Client Analytics
The ComUnity Platform offers client analytics functionality through the integration of Matomo, a leading open-source analytics tool, providing a comprehensive and intuitive interface for analysing user interactions within your projects. This integration furnishes a diverse array of features designed to augment your analytics strategy, enabling effective collection, analysis, and interpretation of user data to drive informed decision-making and optimise user engagement.
Key Benefits
Track user interactions in real time, providing insights into user behaviour and preferences.
Access detailed analytics reports, assisting in the identification of trends and informing data-driven decision-making processes.
Ensure compliance with global data privacy standards, reflecting Matomo's emphasis on user privacy protection.
To leverage your project's Client Analytics dashboard powered by Matomo, follow these steps:
Enable Observability: Activate the observability feature in your project. For detailed instructions on enabling observability within the Toolkit, refer to the Observability Integration guide.
Access the Dashboard: Navigate to the Observability tab. By default, the Client Analytics tab will be displayed, showcasing your analytics dashboard.
Client Analytics
This integration not only enhances the analytical capabilities within the ComUnity Platform but also aligns with the commitment to providing user-centric, secure, and effective digital solutions.
Understanding Client Analytics
When you navigate to Observability → Client Analytics, you'll see the Matomo analytics dashboard showing how users interact with your application.
Client Analytics focuses on user behaviour (what users do), which complements the other observability tools:
Metrics track system performance (how your infrastructure performs)
Traces track request flows (how data moves through your system)
Client Analytics track user actions (how people use your application)
Together, these three tools give you complete visibility: system health, request-level debugging, and user experience insights.
Key Metrics Explained
Visits vs Visitors
Understanding the difference between visits and visitors helps you measure both total engagement and unique user growth.
Visits (Sessions)
A visit is one browsing session by a user
If a user visits your app in the morning, leaves, and returns in the afternoon, that counts as 2 visits
Session typically ends after 30 minutes of inactivity
Use this to: Understand total engagement and activity levels
Visitors (Unique Users)
A visitor is a unique person (tracked by device/browser)
Same person visiting multiple times = 1 visitor, multiple visits
Use this to: Track your actual user base size and growth
Example:
100 visitors made 250 visits = Each user visited ~2.5 times on average
This indicates moderate engagement—users return but aren't heavily engaged
Pages Per Visit (Engagement Depth)
What it shows: Average number of pages or screens viewed during each visit
Interpreting the metric:
Higher is better - Indicates users exploring and engaging with your app
Context matters - What's "good" depends on your application type
Typical ranges by application type:
E-commerce: 3-5 pages typical
Content/News apps: 5-10 pages indicates good engagement
SaaS/Productivity tools: 2-4 pages per session
Single-purpose apps: 1-2 pages may be normal
Warning signs: Very low pages per visit (1-2) in applications that expect exploration might indicate:
Users not finding what they need
Poor navigation or confusing interface
Technical issues preventing page loads
Wrong audience or unclear value proposition
Action: Review which pages users land on and where they immediately exit. Check for navigation issues or missing content.
Visit Duration
What it shows: Average time users spend in your application per visit
Context-dependent benchmarks:
News/Content app: 5-15 minutes indicates engaged reading
Productivity tool: Longer sessions show active use (10-30 minutes)
E-commerce: 2-5 minutes for browsing and purchasing
Quick-action apps: 1-2 minutes may be perfectly normal
Warning signs: Very short visits (under 30 seconds) consistently might indicate:
Users arriving with wrong expectations
Technical problems (slow loading, errors)
Poor first impression or unclear interface
Search engine traffic to irrelevant pages
Improvement strategies:
Improve page load times (check Metrics for performance issues)
Clarify value proposition on landing pages
Enhance navigation and content discovery
Review entry pages for relevance
Bounce Rate
What it shows: Percentage of visits where the user viewed only one page and left without any interaction
Calculation: Single-page visits ÷ Total visits × 100
Healthy bounce rates by application type:
Blog/Article sites: 40-60% is normal (users read one article and leave)
E-commerce: 20-40% is healthy
Web applications: Under 20% is ideal
Landing pages: 70-90% is common (single purpose)
High bounce rate concerns: If your bounce rate exceeds 70% for an interactive application, investigate:
Slow page loading (check Metrics dashboards)
Users not finding expected content
Poor mobile experience (compare device types)
Confusing navigation or broken links
Action steps:
Identify specific high-bounce pages
Review those pages for technical errors (check Logs)
Analyse entry source (did users expect something else?)
Test the user experience on that page
Real-Time Visitors
What it shows: Number of users actively using your app right now
Practical uses:
1. Monitor launches or campaigns
Watch real-time visitors during a product launch
Verify marketing campaigns are driving traffic
See immediate impact of announcements
2. Verify deployment health
Real-time visitors dropping to zero = potential problem
Sudden spike might indicate bot attack or viral content
Compare to typical patterns for the time of day
3. Understand peak usage times
Identify when most users are active
Schedule maintenance during low-traffic periods
Plan capacity for known peak times
Top Pages and Screens
What it shows: Most visited pages in your application, ranked by visit count
Strategic uses:
Identify your most valuable features
High-traffic pages represent key user workflows
These pages deserve the most testing and optimization
Performance issues here have biggest impact
Prioritise development efforts
Focus improvements on pages users actually visit
Deprioritise or remove unused features
Optimise high-traffic page performance first
Understand user navigation patterns
See how users move through your app
Identify unexpected usage patterns
Discover shortcuts or workarounds users have found
Example insights:
Settings page has very high traffic → May need simplification
Help page is most popular → Main UI may need clarification
Advanced feature page has low traffic → Consider hiding or promoting it
Geographic Distribution
What it shows: Where your users are located, broken down by countries and cities
Strategic uses:
Plan localisation efforts
If 30% of users are in Spain, consider Spanish translation
Identify emerging markets worth targeting
Understand cultural context for feature development
Schedule maintenance windows
Choose times when your primary markets are asleep
Minimise impact on your largest user bases
Coordinate with timezone distribution
Detect unusual patterns
Sudden traffic from unexpected countries may indicate bots
Geographic concentration helps identify business opportunities
Compare geographic distribution to your target market
Security considerations:
Unusual geographic spikes may indicate attacks
Cross-reference with error rates in Logs
Monitor for distributed login attempts
Device Types: Mobile vs Desktop vs Tablet
What it shows: Breakdown of devices users are using to access your application
Critical for responsive design:
If 80% of users are on mobile but your app isn't mobile-optimized, that's a critical issue
Tablet users often get poor experiences on responsive designs optimized for mobile/desktop
Device preferences vary by user demographic and application type
Prioritization guidance:
Focus optimization on dominant device type first
Test critical workflows on all device types users actually use
Compare bounce rate and engagement across devices
Warning signs:
Mobile bounce rate much higher than desktop → Responsive design issues
Very short mobile sessions compared to desktop → UX problems
High mobile traffic but low conversions → Mobile checkout flow problems
Action: Navigate to Visitors → Devices → Device Type and compare:
Bounce rate across devices (should be similar)
Pages per visit (mobile slightly lower is acceptable)
Conversion rates or goal completion
Browser and Operating System
What it shows: Which browsers and OS versions your users are using
Practical decisions:
Testing priorities
Test on browsers that represent 90%+ of your traffic
Focus testing on specific browser versions your users have
Identify if users are stuck on old browsers (corporate restrictions?)
Support decisions
Safely drop support for browsers with <1% usage
Plan compatibility testing around actual user distribution
Identify browser-specific issues in error patterns
Debug browser-specific issues
Cross-reference browser data with error logs
Identify if certain browsers have higher error rates
Test features in browsers showing problems
Traffic Sources and Referrers
What it shows: How users found your application
Source categories:
Direct: User typed URL or used bookmark (indicates loyalty/awareness)
Search Engines: Google, Bing, etc. (organic discovery)
Social Media: Facebook, Twitter, LinkedIn, etc.
Referral Sites: Links from other websites
Campaigns: Marketing campaigns with tracking parameters
Strategic uses:
Measure marketing effectiveness
Track which campaigns drive the most traffic
Calculate ROI by comparing campaign cost to conversions
Identify most effective channels
Understand discovery patterns
High search traffic indicates strong SEO or brand awareness
Social media traffic patterns show viral potential
Referral traffic identifies partnership opportunities
Optimize acquisition strategy
Double down on high-performing channels
Investigate why certain channels underperform
A/B test different acquisition approaches
Navigating Time Ranges
Matomo allows you to analyse data across different time periods. Selecting the right time range helps you understand patterns versus anomalies.
Recommended Time Ranges by Use Case
Today (Real-time monitoring)
Monitor current campaign or launch performance
Verify deployment didn't break user experience
Track real-time response to announcements
Caution: Incomplete data - today's metrics will change
Yesterday (Completed day analysis)
Compare to historical data without partial data skewing results
Verify yesterday's deployment success
Review complete day patterns
Best for: Day-to-day monitoring and comparisons
Last 7 Days (Weekly patterns)
Identify weekday vs weekend patterns
Smooth out daily variations
See weekly trends emerging
Best for: Understanding normal variation
Last 30 Days (Monthly trends)
Monthly trends and patterns
Compare month-over-month growth
Seasonal pattern analysis
Best for: Strategic planning and reporting
Custom Date Range (Specific comparisons)
Before/after feature releases
Campaign period analysis
Quarter-over-quarter comparisons
Best for: Measuring specific changes or events
Practical Analytics Workflows
Workflow 1: Track Feature Adoption
Goal: Determine if users are adopting your new feature
Steps:
Navigate to Behaviour → Pages (or Screens for mobile apps)
Search for the page or screen where your feature is located
Review visit count and trend over time
Calculate adoption rate: (Feature page visits ÷ Total site visits) × 100
Metrics indicating success:
Increasing visits over time - Growing adoption
Return visitors to feature page - Users finding value
Reasonable time on page - Users engaging with feature
Low exit rate - Users continuing to other pages
Metrics indicating problems:
High bounce rate on feature page - Users trying but not engaging
Short time on page - Feature not compelling or unclear
Declining visits after initial spike - Initial interest but no retention
High exit rate - Feature is a dead end
Action based on findings:
If low adoption: Improve feature promotion and discoverability
If high bounce: Review feature UX and clarity
If short sessions: Add more value or clearer instructions
If high exit: Connect feature better to other workflows
Workflow 2: Identify Drop-Off Points in Workflows
Goal: Find where users abandon multi-step processes (checkout, onboarding, forms)
Steps:
Navigate to Behaviour → Pages
Review exit rates (percentage of sessions ending on each page)
Map your expected workflow step-by-step
Identify pages with unusually high exit rates
Example: E-commerce checkout analysis
Product page: 30% exit (normal—users browsing)
Cart page: 20% exit (acceptable—users comparing)
Shipping info: 15% exit (acceptable)
Payment info page: 60% exit ← Problem identified
Confirmation: 2% exit (normal)
Investigation steps:
Check for technical errors - Search Logs for errors during that time period
Review page performance - Check Metrics for slow loading on that page
Test user experience - Actually go through the flow yourself
Compare devices - Is drop-off higher on mobile vs desktop?
Check form validation - Are validation errors causing frustration?
Common drop-off causes:
Unexpected costs revealed late in process
Complex or confusing form fields
Technical errors or slow loading
Security concerns (lack of trust indicators)
Required account creation
Limited payment options
Workflow 3: Compare Mobile vs Desktop Experience
Goal: Ensure mobile users have an experience comparable to desktop users
Steps:
Navigate to Visitors → Devices → Device Type
Compare key metrics between Mobile and Desktop:
Bounce rate
Pages per visit
Average visit duration
Conversion rates (if goals configured)
Identify significant discrepancies
Healthy mobile patterns:
Bounce rate within 10-15% of desktop
Pages per visit 20-30% lower than desktop (normal - mobile users more focused)
Visit duration 30-40% shorter than desktop (normal - mobile sessions shorter)
Conversion rates within 20% of desktop
Problem indicators:
Mobile bounce rate 2x higher than desktop → Responsive design issues
Very short mobile sessions (under 30 seconds) → Major UX problems
High exit rate on specific pages (mobile only) → Page not mobile-friendly
Mobile conversions significantly lower → Mobile checkout flow problems
Action steps:
Test the mobile experience yourself on actual devices
Check Metrics for mobile page load times
Review Logs for mobile-specific errors
Use browser developer tools to test responsive design
Consider mobile-first redesign if mobile is dominant traffic source
Workflow 4: Measure Campaign Effectiveness
Goal: Calculate ROI and effectiveness of marketing campaigns
Prerequisites:
Campaigns must use tracking parameters (UTM codes)
Example:
?utm_source=facebook&utm_campaign=spring_sale
Steps:
Navigate to Acquisition → Campaigns
Select the campaign period in date range
Review campaign metrics:
Total visits
New visitors brought in
Bounce rate (quality of traffic)
Pages per visit (engagement)
Goal conversions (if configured)
Calculate cost per acquisition (CPA)
ROI Calculation:
Quality indicators:
Low bounce rate (< 40%) - Relevant traffic
High pages per visit - Engaged visitors
Strong conversion rate - Effective targeting
Return visitors from campaign - Building loyalty
Poor performance indicators:
High bounce rate (> 70%) - Wrong audience or misleading ads
Very short sessions - Poor landing page experience
Low conversion despite high traffic - Landing page optimization needed
High cost per conversion - Need better targeting or creative
Workflow 5: Identify Your Power Users
Goal: Understand what highly engaged users do differently
Steps:
Navigate to Visitors → Engagement → Visits by Duration
Review users with longest sessions (top 10%)
Navigate to Visitors → Engagement → Pages per Visit
Identify users viewing the most pages
Cross-reference to find overlap
Power user characteristics to identify:
Much longer than average session duration
Significantly more pages per visit
Frequent return visits
Complete key workflows or conversions
Access advanced features
Strategic uses of power user insights:
Feature prioritisation - What features do power users love?
User personas - Create profiles of highly engaged users
Retention tactics - What keeps power users coming back?
Monetisation - Power users often willing to pay for premium features
Beta testing - Recruit power users for testing new features
Understanding User Segments
Segmenting users helps you understand different behaviour patterns and optimise for specific groups.
New vs Returning Visitors
New Visitors:
First time visiting your application
Critical for acquisition metrics
Higher bounce rate is more acceptable (they're exploring)
Need clear onboarding and value proposition
May not complete goals on first visit
Returning Visitors:
Came back after initial visit
Strong indicator of product-market fit
Should have better engagement metrics
Already understand your interface
More likely to convert or complete goals
Healthy application indicators:
Growing mix of both new and returning visitors
Return visitor rate of 40-60%
Returning visitors have 2-3x engagement vs new
New visitor conversion rate reasonable for your industry
If return visitor rate is low (< 20%):
Users trying once and not finding value
Lack of sticky features or compelling content
Poor first-time user experience
No reason to return (check if you have new content or features)
Segmenting by Geography
Different regions often use features differently:
Cultural differences affect feature preferences
Time zones affect peak usage hours
Internet speeds vary by region (affects performance needs)
Language preferences may indicate localisation needs
Regulatory requirements vary by country
Action: Use geographic data to inform localisation, feature development, and support priorities.
Segmenting by Device
Mobile users often exhibit different behaviour:
Shorter, more focused sessions
Use different features (location-based, camera)
Need simpler, faster interfaces
May require different optimisation priorities
Desktop users typically:
Longer sessions with more exploration
Complete complex workflows
Use productivity features more
Tolerate more complex interfaces
Action: Optimise critical workflows for each device type's usage patterns.
Privacy and Compliance
Matomo is designed with privacy as a core principle and helps you comply with data protection regulations.
Privacy Features Built Into Matomo
IP Address Anonymisation
Matomo automatically anonymises IP addresses
Only partial IP addresses are stored
Users cannot be individually identified by IP
Do Not Track (DNT) Respect
Matomo honours browser DNT signals
Users who set DNT are not tracked
Automatic compliance with user preferences
Cookie-less Tracking Option
Matomo can track without using cookies
Reduces privacy concerns
Maintains compliance with strict regulations
Data Ownership
All data stays on your infrastructure
No sharing with third-party advertising networks
Complete control over data retention and deletion
Compliance with Regulations
GDPR (Europe)
Matomo provides required privacy controls
Supports user data export and deletion requests
Built-in consent management features
No data transferred outside your control
CCPA (California)
Supports opt-out requirements
Provides data access for users
Enables data deletion on request
HIPAA/Healthcare
Can be configured for healthcare compliance
No PII stored if configured correctly
Full audit trail of data access
User Privacy Rights
Users of your application have the right to:
Know what data is collected - Analytics tracking is disclosed
Opt out of tracking - Via DNT or your consent mechanism
Access their data - Request report of tracked activities
Delete their data - Request removal from analytics
Best practices:
Include analytics tracking in your privacy policy
Provide clear opt-out mechanism
Respect user privacy preferences
Only track what you need for legitimate purposes
Setting Up Goals and Conversions
Goals track specific user actions that indicate success for your business. This is an advanced feature typically configured by administrators.
Common goal types:
User completes registration
User makes a purchase
User submits a form
User watches a video to completion
User reaches a specific page (thank you page)
User stays on site for specific duration
Why goals matter:
Convert raw visits into meaningful business metrics
Calculate conversion rates and ROI
Identify which traffic sources deliver valuable users
Optimize user flows toward goal completion
Note: Goal configuration requires administrator access. If you need goals set up, contact your platform administrator or see the Technical Documentation.
Connecting Analytics to Other Observability Data
Client Analytics tells you what users do, but sometimes you need to understand why by correlating with system data.
Analytics → Logs
When: Analytics shows users abandoning a specific page Action: Check Logs for errors on that page during the same time period
Example:
Steps:
Note exact time when bounce rate increased
Navigate to Logs
Search for errors:
{service_name="checkout"} |= "ERROR"Filter to the time period
Identify correlation between errors and bounce rate
Analytics → Metrics
When: Analytics shows traffic spike but poor engagement Action: Check Metrics to see if performance degraded under load
Example:
Steps:
Note when engagement metrics dropped
Navigate to Metrics dashboard
Check P99 latency, error rate, and resource usage
Compare normal load to spike period
Identify performance bottleneck
Analytics → Traces
When: Users report slow page loads on specific features Action: Use Traces to identify slow operations
Example:
Steps:
Identify problematic page from Analytics
Navigate to Traces
Search for traces to that endpoint
Review trace duration and identify bottlenecks
Optimise slow operations
Tips for Effective Analytics Use
✅ DO:
Check analytics regularly - Weekly for production apps minimum
Focus on trends, not single days - Day-to-day fluctuations are normal
Segment your data - Overall metrics hide important patterns
Set up goals - Track what actually matters to your business
Compare time periods - This month vs last month, this quarter vs last quarter
Correlate with other data - Link to Logs, Metrics, Traces when investigating issues
Test on real devices - Don't rely only on data, experience it yourself
❌ DON'T:
Obsess over vanity metrics - Total visits matter less than engagement and conversion
Ignore context - Traffic drop might be expected (weekend, holiday)
Make decisions from small samples - Need enough data for statistical significance
Forget about bots - High traffic with 100% bounce rate might be bot traffic
Compare dissimilar time periods - Holiday week vs normal week isn't meaningful
Ignore mobile experience - If mobile is growing, prioritize mobile optimization
Skip the qualitative - Numbers tell you what, but not why
Next Steps
See unusual user behaviour? → Check Logs and Metrics for system issues
Want to track custom events? → See Instrumentation (coming soon)
Need to be notified of traffic anomalies? → Set up Alerts (coming soon)
Want detailed analytics setup? → See Technical Documentation
Technical Details
Client Analytics is powered by:
Matomo - Open-source analytics platform
Privacy-first tracking - GDPR, CCPA, and HIPAA compliant
Self-hosted - Data stays on your infrastructure
No third-party sharing - Complete data ownership
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