Ga4 common reports

GA4 Common Reports

Recipes for the reports that get asked for ~95% of the time. Each one is a complete runReport you can paste, change PROPERTY_ID, and run. Prerequisite: ga4-auth-setup done.

The setup block (same for every recipe):

import os
from google.analytics.data_v1beta import BetaAnalyticsDataClient
from google.analytics.data_v1beta.types import (
    RunReportRequest, DateRange, Metric, Dimension,
    FilterExpression, Filter, OrderBy,
)

PROPERTY = f"properties/{os.environ['GA4_PROPERTY_ID']}"
client = BetaAnalyticsDataClient()

1. Daily Active Users (DAU) — 30-day rolling

req = RunReportRequest(
    property=PROPERTY,
    date_ranges=[DateRange(start_date="30daysAgo", end_date="yesterday")],
    metrics=[Metric(name="activeUsers")],
    dimensions=[Dimension(name="date")],
    order_bys=[OrderBy(dimension=OrderBy.DimensionOrderBy(dimension_name="date"))],
)
resp = client.run_report(req)
for r in resp.rows:
    print(f"{r.dimension_values[0].value} {r.metric_values[0].value}")

Why yesterday, not today: today's number is incomplete and will keep climbing through the day. For a clean rolling DAU, end the window at yesterday.

2. MAU / WAU — rolling unique users

GA4 doesn't expose MAU as a single metric — you compute it from the same activeUsers rolled up over a wider date range. The trick: a single-row report with no date dimension returns the unique count over the entire window (de-duplicated across days).

# MAU (last 30 days)
mau = client.run_report(RunReportRequest(
    property=PROPERTY,
    date_ranges=[DateRange(start_date="29daysAgo", end_date="yesterday")],
    metrics=[Metric(name="activeUsers")],
))
mau_count = int(mau.rows[0].metric_values[0].value) if mau.rows else 0

# WAU (last 7 days)
wau = client.run_report(RunReportRequest(
    property=PROPERTY,
    date_ranges=[DateRange(start_date="6daysAgo", end_date="yesterday")],
    metrics=[Metric(name="activeUsers")],
))
wau_count = int(wau.rows[0].metric_values[0].value) if wau.rows else 0

print(f"MAU: {mau_count:,}   WAU: {wau_count:,}   Ratio (engagement): {wau_count/mau_count:.2%}")

Stickiness rule-of-thumb: WAU/MAU > 0.5 is good, > 0.7 is excellent, < 0.2 means most users visit once and bounce.

3. Top pages — last 7 days, ordered by pageviews

req = RunReportRequest(
    property=PROPERTY,
    date_ranges=[DateRange(start_date="7daysAgo", end_date="yesterday")],
    metrics=[Metric(name="screenPageViews"), Metric(name="activeUsers"), Metric(name="averageSessionDuration")],
    dimensions=[Dimension(name="pagePath")],
    order_bys=[OrderBy(metric=OrderBy.MetricOrderBy(metric_name="screenPageViews"), desc=True)],
    limit=25,
)
resp = client.run_report(req)
print(f"{'Path':<60} {'Views':>8} {'Users':>8} {'AvgSec':>8}")
for r in resp.rows:
    print(f"{r.dimension_values[0].value[:58]:<60} "
          f"{r.metric_values[0].value:>8} {r.metric_values[1].value:>8} "
          f"{float(r.metric_values[2].value):>8.1f}")

4. Channel attribution — where did users come from?

req = RunReportRequest(
    property=PROPERTY,
    date_ranges=[DateRange(start_date="30daysAgo", end_date="yesterday")],
    metrics=[Metric(name="activeUsers"), Metric(name="sessions"), Metric(name="engagedSessions")],
    dimensions=[Dimension(name="sessionDefaultChannelGrouping")],
    order_bys=[OrderBy(metric=OrderBy.MetricOrderBy(metric_name="activeUsers"), desc=True)],
)
resp = client.run_report(req)
print(f"{'Channel':<28} {'Users':>10} {'Sessions':>10} {'Engaged%':>10}")
for r in resp.rows:
    users = int(r.metric_values[0].value)
    sess = int(r.metric_values[1].value)
    eng = int(r.metric_values[2].value)
    eng_rate = eng / sess if sess else 0
    print(f"{r.dimension_values[0].value:<28} {users:>10,} {sess:>10,} {eng_rate:>9.1%}")

GA4's default channel grouping has ~12 buckets: Direct, Organic Search, Paid Search, Organic Social, Paid Social, Email, Referral, Display, Video, Affiliates, Audio, etc. Use sessionSource + sessionMedium for finer-grained attribution (e.g. google / organic vs bing / organic).

5. Retention cohort — week 1 / 2 / 3 / 4 return rate

GA4 has a built-in cohort exploration in the UI but the Data API doesn't expose it cleanly. The workaround: query DAU per week and compute rolling overlap. The cheap approximation:

# Weekly active users for the last 8 weeks
req = RunReportRequest(
    property=PROPERTY,
    date_ranges=[DateRange(start_date="56daysAgo", end_date="yesterday")],
    metrics=[Metric(name="activeUsers")],
    dimensions=[Dimension(name="isoYearIsoWeek")],
    order_bys=[OrderBy(dimension=OrderBy.DimensionOrderBy(dimension_name="isoYearIsoWeek"))],
)
resp = client.run_report(req)
for r in resp.rows:
    print(f"{r.dimension_values[0].value}  {r.metric_values[0].value}")

For true cohort retention (e.g. "of users acquired in week N, what % came back in week N+1, N+2, N+3"), you need event-level data — use ga4-bigquery-export and write the cohort SQL directly. The Data API can't express the join.

6. Conversion funnel — landing → engagement → conversion

GA4 funnels via API: query each step as a separate runReport filtered by the event that defines the step, then divide.

def step_users(event_name, days_ago=7):
    return int(client.run_report(RunReportRequest(
        property=PROPERTY,
        date_ranges=[DateRange(start_date=f"{days_ago}daysAgo", end_date="yesterday")],
        metrics=[Metric(name="activeUsers")],
        dimension_filter=FilterExpression(filter=Filter(
            field_name="eventName",
            string_filter=Filter.StringFilter(
                match_type=Filter.StringFilter.MatchType.EXACT,
                value=event_name,
            ),
        )),
    )).rows[0].metric_values[0].value)

# Example funnel: landed → engaged → signed up → purchased
steps = [
    ("session_start",  step_users("session_start")),
    ("user_engagement", step_users("user_engagement")),
    ("sign_up",         step_users("sign_up")),
    ("purchase",        step_users("purchase")),
]
top = steps[0][1] or 1
print(f"{'Step':<20} {'Users':>10} {'% of top':>10}")
for name, count in steps:
    print(f"{name:<20} {count:>10,} {count/top:>9.1%}")

Limitation: this counts users who fired the event at any point in the window, NOT users who progressed through the funnel in order. For ordered funnels (true sequencing), use BigQuery export or the GA4 UI's Exploration → Funnel report.

7. Geo + device split

req = RunReportRequest(
    property=PROPERTY,
    date_ranges=[DateRange(start_date="30daysAgo", end_date="yesterday")],
    metrics=[Metric(name="activeUsers"), Metric(name="bounceRate")],
    dimensions=[Dimension(name="country"), Dimension(name="deviceCategory")],
    order_bys=[OrderBy(metric=OrderBy.MetricOrderBy(metric_name="activeUsers"), desc=True)],
    limit=30,
)
resp = client.run_report(req)
for r in resp.rows:
    country, device = r.dimension_values[0].value, r.dimension_values[1].value
    users, bounce = r.metric_values[0].value, float(r.metric_values[1].value)
    print(f"{country:<20} {device:<10} {users:>10} {bounce:>6.1%}")

A common signal: if one country dominates with low engagement + high bounce, it's often bot traffic from that country's cloud-host hubs (Singapore, Vietnam, China data centers are the usual suspects).

When the Data API isn't enough

Three reasons to graduate to BigQuery export:

1. Sampling — your queries hit resp.metadata.data_loss_from_other_row=True. Sampled = approximate. BQ export = exact.
2. Custom event analytics — joining event-level data across sessions, computing retention cohorts, building attribution models. SQL is the only sensible tool.
3. Cost — Data API has daily quotas; BQ is pay-per-query (free for small properties, cheap up to ~100M events/day).

See ga4-bigquery-export for the setup.

Related skills

• ga4-auth-setup — prerequisite
• ga4-data-api-query — the underlying API the recipes here use
• ga4-realtime-api — for "right now" data instead of any of the above
• ga4-bigquery-export — when these recipes hit their limits