Get Pixel Stats - Markifact

The Get Pixel Stats node retrieves detailed analytics and performance statistics for your Meta Ads pixel, offering various aggregation methods and filtering options to analyze tracking data.

When to Use It

Analyze pixel performance and data quality
Monitor event tracking and conversion patterns
Identify technical issues with pixel implementation
Assess data collection across different devices and browsers
Review custom data field usage and effectiveness
Optimize pixel configuration based on usage patterns
Generate reports for stakeholders and compliance
Debug tracking issues and data gaps

Inputs

Field	Type	Required	Description
Account	Select	Yes	Select the Meta Ads account containing the pixel
Pixel	Select	Yes	Select the specific pixel to get statistics for
Aggregation	Select	No	How to group the statistics (default: event_total_counts)
Date Range	Date Range	No	Time period for statistics (default: last 28 days)
Time Grouping	Select	No	Group by timestamp or date (default: time)
Event Source Filter	Select	No	Filter by web/server events (default: all)

Aggregation Options

Option	Description	Use Case
event_total_counts	Total event counts by type	Overall pixel performance overview
event	Detailed event data with filters	Specific event analysis
event_source	Events grouped by source (web/server)	Compare web vs CAPI performance
pixel_fire	Pixel firing statistics	Technical monitoring
host	Events grouped by website domain	Multi-site tracking analysis
url	Events grouped by specific URLs	Page-level performance
browser_type	Events by browser type	Browser compatibility analysis
device_os	Events by operating system	Device targeting insights
device_type	Events by device category	Mobile vs desktop analysis
match_keys	Data matching quality stats	Attribution accuracy assessment

Output

Returns statistics based on your selected aggregation method:

Event Total Counts Example:

[
  {
    "event_name": "PageView",
    "count": 15420,
    "timestamp": "2024-10-16T00:00:00Z"
  },
  {
    "event_name": "Purchase",
    "count": 342,
    "timestamp": "2024-10-16T00:00:00Z"
  }
]

Event Source Example:

[
  {
    "event_name": "Purchase",
    "source": "web",
    "count": 180,
    "percentage": 52.6
  },
  {
    "event_name": "Purchase", 
    "source": "server",
    "count": 162,
    "percentage": 47.4
  }
]

Device Type Example:

[
  {
    "device_type": "desktop",
    "event_count": 8234,
    "percentage": 53.4
  },
  {
    "device_type": "mobile",
    "event_count": 7186,
    "percentage": 46.6
  }
]

Credit Cost

Cost per run: 1 credit

FAQs

Which aggregation method should I use for different analysis needs?

For Overall Performance Monitoring:

event_total_counts: Best starting point - shows all event volumes
event: Detailed breakdown with filtering options

For Technical Analysis:

event_source: Compare web pixel vs Conversions API performance
pixel_fire: Monitor pixel installation and firing issues
browser_type/device_os: Identify compatibility issues

For Business Intelligence:

host: Analyze performance across different websites
url: Identify high/low performing pages
device_type: Understand user behavior patterns

For Data Quality Assessment:

match_keys: Review data matching and attribution quality
custom_data_field: Analyze custom parameter usage

Pro tip: Start with event_total_counts for overview, then drill down with specific aggregations.

How do I interpret event source data (web vs server)?

Event Source Types:Web Events (Browser Pixel):

Tracked directly from user’s browser
Real-time user interactions
Subject to ad blockers and privacy settings
May miss some conversions due to technical issues

Server Events (Conversions API):

Sent from your server to Meta
More reliable and comprehensive
Not affected by ad blockers
Better for sensitive data and offline events

Healthy Distribution:

50/50 split: Excellent - both sources working well
70% web / 30% server: Good - strong browser tracking
30% web / 70% server: Good - strong server integration
90%+ from one source: Check the other source setup

Optimization Goals:

Aim for both sources active for redundancy
Server events improve data quality and match rates
Use server events for sensitive data (purchases, leads)
Web events good for engagement tracking

What insights can I get from device and browser data?

Device Type Analysis:

Mobile dominance: Optimize for mobile experience and ads
Desktop preference: Focus on desktop-optimized content
Balanced usage: Ensure responsive design and cross-device tracking

Browser Type Insights:

Chrome/Safari/Firefox distribution: Check compatibility
Unusual patterns: May indicate bot traffic or technical issues
Privacy-focused browsers: Expect lower tracking rates

Operating System Data:

iOS vs Android: Mobile app and campaign optimization
Windows/Mac distribution: Desktop experience optimization
Version information: Compatibility and feature support

Business Applications:

Ad creative optimization: Design for dominant platforms
User experience improvements: Focus development efforts
Targeting strategy: Adjust campaigns based on user preferences
Technical troubleshooting: Identify platform-specific issues

Red Flags:

Sudden shifts in device/browser distribution
Extremely low counts from major browsers/devices
Inconsistent patterns compared to industry benchmarks

How can I use URL and host data for optimization?

URL-Level Analysis:

High-converting pages: Identify your best-performing content
Drop-off points: Find where users leave your funnel
Event distribution: See which pages drive specific actions
A/B testing insights: Compare performance across page variants

Host-Level Analysis:

Multi-domain tracking: Compare performance across properties
Subdomain optimization: Identify strong/weak areas
Campaign landing pages: Analyze dedicated campaign sites
Partner integrations: Track third-party domain performance

Optimization Strategies:

Improve low-performing pages: Focus UX improvements
Replicate success: Apply high-performing page elements elsewhere
Adjust traffic allocation: Send more traffic to converting pages
Fix technical issues: Address pages with tracking problems

Common Patterns:

Homepage dominance: May indicate navigation issues
Checkout abandonment: Focus on conversion optimization
Blog engagement: Content marketing effectiveness
Product page variations: Compare product performance

Pro tip: Use this data to inform content strategy and user experience improvements.

What do match keys statistics tell me about data quality?

Match Keys Explained: Match keys are data points used to connect pixel events with Meta user profiles:

Email addresses
Phone numbers
External IDs
Facebook user IDs

Key Metrics:

Match rate: Percentage of events with successful user matching
Coverage: How many events include each type of match key
Quality scores: Accuracy and reliability of matches

Quality Indicators:

High email match rates: Good customer data collection
Strong phone number coverage: Comprehensive contact information
External ID usage: Effective CRM integration
Low match rates: Data quality or collection issues

Improvement Strategies:

Collect better data: Improve form fields and data capture
Implement hashing: Properly format customer data
Use Conversions API: Send server-side data for better matching
Enable automatic matching: Let Meta optimize connections

Business Impact:

Better attribution accuracy
Improved ad targeting effectiveness
Higher conversion tracking reliability
Enhanced audience building capabilities

How should I set up date ranges for meaningful analysis?

Recommended Date Ranges:Daily Monitoring (1-7 days):

Use case: Real-time issue detection
Time grouping: By hour or timestamp
Focus: Technical problems, campaign launches

Weekly Analysis (7-14 days):

Use case: Campaign performance review
Time grouping: By date
Focus: Trend identification, optimization opportunities

Monthly Reviews (28-90 days):

Use case: Strategic analysis and reporting
Time grouping: By date or week
Focus: Long-term patterns, seasonal effects

Quarterly Audits (90+ days):

Use case: Comprehensive pixel health assessment
Time grouping: By week or month
Focus: Infrastructure changes, business growth impact

Comparative Analysis:

Year-over-year: Same period previous year
Month-over-month: Compare recent months
Before/after: Major website or campaign changes

Pro tips:

Use consistent date ranges for trend analysis
Account for seasonality in your business
Include sufficient data for statistical significance
Consider external factors (holidays, market events)

What should I do if I see concerning patterns in my pixel stats?

Common Issues and Solutions:Sudden Drop in Events:

Check: Website changes, pixel code modifications
Action: Verify pixel installation, test with Pixel Helper
Timeline: Address immediately

Low Match Rates:

Check: Customer data quality, CAPI implementation
Action: Improve data collection, implement server events
Timeline: Plan for gradual improvement

Unusual Device/Browser Patterns:

Check: Bot traffic, technical restrictions
Action: Implement bot filtering, check compatibility
Timeline: Monitor and adjust over time

High Web vs Server Imbalance:

Check: CAPI setup, server event configuration
Action: Implement or fix Conversions API
Timeline: Technical implementation project

URL/Host Issues:

Check: Tracking code placement, page load issues
Action: Fix technical implementation, optimize pages
Timeline: Development sprint planning

Monitoring Best Practices:

Set up automated alerts for significant changes
Regular weekly reviews of key metrics
Document known issues and planned fixes
Compare against industry benchmarks when available

Can I export this data for further analysis?

Data Export Options:Direct Use:

Copy JSON output for spreadsheet analysis
Use data in subsequent workflow nodes
Generate reports within Markifact

Integration Approaches:

Google Sheets: Connect output to spreadsheet for team access
BI Tools: Feed data into business intelligence platforms
Dashboards: Create automated reporting workflows
APIs: Use data in custom applications

Analysis Recommendations:

Trend Analysis: Track metrics over time
Comparative Studies: Compare across pixels or time periods
Correlation Analysis: Relate pixel performance to business metrics
Forecasting: Predict future performance based on trends

Reporting Best Practices:

Stakeholder-specific views: Customize reports for different audiences
Regular cadence: Set up automated reporting schedules
Context inclusion: Add business context to raw data
Action items: Include recommendations with data insights

Pro tip: Combine pixel stats with business data (sales, traffic, campaigns) for comprehensive analysis.

Documentation Index

​When to Use It

​Inputs

​Aggregation Options

​Output

​Event Total Counts Example:

​Event Source Example:

​Device Type Example:

​Credit Cost

​FAQs