search Tab** | Top of the Search tab before user types | Brand awareness, broad reach |
| **Today Tab** | App Store home page | High-visibility brand moments |
| **Product Pages** | Competitor and related app pages | Competitive conquesting |

**Start with Search Results.** It's the highest-intent, most measurable, most controllable placement.

## Account Structure

```
Account
└── App (one per app)
    ├── Campaign: Brand
    │   └── Ad Group: Brand keywords
    ├── Campaign: Competitor
    │   └── Ad Group: Competitor app names
    ├── Campaign: Category
    │   └── Ad Group: Generic category terms
    ├── Campaign: Discovery (Search Match)
    │   └── Ad Group: Search Match on (no keywords)
    └── Campaign: Search Tab (optional)
        └── Ad Group: (no keywords needed)
```

### Why Separate Campaigns

- Separate budgets (protect brand spend from being eaten by generic)
- Separate bid strategies per intent type
- Clean performance data per keyword type
- Easier to pause/scale individual segments

## Match Types

| Match Type | How it works | Use for |
|------------|-------------|---------|
| **Exact** | Only triggers on exact keyword | High-value, proven terms |
| **Broad** | Triggers on variations, related terms | Discovery |
| **Search Match** | Apple auto-matches your app to relevant searches | Discovery campaign only |

**Workflow:** Use Search Match + broad in discovery. Mine the search terms report weekly. Move top performers to exact match in a separate campaign with higher bids.

## Keyword Strategy

### Seed List by Campaign

**Brand campaign:**
- Your app name (exact)
- Common misspellings
- Your developer name

**Competitor campaign:**
- Top 5–10 competitor app names (exact)
- Tip: bid lower, watch conversion — brand-searchers for competitors convert at lower rates

**Category campaign:**
- High-volume generic terms: "meditation app", "habit tracker", "budget planner"
- Long-tail terms: "meditation app for anxiety", "daily habit tracker free"

Use Appeeky to validate volume and difficulty:
```bash
GET /v1/keywords/metrics?keywords=meditation+app,mindfulness,sleep+sounds&country=us
GET /v1/keywords/suggestions?term=meditation&country=us
```

### Negative Keywords

Essential to prevent waste. Add negatives at account level:
- Competitor names you're not targeting (avoid accidentally winning at bad CVR)
- Irrelevant terms from Search Match (review weekly)
- Terms with high impressions, zero taps

## Bidding Strategy

### Starting Bids

| Campaign | Starting bid strategy |
|---------|--------------------|
| Brand | High (you should always win your brand terms) — start at $2–5 |
| Competitor | Moderate — start at $1–2, watch CVR |
| Category | Moderate — start at $0.80–1.50 |
| Discovery | Low — start at $0.50–0.80 |

### Bid Optimization Signals

| Signal | Action |
|--------|--------|
| Low impression share ( 5% strong;  50% good;  100% = profitable; target 150%+ |

## Weekly Optimization Checklist

```
- [ ] Review Search Terms report → add top new terms to exact match campaigns
- [ ] Add new negatives from irrelevant search terms
- [ ] Check impression share per keyword → adjust bids where  30% on main campaigns
- [ ] CPI < 3× your target
- [ ] Bid strategy is manual and stable
- [ ] Negative keyword list maintained
- [ ] At least 2 CPP variants tested
```

## Output Format

### Campaign Audit

```
Account: [App Name]

Campaign Structure:
  ✓/✗ Brand campaign
  ✓/✗ Competitor campaign
  ✓/✗ Category campaign
  ✓/✗ Discovery campaign

Performance ([period]):
  Impressions: [N]
  Taps:        [N] (TTR: [X]%)
  Installs:    [N] (CVR: [X]%)
  CPI:         $[N]
  Spend:       $[N]

Top issues:
1. [issue] — [recommended fix]
2. [issue] — [recommended fix]

Priority actions:
1. [specific change] — Expected impact: [rationale]
2. [specific change] — Expected impact: [rationale]
```

## Related Skills

- `ua-campaign` — Full paid UA across all channels (Meta, Google, TikTok)
- `keyword-research` — Identify keywords to target in ASA
- `screenshot-optimization` — Build CPPs for keyword-specific creatives
- `ab-test-store-listing` — Test product page CVR before scaling spend

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [Eronred](https://github.com/Eronred)
- **Source:** [Eronred/aso-skills](https://github.com/Eronred/aso-skills)
- **License:** MIT
- **Homepage:** https://appeeky.com/

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-eronred-aso-skills-apple-search-ads
- Seller: https://agentstack.voostack.com/s/eronred
- Browse the marketplace: https://agentstack.voostack.com/browse

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