docs
Analytics

Analytics

Gain deep insights into your testing patterns, performance trends, and optimization opportunities with Testr's comprehensive analytics dashboard.

Analytics Overview

Testr's analytics platform provides multiple views of your testing data:

  • 📊 Key Metrics Dashboard: High-level performance indicators
  • 📈 Trend Analysis: Historical patterns and changes over time
  • 🔥 Activity Heatmap: GitHub-style visualization of testing activity
  • 🎯 Success Rate Tracking: Monitor test reliability and failure patterns
  • 💰 Credit Usage Analysis: Optimize testing budgets and efficiency
  • 🤖 AI-Powered Insights: Automated recommendations for improvement

Accessing Analytics

From Dashboard

  1. Navigate to your Dashboard
  2. Click "Analytics" in the main navigation
  3. Select your desired time period (7 days, 30 days, 90 days, 1 year)

Real-time Updates

Analytics data updates automatically:

  • Test completions: Immediate integration
  • Metrics recalculation: Every hour
  • Trend analysis: Daily updates
  • AI insights: Weekly generation

Key Metrics Dashboard

Primary Performance Indicators

📊 Total Tests

  • Total number of tests executed in selected period
  • Trend comparison vs. previous period
  • Growth indicator (positive/negative change)

🎯 Success Rate

  • Percentage of tests completed successfully
  • Period-over-period comparison
  • Industry benchmark context

⚡ Credits Used

  • Total credits consumed
  • Average credits per test
  • Budget efficiency tracking

⏱️ Average Duration

  • Mean test execution time
  • Performance trend analysis
  • Optimization opportunities

Analytics Dashboard Comprehensive analytics dashboard with key metrics, trends, and AI-powered insights

Trend Indicators

Each metric includes trend arrows showing:

  • 📈 Green Up Arrow: Improvement vs. previous period
  • 📉 Red Down Arrow: Decline vs. previous period
  • ➡️ Gray Arrow: No significant change

Activity Heatmap

GitHub-Style Visualization

The activity heatmap shows your testing frequency over time:

📅 Daily Activity Squares

  • Darker Green: More tests executed
  • Light Green: Some testing activity
  • Gray: No tests on that day
  • Hover Details: Exact test count per day

📈 Usage Patterns

  • Identify peak testing days
  • Spot testing gaps or inconsistencies
  • Plan better testing schedules
  • Track team adoption over time

Interpreting Heatmap Data

🟢 Consistent Dark Green: Regular, heavy testing (ideal) 🟡 Mixed Light/Dark: Variable testing patterns ⚪ Many Gray Days: Infrequent testing (needs attention) 📊 Weekly Patterns: Identify workday vs. weekend usage

Success vs. Failure Rate Analysis

Interactive Charts

📊 Success Rate Trends

  • Line chart showing success percentage over time
  • Failure rate overlay for complete picture
  • Trend line indicating overall direction

🎯 Failure Pattern Recognition

  • Common failure causes
  • Time-based failure clustering
  • Environment-specific issues

Key Insights

High Success Rate (>90%)

  • Well-designed tests
  • Stable target environments
  • Good testing practices

Medium Success Rate (70-90%)

  • Some optimization needed
  • Review failing test patterns
  • Consider environment stability

Low Success Rate (below 70%)

  • Significant issues present
  • Test design problems
  • Environment or infrastructure issues

Credit Usage Analysis

Spending Patterns

📈 Credit Consumption Chart

  • Daily credit usage over time
  • Spending trend analysis
  • Budget burn rate calculation

💰 Cost Efficiency Metrics

  • Credits per successful test
  • Most expensive test types
  • Optimization opportunities

Budget Optimization

🔍 Usage Insights

  • High-Spend Tests: Identify credit-heavy scenarios
  • Efficient Tests: Find your most cost-effective patterns
  • Waste Detection: Spot unnecessary credit consumption
  • Budget Planning: Forecast future credit needs

Test Volume Trends

Execution Patterns

📊 Daily Test Volume

  • Number of tests executed per day
  • Peak activity periods
  • Growth trends over time

📈 Test Type Distribution

  • Step-by-step vs. Explorer test ratios
  • Average credits per test type
  • Success rates by test mode

Capacity Planning

Use volume data for:

  • Team Scaling: Plan for increased testing needs
  • Credit Budgets: Allocate appropriate resources
  • Infrastructure: Ensure platform can handle growth
  • Process Optimization: Identify workflow improvements

Performance Metrics

Execution Speed Analysis

⚡ Average Test Duration

  • Mean execution time trends
  • Performance degradation detection
  • Speed optimization opportunities

🏃 Step Execution Speed

  • Time per step analysis
  • Bottleneck identification
  • Efficiency improvements

Performance Optimization

🔍 Slow Test Identification

  • Tests taking longer than average
  • Performance regression detection
  • Optimization recommendations

⚡ Speed Improvement Tips

  • Reduce unnecessary verification steps
  • Optimize target URL selection
  • Choose faster target environments
  • Streamline test step sequences

AI-Powered Insights

Automated Recommendations

The analytics dashboard generates intelligent insights:

✅ Success Insights

  • "Your tests show consistent 95% success rate"
  • "Explorer tests are 20% more efficient than step-by-step"
  • "Tuesday testing shows highest success rates"

⚠️ Warning Insights

  • "Credit usage increased 30% this month"
  • "Failure rate spiked in the last week"
  • "Some tests failing due to timeout issues"

💡 Optimization Insights

  • "Consider reducing credit budgets for simple tests"
  • "Consolidate similar tests into reusable templates"
  • "Peak testing efficiency occurs between 10am-2pm"

Insight Categories

🎯 Performance: Speed and efficiency improvements 💰 Cost: Credit usage optimization 🔧 Technical: Infrastructure and reliability issues 📈 Growth: Scaling and adoption opportunities

Time Period Comparison

Flexible Date Ranges

📅 Predefined Periods

  • Last 7 days
  • Last 30 days
  • Last 90 days
  • Last year

📊 Period Comparison

  • Current vs. previous period metrics
  • Percentage change calculations
  • Trend direction indicators

Historical Analysis

📈 Long-term Trends

  • Identify seasonal patterns
  • Track improvement over time
  • Measure ROI of testing investments
  • Plan future testing strategies

Export and Sharing

Data Export Options

📊 Chart Images: Export visualizations for presentations 📋 CSV Data: Raw data for further analysis 📄 PDF Reports: Comprehensive analytics summaries 🔗 Shareable Links: Team collaboration and reporting

Team Reporting

Share insights with stakeholders:

  • Executive Summaries: High-level metrics for leadership
  • Technical Reports: Detailed analysis for development teams
  • Budget Reports: Cost analysis for financial planning
  • Performance Reviews: Success metrics for team evaluation

Integration with Testing Workflow

Continuous Improvement

Use analytics to drive testing excellence:

🔄 Regular Review Cycles

  • Weekly team analytics reviews
  • Monthly trend analysis
  • Quarterly optimization planning
  • Annual strategy assessment

📈 Data-Driven Decisions

  • Test design improvements
  • Credit budget allocation
  • Team training focus areas
  • Infrastructure investments

Advanced Analytics Features

Custom Metrics

Track specific KPIs relevant to your needs:

  • Business-Critical Path Success: Monitor key user journey tests
  • Regression Test Effectiveness: Track deployment validation
  • Team Performance: Individual and group analytics
  • Environment Stability: Success rates by target environment

Alerts and Notifications

Set up automated alerts for:

  • Success Rate Drops: Below threshold warnings
  • Credit Budget Limits: Spending alerts
  • Performance Degradation: Slow test notifications
  • Failure Spikes: Immediate issue alerts

Best Practices

Regular Monitoring

📅 Daily Checks

  • Review overnight test results
  • Check for any failure spikes
  • Monitor credit usage against budget

📊 Weekly Analysis

  • Analyze trend changes
  • Review AI insights and recommendations
  • Plan upcoming testing priorities

📈 Monthly Reviews

  • Comprehensive performance analysis
  • Budget planning and optimization
  • Team performance evaluation
  • Strategic planning updates

Data-Driven Optimization

🎯 Success Rate Improvement

  • Identify and fix failing test patterns
  • Optimize test environments
  • Improve test design based on analytics
  • Monitor success rate trends

💰 Cost Optimization

  • Analyze credit efficiency patterns
  • Optimize credit budgets per test type
  • Eliminate wasteful testing practices
  • Plan budget allocation based on usage

Next Steps

Master your testing analytics:

  1. Create More Tests - Build comprehensive test coverage
  2. Optimize Execution - Improve test reliability
  3. MCP Integration - Automate testing workflows
  4. Set Up Alerts - Get notified of important changes

Ready to integrate with AI assistants? Explore MCP Integration for advanced automation!

Running TestsOverview