# Ga4 Lead Quality Investigation

> Investigate Google Ads lead quality issues by cross-analyzing GA4 behavioral data with Google Ads campaign settings. Auto-invoke when user says "investigate [account] lead quality", "GA4 analysis for [campaign]", "why are [account] leads low quality", or mentions no-shows, missing phone numbers, or bot traffic. Applies 5 red-flag frameworks, hypothesis-driven cross-reference verification, and 3-t…

- **Type:** Skill
- **Install:** `agentstack add skill-fourteenwm-ppc-ai-skills-ga4-lead-quality-investigation`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [fourteenwm](https://agentstack.voostack.com/s/fourteenwm)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [fourteenwm](https://github.com/fourteenwm)
- **Source:** https://github.com/fourteenwm/ppc-ai-skills/tree/main/ga4-lead-quality-investigation

## Install

```sh
agentstack add skill-fourteenwm-ppc-ai-skills-ga4-lead-quality-investigation
```

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

## About

# GA4 Lead Quality Investigation

**Purpose:** Investigate why a Google Ads campaign is generating low-quality leads (no-shows, missing contact info, bot traffic, geographic mismatches) by cross-analyzing GA4 behavioral data with Google Ads campaign settings and producing prioritized recommendations.

**Type:** Read-only investigation skill. Reads GA4 + Google Ads data and writes an analysis document; never writes to Google Ads.

---

## Inputs

The skill expects:

- **`{CUSTOMER_ID}`** — Google Ads customer ID (e.g., `1234567890`)
- **`{CAMPAIGN_NAME}`** — exact campaign name (e.g., `Example Property - Pmax`)
- **`{GA4_PROPERTY}`** — GA4 property ID (e.g., `123456789`)
- **`{CONVERSION_EVENT}`** — GA4 event to analyze (e.g., `book_tour`, `submit_lead_form`)
- **`{ISSUE_DESCRIPTION}`** — what the user is experiencing (e.g., "leads are no-shows", "phone numbers missing")
- **`{DATE_RANGE}`** (optional) — defaults to last 14 days

---

## Auto-Load Domain Knowledge Skills

**CRITICAL:** At the start of every investigation, auto-invoke these companion skills via the Skill tool to load the frameworks, formulas, and templates:

1. `ga4-cross-analysis` — Data collection from GA4 and Google Ads APIs (structured JSON output)
2. `lead-quality-pattern-analysis` — Red flag detection frameworks (5 frameworks: landing pages, geo, devices, time, user behavior)
3. `ga4-campaign-cross-reference` — Hypothesis → Verification → Finding methodology for discrepancy identification
4. `lead-quality-recommendation-prioritization` — 3-tier priority system (Immediate / Medium / Long-term) and recommendation templates
5. `client-communication-standards` — Background → Analysis → Conclusions report formatting with "What We Looked At" attribution

Do this BEFORE collecting any data. All five companion skills are shipped as standalone skills in this repo.

---

## Investigation Protocol

### Step 1: Data Collection

**Reference skill:** `ga4-cross-analysis`

Auto-invoke the `ga4-cross-analysis` companion skill to collect:

- **Google Ads side:** campaign performance + campaign settings (geo targeting, bid strategy, URL exclusions, negative keywords, audience signals, placement exclusions, ad scheduling)
- **GA4 side:** conversion summary, landing pages, user segments (cities, devices, browsers, hourly distribution), engagement metrics

The companion skill returns structured JSON. Document:

- Data collection timestamp
- Date range analyzed
- Any data quality warnings (e.g., low conversion volume, missing events)

> **Implementation note:** The underlying data collection scripts are environment-specific — they call the Google Ads API and GA4 Data API against your accounts and properties. The `ga4-cross-analysis` companion skill documents the data contract; you adapt the scripts to your own infrastructure (credentials, customer IDs, GA4 property IDs).

---

### Step 2: Pattern Analysis (5 Frameworks)

**Reference skill:** `lead-quality-pattern-analysis`

Apply all 5 analysis frameworks from the companion skill:

| Framework | Focus | Red Flag Examples |
|---|---|---|
| **1. Landing Page Distribution** | High-intent vs low-intent pages | >50% from blog/informational |
| **2. Geographic Distribution** | Conversion locations vs target market | Conversions outside target geo, VPN indicators |
| **3. Device & Browser Patterns** | Bot indicators | Android Webview >50%, 100% mobile, unusual UAs |
| **4. Time Patterns** | Hourly conversion distribution | 1-4 AM peaks >20%, exact intervals |
| **5. User Behavior** | Engagement metrics | 100% new users, session duration 3 red flags detected
- **Moderate Quality Concerns** — 1-2 red flags detected
- **Configuration Issues** — 0 red flags but poor performance

---

### Step 3: Campaign Settings Verification

Run a campaign-settings query against the Google Ads API to capture current configuration:

```bash
python query_campaign_settings.py {CUSTOMER_ID} "{CAMPAIGN_NAME}"
```

(The script is environment-specific — see "Script Contract" below.)

**Document ALL settings:**

- Location targeting and exclusions (radius, presence vs interest)
- Audience signals
- URL exclusions (content exclusions)
- Negative keywords
- Bidding strategy and targets
- Asset groups and final URLs
- Ad scheduling (day parting)
- Placement exclusions

This becomes the "what is currently configured" baseline for Step 4.

---

### Step 4: Cross-Reference Analysis (Hypothesis → Verification → Finding)

**Reference skill:** `ga4-campaign-cross-reference`

For each red flag from Step 2, apply the companion skill's verification methodology. The core principle is: **never assume GA4 data indicates a settings problem without verification.**

**Verification template (from companion skill):**

```markdown
### Hypothesis: {What GA4 data suggests}

**Verification Method:**
1. Check {specific campaign setting}
2. Review {additional data source}

**Finding:** Already implemented / Not implemented / Partially implemented

**Evidence:** {Specific setting value or data point}

**Conclusion:** {GAP TO FIX / NO ACTION NEEDED / DATA ANOMALY}
```

**Common cross-references (from companion skill):**

1. **Geographic:** GA4 shows wrong cities BUT targeting correct → IP geolocation issue (NOT a targeting fix)
2. **Landing pages:** GA4 shows blog traffic BUT no URL exclusions → configuration gap (FIX)
3. **Devices:** GA4 shows bots BUT no placement exclusions → missing safeguards (FIX)
4. **Time:** GA4 shows 1-4 AM peak BUT no ad scheduling → consider restricting hours
5. **Audience:** GA4 shows low engagement BUT no audience signals → targeting too broad

---

### Step 5: Prioritize Recommendations (3-Tier Framework)

**Reference skill:** `lead-quality-recommendation-prioritization`

Apply the companion skill's 3-tier priority framework:

**IMMEDIATE (Today):**

- Quick wins ( "Use the ga4-lead-quality-investigation skill to investigate Customer ID 1234567890, campaign 'Example Property - Pmax', GA4 property 123456789, event 'book_tour'. Issue: leads are no-shows."

**Parallel orchestration (when investigating multiple campaigns):**

An orchestrator can launch N parallel `Task(subagent_type="general-purpose", …)` calls in a single message, each invoking this skill against one campaign. Parallelism + per-investigation context isolation are preserved at the Task layer.

---

## Companion Skills (Required)

All 5 are shipped in this repo as standalone skills:

- `ga4-cross-analysis` — Data collection from GA4 + Google Ads APIs (structured JSON output)
- `lead-quality-pattern-analysis` — 5 red-flag detection frameworks with severity classification
- `ga4-campaign-cross-reference` — Hypothesis-driven verification methodology
- `lead-quality-recommendation-prioritization` — 3-tier priority system and recommendation templates
- `client-communication-standards` — Background → Analysis → Conclusions report formatting

Install all 5 alongside this skill for full functionality.

## Source & license

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

- **Author:** [fourteenwm](https://github.com/fourteenwm)
- **Source:** [fourteenwm/ppc-ai-skills](https://github.com/fourteenwm/ppc-ai-skills)
- **License:** MIT

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-fourteenwm-ppc-ai-skills-ga4-lead-quality-investigation
- Seller: https://agentstack.voostack.com/s/fourteenwm
- Browse the marketplace: https://agentstack.voostack.com/browse

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