Seo Ads

---
name: seo-ads
description: Paid-search competitive landscape for a domain or keyword. Pulls SE Ranking's PPC data — domain ad keyword footprint, ad copy patterns, who else bids on the same keywords, SERP shopping/ad-pack visibility — and produces a competitive ads brief plus a recommended bid-keyword shortlist. Use when the user asks "paid search analysis", "competitor ads", "PPC competitive", "ad copy intelligence", "shopping pack", "who bids on this keyword", or "paid keyword footprint".
---
> Example output: [examples/seo-ads-hostinger-com-20260514/ADS.md](../../examples/seo-ads-hostinger-com-20260514/ADS.md)

# Paid-Search Intelligence (Ads)

Map a domain's paid-search footprint and the competitive landscape around its target keywords. Output: a brief on what the brand is bidding on, who else bids on the same terms, ad-copy patterns the leading competitors use, SERP ad+shopping presence per keyword, and a recommended bid-keyword shortlist.

## Prerequisites

- SE Ranking MCP server connected.
- User provides: (a) a target domain OR a target keyword (skill detects which), (b) target country (default `us`).

## Process

1. **Validate input & preflight**
   - Determine: domain mode (analyse a brand's paid footprint) or keyword mode (analyse the bidding landscape for one keyword).
   - `DATA_getCreditBalance` — surface remaining credits.

2. **Domain mode** `DATA_getDomainAdsByDomain`
   - Pull paid keywords the target domain bids on.
   - For each: keyword, search volume, CPC, position, ad copy (title + description), URL.
   - Sort by traffic-weighted score (`volume × CTR-by-paid-position × bid-share`).

3. **Keyword mode** `DATA_getDomainAdsByKeyword`
   - Pull all domains bidding on the target keyword.
   - For each: domain, ad position, ad copy, URL.
   - Surface the top 10 advertisers + their copy patterns.

4. **Intent enrichment** `DATA_getKeywordQuestions`
   - For the keyword(s) in scope, pull related questions.
   - Identifies question-phrased intent variants worth bidding on (often cheaper, higher conversion).

5. **SERP ad/shopping presence** `DATA_getSerpResults`
   - For top 5 keywords (domain mode) or the target keyword (keyword mode):
     - Use SERP-feature filters to detect ad-pack composition: `tads` (top ads above organic), `bads` (bottom ads below organic), `sads` (shopping ads / Google Shopping pack), `mads` (mobile/map-pack ads).
     - Top SERP ad slots (positions 1-4 above organic, 1-3 below).
     - Shopping pack presence (carousel of product cards).
     - Image pack, local pack — these displace ad inventory.
   - Capture which advertisers occupy those slots.

6. **Ad copy pattern analysis**
   - Cluster ad headlines + descriptions by recurring patterns.
   - Identify: USP language used by leaders, pricing/discount mentions, audience segmentation, CTA verbs.
   - Highlight outliers (advertisers doing something different).

7. **Paid-keyword gap (domain mode)** `DATA_getDomainKeywords` with `type: 'adv'`
   - Pull the user's domain's paid keywords using the `type: 'adv'` switch.
   - For each top competitor (from step 2 or `DATA_getDomainCompetitors` with `type: 'adv'`): pull their paid keywords with `type: 'adv'`.
   - Diff: paid keywords competitors bid on that the user's domain doesn't.
   - This becomes the highest-leverage portion of the bid-keyword shortlist (step 8).
   - Skip in keyword mode (no domain to gap against).

8. **Recommended bid-keyword shortlist**
   - For domain mode: paid-keyword gap from step 7 + adjacent question-intent variants.
   - For keyword mode: question-intent and long-tail variants that are likely cheaper than the head term.
   - Each row: keyword, est. CPC, est. volume, who else bids, why-recommended.

9. **Synthesise** `ADS.md`

## Output format

Create a folder `seo-ads-{target-slug}-{YYYYMMDD}/` with:

```
seo-ads-{target-slug}-{YYYYMMDD}/
├── ADS.md                              (synthesised brief — primary deliverable; inlines paid footprint, bidding landscape, SERP ad/shopping pack, ad copy patterns, paid keyword gap)
├── recommended-keywords.csv            (bid-keyword shortlist — load-bearing CSV the PPC team pastes into bid tooling)
└── evidence/
    ├── 01-paid-footprint.md           (domain mode: brand's paid keywords — raw step output)
    ├── 02-bidding-landscape.md        (keyword mode: advertisers on the keyword — raw step output)
    ├── 03-question-variants.md        (DATA_getKeywordQuestions enrichment)
    ├── 04-serp-ad-shopping-pack.md    (SERP feature inventory per keyword)
    ├── 05-ad-copy-patterns.md         (clustered headline/description patterns)
    └── 06-paid-keyword-gap.md         (domain mode: type='adv' diff vs competitors)
```

Step files 01, 02, 04, 05, 06 are inlined as sections in `ADS.md`; the copies in `evidence/` preserve the raw step outputs for reproducibility.

`ADS.md` follows this shape:

```markdown
# Paid-Search Intelligence: {target}

> Snapshot dated {YYYY-MM-DD} · Country: {country} · Mode: {domain | keyword}

## Footprint summary
- Paid keywords: {n}
- Estimated paid traffic: {n}/mo
- Average CPC: ${n}
- SERP slots covered: {n} of top-4 above organic across {n} target keywords

## Top 10 paid keywords (domain mode)

| Keyword | Volume | CPC | Position | Ad copy excerpt |
|---|---|---|---|---|
| {kw} | {n} | ${n} | {pos} | "{headline} — {snippet}" |
| ...

## Bidding landscape (keyword mode — for "{keyword}")

| Advertiser | Position | Ad copy excerpt | URL |
|---|---|---|---|
| {domain} | {pos} | "{headline} — {snippet}" | {url} |
| ...

## Ad copy patterns (top patterns observed)

1. **Pricing-led:** "{N}% off — start at ${X}/mo" — used by {n} advertisers.
2. **Outcome-led:** "Get {specific outcome} in {time}" — used by {n}.
3. **Trust-led:** "Trusted by {n} {audience}" — used by {n}.
4. ...

## SERP feature inventory

| Keyword | Top ads | Shopping pack | PAA | Image pack |
|---|---|---|---|---|
| {kw} | {advertiser list} | {✓/✗} | {✓/✗} | {✓/✗} |
| ...

## Recommended bid-keyword shortlist

See `recommended-keywords.csv`. Top 10:

| Keyword | Volume | Est. CPC | Why |
|---|---|---|---|
| {kw} | {n} | ${n} | Question-intent variant; competitor X bids on head term but not this. |
| ...

## Constraints / caveats
- CPC and volume estimates are directional. Actual costs depend on Quality Score, time of day, audience, etc.
- {Note any ad-copy that's clearly seasonal / promotional and may not represent steady-state.}

## Recommended next step
Cross-reference these paid keywords with `seo-keyword-cluster` output to find under-served paid clusters. For organic content opportunities corresponding to these paid keywords, run `seo-keyword-niche`.
```

`recommended-keywords.csv` columns: `keyword,volume,cpc_estimate,position_target,intent,competitor_count,why_recommended`

## Tips

- Respect rate limit. Domain mode: ~3–5 calls. Keyword mode: ~3 calls. Plus a few SERP queries.
- Cost: ~10–20 credits typical for domain mode; ~5–10 for keyword mode.
- **CPC estimates lag.** SE Ranking's CPC data is not real-time auction data; treat as ±30% directional.
- Ad copy often reveals competitor positioning before product launches do — periodic review (quarterly) catches strategic shifts.
- Question-intent variants often have lower CPC and higher conversion than head terms. The shortlist in step 8 prioritises these.
- Pair with `seo-keyword-niche` for organic content opportunities derived from paid keyword research.
- Pair with `seo-competitor-pages` if the bidding landscape reveals "X vs Y" / "alternatives" intent — those keywords convert best as comparison pages, not paid ads.
- **Ads data via shared DATA_* tools** — beyond the dedicated `DATA_getDomainAdsByDomain` / `DATA_getDomainAdsByKeyword`, the `type: 'adv'` enum switch on `DATA_getDomainKeywords`, `DATA_getDomainKeywordsComparison`, `DATA_getDomainCompetitors`, `DATA_getDomainPages`, and similar tools surfaces the paid view of the same data structures. Combine with the `tads/bads/sads/mads` SERP-feature filters and the CPC filter on SERP queries to map paid landscape comprehensively.
- Don't recommend paid keywords without context. The shortlist is a starting point for the PPC team, not an autopilot.