Solved: Is it me or does GA4 kind of suck? What are some alternatives?

Navigating the shift to Google Analytics 4 (GA4) has been challenging for many IT professionals. This post explores common frustrations with GA4 and provides detailed solutions, from optimizing your GA4 usage to implementing powerful open-source or product-led analytics alternatives.

GA4’s Growing Pains: A Community Rant Session

The Reddit thread title “Is it me or does GA4 kind of suck? What are some alternatives?” resonates deeply with a significant portion of the IT and marketing community. The transition from Universal Analytics (UA) to GA4 has been far from smooth, bringing a new set of challenges that often leave users frustrated. Let’s break down the common symptoms fueling this discontent.

The Learning Cliff

• Counter-intuitive UI: Many long-time UA users find GA4’s interface drastically different, often struggling to locate familiar reports or build new ones. The shift from session-based to event-based data fundamentally changes how one navigates and interprets data.
• Steep Learning Curve for Event Tracking: While powerful, the event-driven data model requires a complete rethinking of tracking strategies. What used to be simple pageview tracking now requires careful consideration of events, parameters, and user properties.

Report Inadequacies and Data Discrepancies

• Lack of Familiar Reports: Critical reports from UA, like “Bounce Rate” (as traditionally defined) or detailed channel grouping, are either absent, redefined, or require complex custom configurations in GA4.
• Data Discrepancies: Users frequently report inconsistencies between GA4 data and other sources, or even between different reports within GA4 itself, leading to mistrust in the data’s accuracy.
• Debugging Challenges: Identifying and fixing tracking issues in GA4 can be more complex due to the event model and less granular real-time debugging tools compared to UA.

Privacy Concerns and Data Ownership

• While GA4 introduced enhanced privacy features like consent mode and cookieless measurement, it remains a Google product. For organizations with strict data sovereignty or privacy mandates, relying on a third-party giant like Google for core analytics can still be a concern.
• The lack of direct data ownership (without BigQuery export) compared to self-hosted solutions is a common point of contention.

Solution 1: Master the GA4 Paradigm Shift

Before abandoning GA4 entirely, consider that many frustrations stem from trying to force UA methodologies onto an entirely different architecture. Embracing GA4’s unique strengths can transform it from a pain point into a powerful analytics engine.

Embrace the Event-Driven Model

GA4 is built on events. Everything is an event, from page views to clicks to custom interactions. Understanding this is key.

• Rethink Measurement: Instead of focusing on sessions, focus on user journeys and event sequences.
• Standardize Event Naming: Adopt a consistent naming convention for events and their parameters (e.g., button_click with parameter button_name).
• Leverage Enhanced Measurement: Enable built-in automatic event tracking for common interactions like scroll, outbound clicks, site search, video engagement, and file downloads.

Leverage Explorations and Custom Reporting

The standard reports in GA4 are often not enough. The true power lies in “Explorations” and custom reporting.

• Funnel Explorations: Analyze user progression through predefined steps (e.g., product page view > add to cart > checkout > purchase).
• Path Explorations: Understand user flows and common navigation paths after or before specific events.
• Free-form Explorations: Build highly customized tables and charts with any combination of dimensions and metrics.
• Custom Reports: Create bespoke reports that appear in your standard reporting interface by saving explorations or using the custom report builder.

Unlock Power with BigQuery Export

For advanced data analysis, connecting GA4 to Google BigQuery is a game-changer. This feature, free for standard GA4 properties, exports raw, unsampled event data directly to your BigQuery project, allowing for virtually limitless analysis.

• True Data Ownership: Your raw data is in your BigQuery instance.
• Complex Queries: Perform sophisticated SQL queries that are impossible within the GA4 UI.
• Integration: Combine GA4 data with CRM data, sales data, ad spend, and other datasets for a holistic view.
• Visualization: Connect BigQuery to tools like Google Data Studio (Looker Studio), Tableau, or Power BI for custom dashboards.

Example: Basic BigQuery Query for GA4 Event Data

This query counts page_view events and their associated page_location from the last day.

SELECT
  event_name,
  (SELECT value.string_value FROM UNNEST(event_params) WHERE key = 'page_location') AS page_url,
  COUNT(1) AS event_count
FROM
  `your-gcp-project.analytics_XXXXXXXXX.events_*`
WHERE
  _TABLE_SUFFIX = FORMAT_DATE('%Y%m%d', CURRENT_DATE('-1 day'))
  AND event_name = 'page_view'
GROUP BY
  1, 2
ORDER BY
  event_count DESC
LIMIT 10;

Note: Replace your-gcp-project with your GCP Project ID and analytics_XXXXXXXXX with your GA4 property ID.

Solution 2: Go Open-Source and Self-Hosted with Matomo

For organizations prioritizing data privacy, ownership, and a more familiar UA-like interface, Matomo (formerly Piwik) stands out as a leading open-source alternative.

Data Ownership and Privacy by Design

• Full Data Control: Your data resides on your servers, giving you complete ownership and control.
• GDPR, CCPA, ePrivacy Compliant: Matomo is designed with privacy in mind, offering features like IP anonymization, opt-out mechanisms, and easy data export/deletion.
• No Data Sampling: Unlike some analytics platforms, Matomo provides raw, unsampled data.

Installation and Basic Configuration

Matomo can be deployed in various ways, with Docker Compose being a popular choice for ease of setup.

Example: Matomo Docker Compose Setup

Create a docker-compose.yml file:

version: '3'

services:
  db:
    image: mariadb:10.6
    command: --max-allowed-packet=64MB
    volumes:
      - matomo_db:/var/lib/mysql
    environment:
      MYSQL_ROOT_PASSWORD: your_root_password
      MYSQL_DATABASE: matomo
      MYSQL_USER: matomo
      MYSQL_PASSWORD: your_matomo_password
    restart: unless-stopped

  matomo:
    image: matomo:4-fpm-alpine
    links:
      - db
    volumes:
      - matomo_app:/var/www/html
    environment:
      MATOMO_DATABASE_HOST: db
      MATOMO_DATABASE_USERNAME: matomo
      MATOMO_DATABASE_PASSWORD: your_matomo_password
      MATOMO_DATABASE_NAME: matomo
      MATOMO_TRUSTED_HOSTS: "analytics.yourdomain.com" # Replace with your domain
    restart: unless-stopped

  web:
    image: nginx:alpine
    ports:
      - "80:80"
    volumes:
      - matomo_app:/var/www/html:ro
      - ./nginx.conf:/etc/nginx/conf.d/default.conf:ro
    restart: unless-stopped

volumes:
  matomo_db:
  matomo_app:

Create an nginx.conf file in the same directory:

server {
    listen 80;
    server_name analytics.yourdomain.com; # Replace with your domain

    root /var/www/html;
    index index.php index.html index.htm;

    location / {
        try_files $uri $uri/ /index.php?$query_string;
    }

    location ~ \.php$ {
        fastcgi_pass matomo:9000;
        fastcgi_index index.php;
        fastcgi_param SCRIPT_FILENAME $document_root$fastcgi_script_name;
        include fastcgi_params;
    }

    location ~ /\.ht {
        deny all;
    }
}

Then run: docker-compose up -d. Access Matomo via http://analytics.yourdomain.com and complete the web-based installation.

Example: Matomo JavaScript Tracking Code

Place this in the <head> or <body> of your website, replacing placeholders:

<!-- Matomo -->
<script>
  var _paq = window._paq = window._paq || [];
  /* tracker methods like "setCustomDimension" should be called before "trackPageView" */
  _paq.push(['trackPageView']);
  _paq.push(['enableLinkTracking']);
  (function() {
    var u="//analytics.yourdomain.com/"; // Replace with your Matomo URL
    _paq.push(['setTrackerUrl', u+'matomo.php']);
    _paq.push(['setSiteId', '1']); // Replace with your Site ID from Matomo UI
    var d=document, g=d.createElement('script'), s=d.getElementsByTagName('script')[0];
    g.async=true; g.src=u+'matomo.js'; s.parentNode.insertBefore(g,s);
  })();
</script>
<noscript><p><img src="//analytics.yourdomain.com/matomo.php?idsite=1&rec=1" style="border:0;" alt="" /></p></noscript>
<!-- End Matomo Code -->

Matomo vs. GA4: A Feature Comparison

Feature	Matomo (Self-Hosted)	Google Analytics 4 (GA4)
Data Ownership	100% on your servers	Google’s servers (raw data via BigQuery export)
Privacy Compliance	Built-in GDPR/CCPA features, cookieless tracking, IP anonymization	Consent Mode, cookieless measurement, IP anonymization
Data Sampling	No sampling (raw data always)	Sampling for complex queries in standard reports (BigQuery export offers raw data)
User Interface	Traditional, UA-like reports, customizable dashboards	Event-based, new UI, relies heavily on Explorations
Cost	Free (open-source), hosting/maintenance costs	Free (standard), BigQuery costs for large data volumes
Extensibility	Extensive plugin marketplace, open APIs	Integrates with Google ecosystem (Ads, Search Console), BigQuery, API
Maintenance	Requires server administration and updates	Managed by Google

Solution 3: Product Analytics & Data Lake Integration with PostHog

For modern product-led companies, or those seeking deep behavioral insights, A/B testing, and feature flags alongside analytics, platforms like PostHog (open-source) or SaaS alternatives like Mixpanel/Amplitude offer a different paradigm than traditional web analytics.

Beyond Pageviews: Deep Product Insights

These tools focus on understanding user behavior within your application, not just traffic to your website.

• Event-Driven by Design: They natively track every user interaction as an event.
• Behavioral Analytics: Funnels, retention analysis, user paths, cohort analysis are core features.
• A/B Testing & Feature Flags: Integrate experimentation directly into your analytics workflow.
• Session Replays: Understand user experience visually (PostHog).
• Data Lake Integration: Designed to push data into your own data warehouse (e.g., Snowflake, BigQuery, Redshift) for further analysis and integration with other business data.

PostHog is an excellent open-source choice that combines many of these features into a single self-hostable platform.

Deployment and Event Capture

PostHog can be self-hosted on a Kubernetes cluster or a single server via Docker Compose.

Example: PostHog Docker Deployment (Simplified for a Single Server)

First, install Docker and Docker Compose. Then, run the PostHog deployment script:

/bin/bash -c "$(curl -fsSL https://posthog.com/install.sh)"

Follow the prompts. This script sets up a complete PostHog instance including ClickHouse for data storage, a PostgreSQL database, and the PostHog application containers.

After installation, you can access PostHog at the IP address or domain you configured during setup.

Example: PostHog JavaScript Integration for Event Capture

Add the following to your website’s <head> section, replacing 'YOUR_API_KEY' and 'https://your-posthog-instance.com':

<script>
!function(t,e){var o,n,p,r;e.__SV||(window.posthog=e,e._i=[],e.init=function(i,s,a){function g(t,e){var o=e.split(".");2==o.length&&(t=t[o[0]],e=o[1]),t[e]=function(){t.push([e].concat(Array.prototype.slice.call(arguments)))}}(p="get disable_session_recording posthog_enable_recording posthog_stop_recording identify isFeatureEnabled onFeatureFlags getFeatureFlag setPersonProperties register register_once alias reset group setEventProperties capture_pageview capture_start_session set_config_properties set_config set_autocapture send_events_to_posthog capture exception track_sentiment report_exception").split(" "),r=["identify","reset","capture"];for(o=0;o<p.length;o++)g(e,p[o]);for(n=0;n<r.length;n++)g(a[r[n]]||e,r[n]);e._i.push([i,s,a])},e.__SV=1)}(document,window.posthog||[]);
posthog.init('YOUR_API_KEY',{api_host:'https://your-posthog-instance.com'}) // Replace with your API key and instance URL
</script>

To capture a custom event (e.g., a button click):

<button onclick="posthog.capture('signup_button_clicked', { plan_type: 'premium' })">Sign Up</button>

Conclusion: Choose Your Analytics Weapon Wisely

The “GA4 sucks” sentiment is a symptom of a deeper challenge: the evolving landscape of web and product analytics. There’s no one-size-fits-all solution, but by understanding the strengths and weaknesses of each approach, you can make an informed decision:

• For existing GA4 users who feel lost: Invest time in understanding its event model, leverage Explorations, and consider BigQuery for advanced needs. GA4 is powerful when used as intended.
• For privacy-conscious organizations desiring full data control and a familiar UI: Matomo is an excellent open-source, self-hosted option that delivers on data sovereignty and compliance.
• For product-led teams needing deep behavioral insights, A/B testing, and a scalable data stack: Solutions like PostHog (open-source) or Mixpanel/Amplitude (SaaS) are purpose-built to go beyond traditional web analytics and integrate tightly with your product development lifecycle.

Ultimately, the “best” alternative depends on your organization’s specific needs, technical capabilities, and privacy requirements. Evaluate your goals, experiment with these tools, and choose the analytics platform that empowers you, rather than frustrates you.

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