Ga4 Lead Quality Investigation

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.

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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

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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.

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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).

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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

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Step 3: Campaign Settings Verification

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

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.

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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):

### 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

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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.

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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
• Source: fourteenwm/ppc-ai-skills
• License: MIT

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