Task Analytics

Track task completion patterns, time metrics, and productivity trends to optimize your workflow and improve team efficiency.

Overview

Task analytics help you understand:

• How quickly tasks are completed
• Which tasks take longer than expected
• Completion rate trends over time
• Bottlenecks in your workflow
• Task distribution across projects and team members

Use these insights to refine estimates, balance workloads, and improve planning accuracy.

Prerequisites

Required role: Any (see metrics for tasks you have access to) Plan requirements:

• Free: Basic metrics, 30-day history
• Pro: Advanced analytics, 1-year history
• Enterprise: Full analytics suite, unlimited history

Accessing Task Analytics

Navigate to task-specific analytics:

1. Click "Analytics" in the left sidebar
2. Select "Tasks" from the tab menu
3. Choose analysis type:

• Overview (completion metrics)
• Time Analysis (duration and estimates)
• Trends (historical patterns)
• Distribution (by project, assignee, priority)

4. Apply filters to narrow down data

Key Task Metrics

Completion Rate

Percentage of tasks completed within their time constraints:

Calculation:

Completion Rate = (Completed on Time / Total Completed) × 100

Tracking by:

• Overall: All tasks across organization
• By Project: Compare project efficiency
• By Assignee: Individual performance
• By Priority: High vs. medium vs. low priority tasks
• By Task Type: Bugs, features, maintenance, etc.

Healthy benchmarks:

• High priority: 85%+ completion rate
• Medium priority: 75%+ completion rate
• Low priority: 65%+ completion rate

Improving completion rates:

• Set more realistic due dates
• Break large tasks into smaller ones
• Address common blockers proactively
• Improve task clarity and requirements

Average Completion Time

Mean duration from task creation to completion:

View by:

• All tasks: Overall average
• Priority level: Do high-priority tasks complete faster?
• Task size: Small vs. large tasks
• Creator: Who creates well-scoped tasks?
• Project: Which projects have predictable timelines?

Analysis insights:

Small tasks (1-2 hours estimate): Average 1.5 days
Medium tasks (1 day estimate): Average 3 days
Large tasks (3+ days estimate): Average 8 days

Use this data to:

• Improve future estimates
• Identify workflow bottlenecks
• Set realistic expectations with stakeholders
• Plan sprint capacity

Cycle Time

Time from when work begins (status changes to "In Progress") to completion:

Why it matters: Cycle time excludes queue time, showing actual work duration. This is more actionable than total completion time.

Typical patterns:

• Short cycle time, long total time = Tasks sitting in backlog too long
• Long cycle time, similar total time = Tasks start immediately but take long to complete

Optimization strategies:

• Reduce cycle time: Minimize interruptions and context switching
• Reduce queue time: Better prioritization and capacity planning

Task Velocity

Number of tasks completed per time period:

Velocity tracking:

• Daily: Spot daily productivity patterns
• Weekly: Standard sprint planning metric
• Monthly: Long-term capacity planning

Team velocity example:

Week 1: 24 tasks
Week 2: 28 tasks
Week 3: 22 tasks
Week 4: 26 tasks
Average: 25 tasks/week

Using velocity for planning: If your average velocity is 25 tasks/week, plan sprints with 23-27 tasks to account for variability.

Time Tracking Analysis

Estimated vs. Actual Time

Compare time estimates to actual completion time:

View the comparison:

1. Navigate to Analytics → Tasks → Time Analysis
2. Review Estimation Accuracy chart
3. Filter by project or team member

Understanding the data:

• Over-estimated: Tasks completed faster than expected (green)
• On-target: Within 10% of estimate (yellow)
• Under-estimated: Took longer than expected (red)

Typical accuracy:

• Experienced teams: 70-80% within 10% of estimate
• New teams: 50-60% accuracy
• Complex projects: 40-50% accuracy

Improving estimation:

1. Review historical data for similar tasks
2. Break down large tasks for better accuracy
3. Include buffer for unknowns (multiply estimate by 1.2-1.5)
4. Track estimation accuracy per team member
5. Use reference tasks for comparison

Time-Based Patterns

Identify when tasks are completed:

Day of week analysis:

• Which days are most productive?
• When do tasks typically get completed?
• Are there end-of-week rushes?

Time of day patterns:

• Morning vs. afternoon completion rates
• Optimize meeting schedules around productive hours
• Consider time zones for distributed teams

Seasonal trends:

• Month-over-month productivity
• Holiday impact on completion rates
• Quarterly planning cycle effects

Task Distribution Analysis

By Priority

Understand how priority affects completion:

Priority breakdown:

High Priority: 25% of tasks, 40% of time
Medium Priority: 50% of tasks, 45% of time
Low Priority: 25% of tasks, 15% of time

Analysis questions:

• Are high-priority tasks actually completing first?
• Are low-priority tasks languishing?
• Is priority assigned consistently?

Optimization:

• Review priority assignments weekly
• Limit high-priority tasks to true urgencies
• Schedule time for low-priority work to prevent accumulation

By Project

Compare task metrics across projects:

Project comparison:

1. Navigate to Task Analytics → Distribution
2. Select "By Project" view
3. Compare metrics:

• Average completion time
• Completion rate
• Velocity
• Overdue percentage

Identifying issues:

• Projects with low completion rates may need clearer requirements
• Projects with high cycle time may have too many dependencies
• Projects with variable velocity may need better planning

By Assignee

Individual productivity metrics:

Available metrics:

• Tasks completed per week
• Average completion time
• Estimation accuracy
• Overdue task percentage
• Task types handled

Using assignee analytics:

• Identify specialists (who excels at what task types)
• Balance workload across team
• Set personal improvement goals
• Recognize top performers

Advanced Analytics

Burndown Charts

Track progress toward milestones or sprint goals:

Reading a burndown chart:

• X-axis: Time (days, weeks)
• Y-axis: Remaining tasks or story points
• Ideal line: Linear decline from start to target completion
• Actual line: Real progress

Healthy burndown: Actual line tracks close to ideal line with minor deviations.

Warning signs:

• Flat actual line: No progress, investigate blockers
• Increasing actual line: Scope creep, review priorities
• Steep drops: Unrealistic estimates or batch completions

Cumulative Flow Diagrams

Visualize task flow through your workflow stages:

Stages tracked:

• To Do (backlog)
• In Progress (active work)
• Review (awaiting approval)
• Done (completed)

Healthy flow:

• Steady, parallel bands
• Minimal work-in-progress
• Consistent throughput

Problem indicators:

• Bulging "In Progress": Too much concurrent work
• Bulging "Review": Review bottleneck
• Growing "To Do": Backlog accumulation
• Flat "Done": Low completion rate

Task Aging Analysis

Identify stale tasks that need attention:

Age categories:

• Fresh (0-7 days): Recently created
• Active (8-30 days): Normal age
• Aging (31-90 days): Needs review
• Stale (90+ days): Likely obsolete or blocked

Regular cleanup:

1. Review aging tasks monthly
2. Close obsolete tasks
3. Break down large tasks
4. Escalate blocked tasks
5. Reassign abandoned tasks

Analytics Use Cases

Use Case 1: Sprint Planning

Goal: Plan realistic sprint capacity

Process:

1. Review last 4 sprints velocity (average tasks completed)
2. Check team capacity for upcoming sprint (holidays, time off)
3. Adjust velocity estimate (reduce by 20% for unknowns)
4. Select tasks totaling adjusted velocity
5. Add buffer tasks (if time allows)

Result: More predictable sprint outcomes, fewer carryovers

Use Case 2: Process Improvement

Goal: Reduce average completion time

Analysis:

1. Identify tasks taking longer than average
2. Look for common characteristics:

• Similar task type?
• Same assignee?
• Specific project?
• Time of month?

3. Investigate root causes
4. Implement targeted improvements
5. Measure impact over next 4 weeks

Example finding: "Bug fixes take 2x longer than features because they require more investigation. Solution: Create investigation template to standardize approach."

Use Case 3: Workload Balancing

Goal: Ensure equitable task distribution

Review:

1. Check tasks per team member (last 30 days)
2. Compare to team average
3. Look for:

• Overloaded members (>120% average)
• Underutilized members (under 80% average)
• Skill mismatches (people working outside expertise)

4. Rebalance assignments
5. Monitor for improvement

Use Case 4: Client Reporting

Goal: Demonstrate progress to stakeholders

Prepare:

1. Filter tasks to specific project
2. Generate completion rate chart (last 90 days)
3. Create velocity trend (weekly)
4. Highlight completed milestones
5. Show remaining work burndown

Export:

• PDF report for email
• Excel data for custom analysis
• Live dashboard link for ongoing access

Troubleshooting

Metrics Seem Inaccurate

Common causes:

• Tasks marked complete without actually finishing
• Due dates set arbitrarily
• Tasks created and completed same day (skews averages)
• Timezone issues affecting date calculations

Fixes:

1. Audit task completion process
2. Establish due date guidelines
3. Exclude same-day tasks from analysis
4. Verify timezone settings

Can't See Certain Tasks in Analytics

Check:

• You have access to the project
• Tasks haven't been archived or deleted
• Date range includes the tasks
• Filters aren't excluding them

Solution: Clear all filters and expand date range to "All time" to see if tasks appear.

Analytics Loading Slowly

Optimization:

• Reduce date range (analyze last 90 days instead of all time)
• Filter to specific projects
• Avoid too many concurrent widgets
• Export large datasets for offline analysis

Velocity Varies Wildly

Possible reasons:

• Inconsistent task sizing
• Variable team capacity (holidays, PTO)
• Changing priorities and interruptions
• Scope changes mid-sprint

Stabilization strategies:

• Standardize task sizing (use story points or t-shirt sizes)
• Track capacity separately from velocity
• Protect sprint scope from changes
• Use rolling average instead of individual sprint velocity

Best Practices

Review Metrics Regularly Schedule weekly analytics reviews to catch trends early. Don't wait for monthly retrospectives.

Focus on Trends, Not Absolutes A team completing 20 tasks/week isn't necessarily worse than one completing 30—task complexity and size matter more than quantity.

Combine Metrics Don't look at completion rate in isolation. Consider it alongside cycle time, estimation accuracy, and team satisfaction.

Act on Insights Analytics are only valuable if they drive improvements. For every insight, define a specific action.

Track Improvements When you make a process change, mark it in your analytics (add a note or marker) so you can see its impact over time.

Share Insights Make analytics visible to the whole team. Transparency builds ownership and encourages improvement.

Avoid Micromanagement Use analytics to identify systemic issues, not to monitor individual productivity minute-by-minute.

Customize for Your Context What matters in a support team differs from a product team. Adapt metrics to your workflow and goals.

Related Documentation

• Dashboard Overview - Main analytics dashboard
• Team Performance - Team-specific metrics
• Custom Reports - Create custom analytics
• Project Analytics - Project-level insights
• Task Views - Different ways to view tasks

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Need help interpreting your metrics? Our support team can help you understand your analytics.