lead-quality-pattern-analysis

Lead Quality Pattern Analysis Skill

Purpose: Provides standardized red flag detection frameworks for identifying lead quality issues through GA4 behavioral data analysis.

Type: Domain knowledge skill (auto-invoked)

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What This Skill Deliberately Does NOT Do

• Collect the data. It runs on GA4 behavioral data already gathered (Step 1 hands it ga4-cross-analysis output); it pulls nothing itself.
• Verify flags against campaign settings. It detects patterns in GA4 behavior; confirming whether a flag is a real config gap or a data artifact is ga4-campaign-cross-reference's hypothesis → verification job.
• Build the prioritized plan. It counts and severity-classifies red flags; turning them into a sequenced action plan is lead-quality-recommendation-prioritization.
• Apply the fixes. Adding URL or placement exclusions runs through mutation-safety's two-step approval, not here.

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Quick Reference: Red Flag Decision Tree

Severity Classification:

• 🚨 Severe Quality Issues: >3 red flags detected
• ⚠️ Moderate Quality Concerns: 1-2 red flags detected
• ℹ️ Configuration Issues: 0 red flags but poor performance

Common Red Flags:

1. 50% conversions from non-conversion-intent pages (blog/informational)

2. Geographic mismatch (conversions outside target market)
3. Bot indicators (Android Webview dominance, unusual browsers)
4. Suspicious time patterns (1-4 AM peaks, exact intervals)
5. Abnormal device consistency (100% mobile or 100% desktop)
6. 100% new users (no returning visitors)
7. Extremely short session durations (<10 seconds before conversion)
8. Multiple conversions from single user/IP

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Framework 1: Landing Page Distribution Analysis

Purpose

Identify if conversions are coming from high-intent pages (property/product pages) vs low-intent pages (blog/informational content).

Analysis Method

Step 1: Categorize Landing Pages

High-Intent Pages (Conversion-Focused):

• Service detail pages (e.g., /services/, /properties/)
• Product pages (e.g., /contact/, /request-quote/)
• Pricing pages (e.g., /pricing/, /specials/)
• Community/amenity pages (e.g., /amenities/, /gallery/)

Low-Intent Pages (Informational/Blog):

• Blog posts (e.g., /blog/, /news/)
• Resource centers (e.g., /resources/, /guides/)
• General information (e.g., /about/, /faq/)
• Category pages (e.g., /neighborhoods/, /lifestyle/)

Step 2: Calculate Distribution

High-Intent % = (High-Intent Conversions ÷ Total Conversions) × 100
Low-Intent % = (Low-Intent Conversions ÷ Total Conversions) × 100

Step 3: Apply Red Flag Thresholds

Low-Intent %	Severity	Action
>75%	🚨 Severe	IMMEDIATE: Add URL exclusions for blog/informational pages
50-75%	⚠️ Moderate	MEDIUM: Review and selectively exclude low-performing URLs
25-50%	ℹ️ Monitor	OPTIONAL: Consider excluding if CPA significantly higher
<25%	✅ Normal	No action needed (expected mix)

Step 4: Document Findings

Example Table Format:

| Category | Conversions | % of Total | Top Examples |
|----------|-------------|------------|--------------|
| Blog/Community | 64 | 76.2% | /blog/10-tips, /blog/moving-guide |
| Service Pages | 15 | 17.9% | /pricing/2br, /services/studio |
| Other | 5 | 5.9% | /amenities, /contact |

Red Flag Statement (if >50%):

🚨 CRITICAL: 76.2% of conversions from blog/informational content. These users are researching moving tips, not actively seeking services. Leads likely to have low intent and high no-show rates.

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Framework 2: Geographic Distribution Analysis

Purpose

Identify conversions from outside target geography (VPNs, proxies, incorrect IP geolocation, or misconfigured targeting).

Analysis Method

Step 1: Map Conversions to Geography

Extract from GA4:

• City (most specific)
• State/Region
• Country

Step 2: Compare to Target Market

Define Target Geography:

• Primary market (e.g., the city or metro area the business serves)
• Acceptable radius (e.g., 40-mile radius)
• Explicitly excluded areas

Calculate Match Rate:

Target Geography Match % = (Conversions in Target ÷ Total Conversions) × 100

Step 3: Identify Patterns

Normal Pattern:

• Most conversions from target geography
• Some variance expected (mobile IP geolocation inaccuracy)
• Scattered outliers (travelers, relocating users)

Suspicious Patterns:

• Multiple conversions from single user in distant cities
• Geographic clusters far from target market
• International traffic when targeting domestic only

Step 4: Cross-Reference with Campaign Settings

CRITICAL: Before flagging geographic issues:

1. Verify campaign location targeting (check radius, included/excluded locations)
2. Check if conversions from wrong geo BUT targeting is correct → IP geolocation issue (NOT targeting problem)
3. Check if conversions from wrong geo AND targeting is wrong → Targeting misconfiguration

Step 5: Apply Red Flag Thresholds

Outside Target %	Pattern	Severity	Action
>50%	Distant cities, international	🚨 Severe	Verify targeting, consider IP exclusions
25-50%	Adjacent markets, mobile variance	⚠️ Moderate	Review targeting radius, check for VPNs
<25%	Scattered outliers	✅ Normal	Mobile IP variance expected

Special Case: VPN/Proxy Detection

Indicators:

• Multiple conversions from same user across different cities
• IP geolocation shows distant city but form data shows local address
• Clustering in VPN-heavy cities (certain international locations)

Note: GA4 city data is often inaccurate for mobile users. Always verify campaign targeting before concluding targeting issue.

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Framework 3: Device & Browser Pattern Analysis

Purpose

Identify bot traffic, automation tools, and abnormal device/browser patterns indicating low-quality or fraudulent conversions.

Analysis Method

Step 1: Extract Device & Browser Data

From GA4:

• Device category (mobile, desktop, tablet)
• Operating system (Android, iOS, Windows, Mac)
• Browser (Chrome, Safari, Firefox, WebView)
• Specific browser version

Step 2: Identify Bot Indicators

🚨 SEVERE Bot Indicators:

• Android WebView >50% - In-app browser (often low-quality ad placements)
• Headless browsers - Automation tools (Puppeteer, Selenium)
• Unusual user agents - Bot signatures in UA string
• Single browser version >90% - Bot pattern (not natural distribution)

⚠️ MODERATE Bot Indicators:

• 100% mobile or 100% desktop - Unnatural consistency
• Old browser versions - Outdated bots or scrapers
• Obscure browsers - Non-mainstream browsers at scale

ℹ️ MONITORING Indicators:

• Very high mobile % (>85%) - May indicate placement issues
• Single OS dominance (>95%) - Check if campaign-specific or bot

Step 3: Calculate Distribution

| Device/Browser | Conversions | % of Total | Red Flag? |
|----------------|-------------|------------|-----------|
| Android Mobile (WebView) | 70 | 83.3% | 🚨 YES (in-app ads, low quality) |
| iOS Mobile (Safari) | 10 | 11.9% | ✅ Normal |
| Desktop (Chrome) | 4 | 4.8% | ✅ Normal |

Step 4: Apply Red Flag Thresholds

Pattern	Severity	Action
Android WebView >50%	🚨 Severe	Exclude in-app placements, review mobile network quality
Single device type 100%	⚠️ Moderate	Review placement targeting, check for bot traffic
Unusual browser >25%	⚠️ Moderate	Investigate user agents, consider browser exclusions
Diverse distribution	✅ Normal	No action needed

Step 5: Cross-Reference with User Behavior

If high Android WebView + high no-shows:

• Root cause: In-app ad placements (users clicking ads while scrolling, not intentionally seeking services)
• Solution: Exclude mobile app inventory or add audience signals for in-market users

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Framework 4: Time Pattern Analysis

Purpose

Detect suspicious conversion timing patterns indicating bot traffic, automation, or low-quality placements.

Analysis Method

Step 1: Extract Hourly Conversion Data

From GA4:

• Hour of day (0-23 in property timezone)
• Day of week
• Conversion timestamp clustering

Step 2: Chart Distribution

| Hour | Conversions | % | Notes |
|------|-------------|---|-------|
| 1 AM | 18 | 21.4% | 🚨 Suspicious (off-hours peak) |
| 2 AM | 15 | 17.9% | 🚨 Suspicious |
| 3 AM | 12 | 14.3% | 🚨 Suspicious |
| 4 AM | 9 | 10.7% | ⚠️ Elevated |
| ... | ... | ... | ... |
| 10 AM | 4 | 4.8% | ✅ Normal business hours |

Step 3: Identify Red Flags

🚨 SEVERE Patterns:

• 1-4 AM peak (>30% of conversions) - Bot traffic or international placements
• Exact interval timing (every 5 min, every hour) - Automation script
• Single hour dominance (>50% in one hour) - Placement or bot issue

⚠️ MODERATE Patterns:

• Late night elevated (11 PM - 6 AM >40%) - Check for international traffic
• Weekend-only pattern - May indicate specific placement or audience

ℹ️ MONITORING Patterns:

• Business hours heavy (9 AM - 6 PM >60%) - Normal for B2B/services
• Evening peak (6 PM - 11 PM >50%) - Normal for consumer/residential

Step 4: Compare to Industry Benchmarks

Service Business - Expected Pattern:

• Peak: 6 PM - 11 PM (after work hours) - 40-50%
• Business hours: 9 AM - 6 PM - 30-40%
• Late night: 11 PM - 6 AM - 5-15%
• Early morning: 6 AM - 9 AM - 5-10%

Red Flag Threshold:

• If late night (1-4 AM) >20% → Investigate for bots/international traffic

Step 5: Cross-Reference with Geography

Pattern: 1-4 AM peak + international cities (Asia, Europe) = International placements Pattern: 1-4 AM peak + domestic cities = Bot traffic or automation

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Framework 5: User Behavior Analysis

Purpose

Detect abnormal user engagement patterns indicating low intent, bot traffic, or form spam.

Analysis Method

Step 1: Extract User Engagement Metrics

From GA4:

• New vs Returning Users (% split)
• Session Duration (avg seconds before conversion)
• Pages per Session (avg pages viewed)
• Engagement Rate (% of engaged sessions)
• Bounce Rate (% single-page sessions)

Step 2: Calculate Ratios

New User % = (New Users ÷ Total Users) × 100
Avg Session Duration = Total Session Duration ÷ Total Sessions
Avg Pages per Session = Total Pages Viewed ÷ Total Sessions

Step 3: Apply Red Flag Thresholds

New vs Returning User Ratio

New User %	Severity	Interpretation
100%	🚨 Severe	No returning visitors = no brand engagement, possible bots
95-99%	⚠️ Moderate	Very low returning rate = low intent or one-time form fills
85-95%	ℹ️ Monitor	Slightly elevated (check if new campaign or seasonal)
70-85%	✅ Normal	Expected for prospecting campaigns

Service Business Benchmark: 75-85% new users (normal for service searches)

Session Duration Before Conversion

Avg Duration	Severity	Interpretation
<10 seconds	🚨 Severe	Instant form submission = bot or accidental click
10-30 seconds	⚠️ Moderate	Very quick = low engagement, possible spam
30-120 seconds	✅ Normal	Quick but engaged (mobile users)
2-5 minutes	✅ Normal	Researching, reading content
>10 minutes	✅ High Intent	Deep engagement, comparison shopping

Service Business Benchmark: 2-5 minutes (viewing pricing details, amenities, pricing)

Pages per Session

Avg Pages	Severity	Interpretation
1 page	🚨 Severe	Bounce and convert = form spam or bot
1-2 pages	⚠️ Moderate	Low engagement
2-4 pages	✅ Normal	Viewing property info, amenities
4-8 pages	✅ High Intent	Comparison shopping, researching
>8 pages	ℹ️ Monitor	Very engaged OR bot crawling

Service Business Benchmark: 3-5 pages (service page → pricing details → amenities → convert)

Step 4: Identify Patterns

🚨 SEVERE Combination (Form Spam/Bots):

• 100% new users
• <10 sec avg session duration
• 1 page per session
• Conclusion: Automated form submission or accidental clicks

⚠️ MODERATE Combination (Low Intent):

• 95% new users

• 10-30 sec avg duration
• 1-2 pages per session
• Conclusion: Users filling forms from blog content without property intent

✅ NORMAL Combination (Engaged Prospects):

• 75-85% new users
• 2-5 min avg duration
• 3-5 pages per session
• Conclusion: Genuine potential customers researching options

Step 5: Cross-Reference with Other Frameworks

If poor user behavior + blog landing pages:

• Root Cause: Users reading moving tips, not seeking services
• Solution: Exclude blog URLs

If poor user behavior + Android WebView:

• Root Cause: In-app ad clicks while scrolling social feeds
• Solution: Exclude mobile app inventory

If poor user behavior + 1-4 AM peak:

• Root Cause: Bot traffic or automation
• Solution: Implement bot protection, review placement quality

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Red Flag Summary Table

Quick Severity Assessment

Use this table to quickly classify lead quality issues:

Red Flags Detected	Severity Classification	Recommended Action Timeline
0 flags, poor performance	ℹ️ Configuration Issues	Review targeting, bidding, budgets
1 red flag	⚠️ Moderate Concerns	Investigate root cause, implement fix this week
2 red flags	⚠️ Moderate Concerns	Multiple fixes needed, prioritize by impact
3 red flags	🚨 Severe Quality Issues	Immediate action required (same day)
4+ red flags	🚨 Severe Quality Issues	Consider pausing campaign until fixed

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Integration with Investigation Workflow

Step 1: Collect Data

• Run GA4 cross-analysis to get behavioral data
• Extract landing pages, geo, devices, time, user behavior metrics

Step 2: Apply Each Framework

• Framework 1: Categorize and calculate landing page distribution
• Framework 2: Map geography and compare to target market
• Framework 3: Identify device/browser bot indicators
• Framework 4: Chart hourly patterns and flag suspicious timing
• Framework 5: Calculate user engagement ratios

Step 3: Count Red Flags

• Tally total red flags across all 5 frameworks
• Apply severity classification (0 / 1-2 / 3+)

Step 4: Identify Root Causes

• Cross-reference red flags to find patterns
• Example: Blog pages + 100% new users + short duration = informational traffic, not property seekers

Step 5: Generate Recommendations

• Use severity classification to prioritize
• Reference specific framework findings in recommendations

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Real-World Example: PMax Lead Quality Analysis

Red Flags Detected: 5 (🚨 SEVERE)

1. Framework 1 (Landing Pages): 76.2% from blog/community content ✗
2. Framework 3 (Devices): 83.3% Android WebView ✗
3. Framework 4 (Time): Peak activity 1-4 AM (53.6%) ✗
4. Framework 5 (User Behavior): 100% new users ✗
5. Framework 5 (Engagement): Likely low engagement (not measured but inferred from blog traffic) ✗

Severity Classification: 🚨 SEVERE (5 red flags)

Root Cause Identified:

Performance Max campaign serving in-app ads (Android WebView) that lead users to blog content. Users clicking while scrolling social feeds at night (1-4 AM), filling forms out of curiosity, not genuine purchase intent.

Recommendations Generated:

• 🚨 IMMEDIATE: Add /blog/* to URL exclusions
• 🚨 IMMEDIATE: Review mobile app inventory quality
• ⚠️ MEDIUM: Add audience signals for in-market potential customers
• ⚠️ MEDIUM: Consider reducing budget during 1-4 AM hours

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When to Use This Skill

Auto-Invoked When:

• Investigating lead quality issues
• Analyzing GA4 conversion data
• Identifying bot traffic
• Reviewing no-show rates or missing contact info
• User mentions "low quality leads", "bot traffic", "form spam"
• Creating lead quality analysis reports

Manual Invocation:

• Baseline analysis for new campaigns
• Monthly quality audits
• Client reporting on lead quality metrics

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When to Load Other Skills

Skill	When
ga4-cross-analysis	Before applying the frameworks, when you still need to collect the GA4 behavioral data they read
ga4-campaign-cross-reference	To verify a flagged pattern against live campaign settings — flag → hypothesis → finding
lead-quality-recommendation-prioritization	To turn the counted red flags into a prioritized, sequenced action plan
investigation-methodology	For the broader hypothesis-driven framework when the root cause spans more than lead quality
mutation-safety	Two-step approval before applying any URL or placement exclusion the analysis recommends
ga4-lead-quality-investigation	It orchestrates the full investigation and auto-loads this skill as its red-flag detection step

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Built by Kurt Henninger. More free skills: github.com/fourteenwm/ppc-ai-skills