600 Reference Df511151

Google Analytics Metrics Reference

Complete reference for Google Analytics 4 (GA4) metrics and dimensions.

Core Metrics

User Metrics

Session Metrics

Page/Screen Metrics

Event Metrics

E-commerce Metrics

Key Dimensions

Traffic Source Dimensions

Content Dimensions

User Dimensions

Time Dimensions

Common Metric Combinations

Traffic Analysis

metrics = [
    "sessions",
    "activeUsers",
    "newUsers",
    "engagementRate",
    "bounceRate"
]
dimensions = ["sessionSource", "sessionMedium"]

Content Performance

metrics = [
    "screenPageViews",
    "averageSessionDuration",
    "bounceRate",
    "eventCount"
]
dimensions = ["pagePath", "pageTitle"]

User Behavior

metrics = [
    "activeUsers",
    "sessionsPerUser",
    "screenPageViewsPerSession",
    "userEngagementDuration"
]
dimensions = ["deviceCategory", "country"]

Conversion Tracking

metrics = [
    "conversions",
    "sessions",
    "eventCount",
    "engagementRate"
]
dimensions = ["sessionSource", "sessionMedium", "sessionCampaignName"]

E-commerce Analysis

metrics = [
    "transactions",
    "purchaseRevenue",
    "averagePurchaseRevenue",
    "itemsViewed",
    "addToCarts"
]
dimensions = ["sessionSource", "deviceCategory"]

Date Range Formats

Relative Ranges

• yesterday - Previous day
• today - Current day
• 7daysAgo - Week ago
• 30daysAgo - Month ago
• 90daysAgo - Quarter ago

Absolute Ranges

• Format: YYYY-MM-DD
• Example: 2026-01-01 to 2026-01-31

Common Periods

# Last 7 days
start_date = "7daysAgo"
end_date = "yesterday"

# Last 30 days
start_date = "30daysAgo"
end_date = "yesterday"

# Month-to-date
start_date = "2026-01-01"
end_date = "today"

# Compare periods
current_start = "30daysAgo"
current_end = "yesterday"
previous_start = "60daysAgo"
previous_end = "31daysAgo"

Filters

Basic Filters

# Filter by page path
dimension_filter = {
    "filter": {
        "fieldName": "pagePath",
        "stringFilter": {"value": "/blog"}
    }
}

# Filter by country
dimension_filter = {
    "filter": {
        "fieldName": "country",
        "stringFilter": {"value": "United States"}
    }
}

# Filter by session source
dimension_filter = {
    "filter": {
        "fieldName": "sessionSource",
        "stringFilter": {"value": "google"}
    }
}

Numeric Filters

# Sessions greater than 100
metric_filter = {
    "filter": {
        "fieldName": "sessions",
        "numericFilter": {
            "operation": "GREATER_THAN",
            "value": {"int64Value": "100"}
        }
    }
}

Filter Operators

Order By

Sort results by metrics or dimensions:

# Order by sessions descending
order_bys = [{
    "metric": {"metricName": "sessions"},
    "desc": True
}]

# Order by page path ascending
order_bys = [{
    "dimension": {"dimensionName": "pagePath"},
    "desc": False
}]

API Response Structure

{
    "dimensionHeaders": [
        {"name": "sessionSource"},
        {"name": "sessionMedium"}
    ],
    "metricHeaders": [
        {"name": "sessions", "type": "TYPE_INTEGER"},
        {"name": "bounceRate", "type": "TYPE_FLOAT"}
    ],
    "rows": [
        {
            "dimensionValues": [
                {"value": "google"},
                {"value": "organic"}
            ],
            "metricValues": [
                {"value": "1250"},
                {"value": "0.45"}
            ]
        }
    ],
    "rowCount": 1,
    "metadata": {...}
}

Best Practices

Performance

• Request only needed metrics and dimensions
• Use date ranges that balance detail and performance
• Limit results with limit parameter (max 100,000)
• Use pagination for large datasets

Accuracy

• Allow 24-48 hours for data processing
• Use yesterday instead of today for complete data
• Be aware of sampling in large datasets
• Check metadata.samplingMetadatas in responses

Analysis

• Compare periods for context (week-over-week, month-over-month)
• Segment by meaningful dimensions (device, source, location)
• Focus on engagement metrics, not just volume
• Track trends over time, not just snapshots

Common Calculations

Conversion Rate

conversion_rate = (conversions / sessions) * 100

Average Order Value

aov = purchaseRevenue / transactions

Pages per Session

pages_per_session = screenPageViews / sessions

Engagement Rate

engagement_rate = (engagedSessions / sessions) * 100

Revenue per User

rpu = totalRevenue / activeUsers

Resources

• GA4 Dimensions & Metrics Explorer
• GA4 API Reference
• GA4 Query Explorer