What are Quality Assurance metrics?
Finding the right Quality Assurance metrics can be daunting, especially when you're busy working on your day-to-day tasks. This is why we've curated a list of examples for your inspiration.
You can copy these examples into your preferred app, or alternatively, use Tability to stay accountable.
Find Quality Assurance metrics with AI
While we have some examples available, it's likely that you'll have specific scenarios that aren't covered here. You can use our free AI metrics generator below to generate your own strategies.
Examples of Quality Assurance metrics and KPIs
Metrics for Quality Assurance in Finance
1. Defect Rate
The percentage of products or services that have defects relative to the total produced, often calculated by dividing the number of defective units by the total number of units produced.
What good looks like for this metric: Typically less than 1%
Ideas to improve this metric- Implement stricter quality control processes
- Enhance staff training initiatives
- Conduct regular audits and inspections
- Utilise root cause analysis tools
- Increase customer feedback collection
2. First Pass Yield (FPY)
The percentage of products manufactured correctly and to specification the first time through the process without using rework.
What good looks like for this metric: 85%-95%
Ideas to improve this metric- Improve process documentation
- Increase equipment maintenance frequency
- Optimise employee onboarding and training
- Reduce process variability
- Incorporate automated quality checks
3. Customer Complaint Rate
The number of customer complaints received over a specific period divided by the number of transactions within that period.
What good looks like for this metric: Less than 5 per 1,000 transactions
Ideas to improve this metric- Improve after-sales support
- Analyse customer feedback for trends
- Maintain open communication channels
- Enhance product/service quality
- Regularly revise protocols based on feedback
4. Audit Compliance Rate
The percentage of audits that pass compliance checks relative to the total number of audits conducted.
What good looks like for this metric: Above 95%
Ideas to improve this metric- Regularly update compliance training for staff
- Automate compliance tracking
- Engage third-party compliance experts
- Conduct more frequent internal audits
- Develop corrective action plans for identified issues
5. Corrective Action Effectiveness
Measures the success of implemented corrective actions, determined by the reduction in defects and issues post implementation.
What good looks like for this metric: Reduction in issues by at least 75%
Ideas to improve this metric- Utilise a robust change management process
- Track and measure results of actions
- Ensure clear communication of changes to all stakeholders
- Perform regular follow-up checks
- Encourage continuous improvement culture
Metrics for Assessing software quality
1. defect density
Defect density measures the number of defects per unit of software size, usually per thousand lines of code (KLOC)
What good looks like for this metric: 1-5 defects per KLOC
Ideas to improve this metric- Improve code reviews
- Implement automated testing
- Enhance developer training
- Increase test coverage
- Use static code analysis
2. code coverage
Code coverage measures the percentage of code that is executed by automated tests
What good looks like for this metric: 70-80%
Ideas to improve this metric- Write more unit tests
- Implement integration testing
- Use better testing tools
- Collaborate closely with QA team
- Regularly refactor code for testability
3. mean time to resolve (MTTR)
MTTR measures the average time taken to resolve a defect once it has been identified
What good looks like for this metric: Less than 8 hours
Ideas to improve this metric- Streamline incident management process
- Automate triage tasks
- Improve defect prioritization
- Enhance developer expertise
- Implement rapid feedback loops
4. customer-reported defects
This metric counts the number of defects reported by end users or customers
What good looks like for this metric: Less than 1 defect per month
Ideas to improve this metric- Implement thorough user acceptance testing
- Conduct regular beta tests
- Enhance support and issue tracking
- Improve customer feedback channels
- Use user personas in development
5. code churn
Code churn measures the amount of code changes over a period of time, indicating stability and code quality
What good looks like for this metric: 10-20%
Ideas to improve this metric- Encourage smaller, iterative changes
- Implement continuous integration
- Use version control effectively
- Conduct regular code reviews
- Enhance change management processes
Metrics for Increase Fermentation Of Cacao
1. Fermentation Duration
This measures the time period for which cacao beans are fermented, typically calculated by the number of days the beans are kept at a controlled temperature and humidity.
What good looks like for this metric: 6 to 7 days
Ideas to improve this metric- Ensure an even distribution of heat during fermentation
- Monitor and control humidity levels diligently
- Turn the beans regularly to aerate
- Use a thermometer to achieve optimal temperature
- Adjust fermenting period based on observed results
2. Temperature Control
This measures the consistency of temperature maintained during cacao bean fermentation, which affects the flavour and quality of the final product.
What good looks like for this metric: 45°C to 50°C
Ideas to improve this metric- Install thermal insulators around the fermentation setup
- Use thermostatic controllers to maintain steady temperature
- Regularly check for hot spots inside the fermenting boxes
- Utilise temperature logs to detect anomalies
- Consider environmental impact on temperatures and adjust accordingly
3. pH Levels
Monitoring the acidity levels of fermenting beans helps in assessing proper fermentation, calculated by taking pH readings at intervals.
What good looks like for this metric: 4.5 to 5.5
Ideas to improve this metric- Use a reliable pH meter for accurate readings
- Sample beans from different sections of the fermentation mass
- Evaluate pH at regular intervals
- Adjust fermenting circumstances to reach desired pH
- Apply organic acids if necessary to modulate pH
4. Moisture Content
The proportion of water present in the fermenting beans, affecting final texture and processing requirements.
What good looks like for this metric: 53% to 60% during fermentation
Ideas to improve this metric- Weigh batch before and after fermentation to determine moisture loss
- Use moisture meters for precise measurements
- Adjust ventilation to control evaporation rate
- Add water incrementally if moisture drops too low
- Monitor climate conditions to understand moisture variation
5. Aeration Frequency
Frequency with which cacao beans are stirred or turned during fermentation to increase exposure to oxygen for consistent fermentation.
What good looks like for this metric: Every 48 hours
Ideas to improve this metric- Use mechanical turners for uniform aeration
- Implement a consistent aeration schedule
- Observe changes in aroma to gauge when turning is needed
- Document each aeration session for review
- Collaborate with fermented food experts
Metrics for Tracking Quality of Code
1. Code Coverage
Measures the percentage of your code that is covered by automated tests
What good looks like for this metric: 70%-90%
Ideas to improve this metric- Increase unit tests
- Use code coverage tools
- Refactor complex code
- Implement test-driven development
- Conduct code reviews frequently
2. Code Complexity
Assesses the complexity of the code using metrics like Cyclomatic Complexity
What good looks like for this metric: 1-10 (Lower is better)
Ideas to improve this metric- Simplify conditional statements
- Refactor to smaller functions
- Reduce nested loops
- Use design patterns appropriately
- Perform regular code reviews
3. Technical Debt
Measures the cost of additional work caused by choosing easy solutions now instead of better approaches
What good looks like for this metric: Less than 5%
Ideas to improve this metric- Refactor code regularly
- Avoid quick fixes
- Ensure high-quality code reviews
- Update and follow coding standards
- Use static code analysis tools
4. Defect Density
Calculates the number of defects per 1000 lines of code
What good looks like for this metric: Less than 1 defect/KLOC
Ideas to improve this metric- Implement thorough testing
- Increase peer code reviews
- Enhance developer training
- Use static analysis tools
- Adopt continuous integration
5. Code Churn
Measures the amount of code that is added, modified, or deleted over time
What good looks like for this metric: 10-20%
Ideas to improve this metric- Stabilise project requirements
- Improve initial code quality
- Adopt pair programming
- Reduce unnecessary refactoring
- Enhance documentation
Metrics for Improving workflows and safety
1. Infection Rate Reduction
The measure of reduction in infection cases reported in the facility after renovations
What good looks like for this metric: A typical benchmark is a 20% reduction in infection rates
Ideas to improve this metric- Conduct regular infection audits
- Ensure proper sanitisation of equipment
- Implement staff training on infection control
- Enhance air filtration systems
- Utilise antimicrobial surfaces
2. Patient Safety Incident Count
Number of safety-related incidents reported per 1,000 patient days
What good looks like for this metric: Aim for fewer than 10 incidents per 1,000 patient days
Ideas to improve this metric- Standardise safety protocols
- Improve staff communication channels
- Introduce safety drills and training
- Enhance surveillance systems
- Regularly update safety guidelines
3. Workflow Efficiency Percentage
Percentage of processes completed within the expected time frame
What good looks like for this metric: Achieving at least 85% on-time process completion
Ideas to improve this metric- Optimise staffing schedules
- Implement workflow management software
- Regularly review and adjust processes
- Conduct time management training
- Utilise feedback to streamline operations
4. Patient Satisfaction Scores
Patients' average satisfaction rating post-renovation
What good looks like for this metric: A target of at least 90% satisfaction
Ideas to improve this metric- Enhance waiting area conditions
- Provide clear communication about changes
- Solicit frequent patient feedback
- Ensure staff are attentive and responsive
- Provide patient education on safety improvements
5. Staff Compliance Rate with Protocols
Percentage of staff compliance with updated infection control protocols
What good looks like for this metric: Aim for at least 95% compliance
Ideas to improve this metric- Incentivise adherence to protocols
- Conduct regular staff assessments
- Provide ongoing training sessions
- Utilise visual reminders and aids
- Implement a peer review system
Metrics for Quality and Reliability
1. Defect Density
Measures the number of defects per unit size of the software, usually per thousand lines of code
What good looks like for this metric: 1-10 defects per KLOC
Ideas to improve this metric- Implement code reviews
- Increase automated testing
- Enhance developer training
- Use static code analysis tools
- Adopt Test-Driven Development (TDD)
2. Mean Time to Failure (MTTF)
Measures the average time between failures for a system or component during operation
What good looks like for this metric: Varies widely by industry and system type, generally higher is better
Ideas to improve this metric- Conduct regular maintenance routines
- Implement rigorous testing cycles
- Enhance monitoring and alerting systems
- Utilise redundancy and failover mechanisms
- Improve codebase documentation
3. Customer-Reported Incidents
Counts the number of issues or bugs reported by customers within a given period
What good looks like for this metric: Varies depending on product and customer base, generally lower is better
Ideas to improve this metric- Engage in proactive customer support
- Release regular updates and patches
- Conduct user feedback sessions
- Improve user documentation
- Monitor and analyse incident trends
4. Code Coverage
Indicates the percentage of the source code covered by automated tests
What good looks like for this metric: 70-90% code coverage
Ideas to improve this metric- Increase unit testing
- Use automated testing tools
- Adopt continuous integration practices
- Refactor legacy code
- Integrate end-to-end testing
5. Release Frequency
Measures how often new releases are deployed to production
What good looks like for this metric: Depends on product and development cycle; frequently updated software is often more reliable
Ideas to improve this metric- Adopt continuous delivery
- Automate deployment processes
- Improve release planning
- Reduce deployment complexity
- Engage in regular sprint retrospectives
Metrics for Backend Developer Performance
1. Code Quality
Measures the standards of the code written by the developer using metrics like cyclomatic complexity, code churn, and code maintainability index
What good looks like for this metric: Maintainability index above 70
Ideas to improve this metric- Conduct regular code reviews
- Utilise static code analysis tools
- Adopt coding standards and guidelines
- Refactor code regularly to reduce complexity
- Invest in continuous learning and training
2. Deployment Frequency
Evaluates the frequency at which a developer releases code changes to production
What good looks like for this metric: Multiple releases per week
Ideas to improve this metric- Automate deployment processes
- Use continuous integration and delivery pipelines
- Schedule regular release sessions
- Encourage modular code development
- Enhance collaboration with DevOps teams
3. Lead Time for Changes
Measures the time taken from code commit to deployment in production, reflecting efficiency in development and delivery
What good looks like for this metric: Less than one day
Ideas to improve this metric- Streamline the code review process
- Optimise testing procedures
- Improve communication across teams
- Automate build and testing workflows
- Implement parallel development tracks
4. Change Failure Rate
Represents the proportion of deployments that result in a failure requiring a rollback or hotfix
What good looks like for this metric: Less than 15%
Ideas to improve this metric- Implement thorough testing before deployment
- Decrease batch size of code changes
- Conduct post-implementation reviews
- Improve error monitoring and logging
- Enhance rollback procedures
5. System Downtime
Assesses the total time that applications are non-operational due to code changes or failures attributed to backend systems
What good looks like for this metric: Less than 0.1% downtime
Ideas to improve this metric- Invest in high availability infrastructure
- Enhance real-time monitoring systems
- Regularly test system resilience
- Implement effective incident response plans
- Improve software redundancy mechanisms
Metrics for Backend Developer Performance
1. Code Quality
Measures the frequency and severity of bugs detected in the codebase.
What good looks like for this metric: Less than 10 bugs per 1000 lines of code
Ideas to improve this metric- Implement regular code reviews
- Use static code analysis tools
- Provide training on best coding practices
- Encourage test-driven development
- Adopt a peer programming strategy
2. Deployment Frequency
Tracks how often code changes are successfully deployed to production.
What good looks like for this metric: Deploy at least once a day
Ideas to improve this metric- Automate the deployment pipeline
- Reduce bottlenecks in the process
- Regularly publish small, manageable changes
- Incentivise swift yet comprehensive testing
- Improve team communication and collaboration
3. Mean Time to Recovery (MTTR)
Measures the average time taken to recover from a service failure.
What good looks like for this metric: Less than 1 hour
Ideas to improve this metric- Develop a robust incident response plan
- Streamline rollback and recovery processes
- Use monitoring tools to detect issues early
- Conduct post-mortems and learn from failures
- Enhance system redundancy and fault tolerance
4. Test Coverage
Represents the percentage of code which is tested by automated tests.
What good looks like for this metric: 70% to 90%
Ideas to improve this metric- Implement continuous integration with testing
- Educate developers on writing effective tests
- Regularly update and refactor out-of-date tests
- Encourage a culture of writing tests
- Utilise behaviour-driven development techniques
5. API Response Time
Measures the time taken for an API to respond to a request.
What good looks like for this metric: Less than 200ms
Ideas to improve this metric- Optimize database queries
- Utilise caching effectively
- Reduce payload size
- Use load balancing techniques
- Profile and identify performance bottlenecks
Metrics for AI in Assignment Rubrics
1. Time Saved Creating Rubrics
The amount of time saved when using AI compared to traditional methods for creating assignment and grading rubrics
What good looks like for this metric: 20-30% time reduction
Ideas to improve this metric- Automate repetitive tasks
- Utilise AI suggestions for common criteria
- Implement AI feedback loops
- Train staff on AI tools
- Streamline rubric creation processes
2. Consistency of Grading
The uniformity in applying grading standards when using AI-generated rubrics across different assignments and graders
What good looks like for this metric: 90-95% consistency
Ideas to improve this metric- Use AI for grading calibration
- Standardise rubric templates
- Provide grader training sessions
- Incorporate peer reviews
- Regularly update rubrics
3. Accuracy of AI Suggestions
The correctness and relevance of AI-generated rubric elements compared to expert-generated criteria
What good looks like for this metric: 85-95% accuracy
Ideas to improve this metric- Customise AI settings
- Review AI outputs with experts
- Incorporate machine learning feedback
- Regularly update AI models
- Collect user feedback
4. User Satisfaction With Rubrics
The level of satisfaction among educators and students with AI-created rubrics in terms of clarity and usefulness
What good looks like for this metric: 70-80% satisfaction rate
Ideas to improve this metric- Conduct satisfaction surveys
- Gather and implement feedback
- Offer training on rubric interpretation
- Enhance user interface
- Continuously update rubric features
5. Overall Cost of Rubric Creation
Total expenses saved by using AI tools over traditional methods for creating and managing rubrics
What good looks like for this metric: 10-15% cost reduction
Ideas to improve this metric- Analyse cost-benefit regularly
- Leverage cloud-based AI solutions
- Negotiate better software licensing
- Train in-house AI experts
- Integrate AI with existing systems
Metrics for Evaluating Test Performance
1. Test Coverage
Measures the percentage of the codebase tested by automated tests, calculated as (number of lines or code paths tested / total lines or code paths) * 100
What good looks like for this metric: 70%-90% for well-tested code
Ideas to improve this metric- Increase automation in testing
- Refactor complex code to simplify testing
- Utilise test-driven development
- Regularly update and review test cases
- Incorporate pair programming
2. Defect Density
Calculates the number of confirmed defects divided by the size of the software entity being measured, typically measured as defects per thousand lines of code
What good looks like for this metric: Less than 1 bug per 1,000 lines
Ideas to improve this metric- Conduct thorough code reviews
- Implement static code analysis
- Improve developer training
- Use standard coding practices
- Perform regular software audits
3. Test Execution Time
The duration taken to execute all test cases, calculated by summing up the time taken for all tests
What good looks like for this metric: Shorter is better; aim for less than 30 minutes
Ideas to improve this metric- Optimise test scripts
- Use parallel testing
- Remove redundant tests
- Upgrade testing tools or infrastructure
- Automate test environment setup
4. Code Churn Rate
Measures the amount of code change within a given period, calculated as the number of lines of code added, modified, or deleted
What good looks like for this metric: 5%-10% considered manageable
Ideas to improve this metric- Emphasise on quality over quantity in changes
- Increase peer code reviews
- Ensure clear and precise project scopes
- Monitor team workload to avoid burnout
- Provide comprehensive documentation
5. User Reported Defects
Counts the number of defects reported by users post-release, provides insights into the software's real-world performance
What good looks like for this metric: Strive for zero, but less than 5% of total defects
Ideas to improve this metric- Enhance pre-release testing
- Gather detailed user feedback
- Offer user training and resources
- Implement beta testing
- Regularly update with patches and fixes
Metrics for Feature Completeness
1. Feature Completion Rate
The percentage of features fully implemented and functional compared to the initial plan
What good looks like for this metric: 80% to 100% during development cycle
Ideas to improve this metric- Improve project management processes
- Ensure clear feature specifications
- Allocate adequate resources
- Conduct regular progress reviews
- Increase team collaboration
2. Planned vs. Actual Features
The ratio of features planned to features actually completed
What good looks like for this metric: Equal or close to 1:1
Ideas to improve this metric- Create realistic project plans
- Regularly update feature lists
- Adjust deadlines as needed
- Align teams on priorities
- Open channels for feedback
3. Feature Review Score
Average score from review sessions that evaluate feature completion and quality
What good looks like for this metric: Scores above 8 out of 10
Ideas to improve this metric- Provide detailed review criteria
- Use peer review strategies
- Incorporate customer feedback
- Holistic testing methodologies
- Re-evaluate low scoring features
4. Feature Dependency Resolution Time
Average time taken to resolve issues linked to feature dependencies
What good looks like for this metric: Resolution time within 2 weeks
Ideas to improve this metric- Map feature dependencies early
- Optimize dependency workflow
- Increase team communication
- Utilise dependency management tools
- Prioritize complex dependencies
5. Change Request Frequency
Number of changes requested post-initial feature specification
What good looks like for this metric: Less than 10% of total features
Ideas to improve this metric- Ensure initial feature clarity
- Involve stakeholders early on
- Implement change control processes
- Clarify project scope
- Encourage proactive team discussions
Metrics for Frontend Development Skill Assessment
1. Code Quality
Assesses the readability, structure, and efficiency of the written code in HTML, CSS, and JavaScript
What good looks like for this metric: Clean, well-commented code with no linting errors
Ideas to improve this metric- Utilise code linters and formatters
- Adopt a consistent coding style
- Refactor code regularly
- Practise writing clear comments
- Review code with peers
2. Page Load Time
Measures the time it takes for a webpage to fully load in a browser
What good looks like for this metric: Less than 3 seconds
Ideas to improve this metric- Minimise HTTP requests
- Optimise image sizes
- Use CSS and JS minification
- Leverage browser caching
- Use content delivery networks
3. Responsive Design
Evaluates how well a website adapts to different screen sizes and devices
What good looks like for this metric: Seamless functionality across all devices
Ideas to improve this metric- Use relative units like percentages
- Implement CSS media queries
- Test designs on multiple devices
- Adopt a mobile-first approach
- Utilise frameworks like Bootstrap
4. Cross-browser Compatibility
Ensures a website functions correctly across different web browsers
What good looks like for this metric: Consistent experience on all major browsers
Ideas to improve this metric- Test site on all major browsers
- Use browser-specific prefixes
- Avoid deprecated features
- Employ browser compatibility tools
- Regularly update code for latest standards
5. User Experience (UX)
Measures how user-friendly and intuitive the interface is for users
What good looks like for this metric: High user satisfaction and easy navigation
Ideas to improve this metric- Simplify navigation structures
- Ensure consistent design patterns
- Conduct user testing regularly
- Gather and implement user feedback
- Improve the accessibility of designs
Metrics for History and Physical Completion
1. Completion Rate
The percentage of history and physical exams completed within 24 hours of patient admission
What good looks like for this metric: 90-95%
Ideas to improve this metric- Implement a reminder system for staff
- Introduce electronic health record alerts
- Provide training for efficient documentation
- Increase staffing during peak admission times
- Encourage early initiation of assessments
2. Average Completion Time
The average time taken to complete history and physical exams after patient admission
What good looks like for this metric: Under 24 hours
Ideas to improve this metric- Streamline documentation processes
- Utilise checklists for thoroughness and speed
- Cross-train staff for flexibility
- Optimise patient flow and prioritisation
- Regularly review and address bottlenecks
3. Staff Compliance Rate
The percentage of staff adhering to the completion protocol for history and physical exams
What good looks like for this metric: Above 90%
Ideas to improve this metric- Conduct routine compliance audits
- Offer incentives for high compliance
- Provide feedback on performance
- Strengthen compliance policies
- Ensure clarity in protocols and guidelines
4. Patient Outcome Correlation
The link between timely completion of history and physical exams and patient outcomes
What good looks like for this metric: Positive correlation
Ideas to improve this metric- Analyse correlations with patient recovery times
- Adjust practices based on outcome data
- Focus on accuracy and completeness in assessments
- Regularly reassess assessment procedures
- Align protocols with best practices
5. Error Rate in Documentation
The frequency of errors found in the completed history and physical exams
What good looks like for this metric: Below 5%
Ideas to improve this metric- Enhance staff training on documentation
- Introduce a peer review process
- Use electronic templates to minimise errors
- Implement regular quality checks
- Increase awareness of common documentation errors
Metrics for Software Feature Completeness
1. Feature Implementation Ratio
The ratio of implemented features to planned features.
What good looks like for this metric: 80-90%
Ideas to improve this metric- Prioritise features based on user impact
- Allocate dedicated resources for feature development
- Conduct regular progress reviews
- Utilise agile methodologies for iteration
- Ensure clear feature specifications
2. User Acceptance Test Pass Rate
Percentage of features passing user acceptance testing.
What good looks like for this metric: 95%+
Ideas to improve this metric- Enhance test case design
- Involve users early in the testing process
- Provide comprehensive user training
- Utilise automated testing tools
- Identify and fix defects promptly
3. Bug Resolution Time
Average time taken to resolve bugs during feature development.
What good looks like for this metric: 24-48 hours
Ideas to improve this metric- Implement a robust issue tracking system
- Prioritise critical bugs
- Conduct regular team stand-ups
- Improve cross-functional collaboration
- Establish a swift feedback loop
4. Code Quality Index
Assessment of code quality using a standard index or score.
What good looks like for this metric: 75-85%
Ideas to improve this metric- Conduct regular code reviews
- Utilise static code analysis tools
- Refactor code periodically
- Strictly adhere to coding standards
- Invest in developer training
5. Feature Usage Frequency
Frequency at which newly implemented features are used.
What good looks like for this metric: 70%+ usage of released features
Ideas to improve this metric- Enhance user interface design
- Provide user guides or tutorials
- Gather user feedback on new features
- Offer feature usage incentives
- Regularly monitor usage statistics
Metrics for Effective Delivery for Waterfall Team
1. Planned vs Actual Delivery Dates
This metric compares the initially planned delivery dates to the actual delivery dates to assess the team's ability to meet deadlines
What good looks like for this metric: 80% of projects delivered on time
Ideas to improve this metric- Conduct detailed planning sessions
- Implement regular progress reviews
- Improve risk management practices
- Enhance communication within the team
- Optimise resource allocation
2. Scope Creep
Measures the changes and additions in the project scope after the project has commenced, indicating how often the team deviates from the original plan
What good looks like for this metric: Less than 5% increase in scope
Ideas to improve this metric- Establish clear project requirements
- Implement strict change control processes
- Engage stakeholders early and often
- Document all changes meticulously
- Maintain a project scope baseline
3. Budget Variance
This metric tracks the difference between the budgeted costs and the actual costs incurred, indicating financial planning accuracy
What good looks like for this metric: Less than 10% budget overrun
Ideas to improve this metric- Conduct thorough budget forecasting
- Monitor expenditures closely
- Implement cost control measures
- Review financial reports regularly
- Optimise purchasing processes
4. Defect Density
Measures the number of defects identified within a certain timeframe or phase of the project, reflecting product quality
What good looks like for this metric: Fewer than 1 defect per 1000 lines of code
Ideas to improve this metric- Enhance testing processes
- Implement automated testing tools
- Provide training on quality standards
- Review code regularly
- Incorporate quality assurance in each phase
5. Customer Satisfaction
Assesses the stakeholders' and customers' satisfaction with the delivered project through surveys and feedback mechanisms
What good looks like for this metric: Customer satisfaction score above 8 out of 10
Ideas to improve this metric- Gather customer feedback regularly
- Act on the feedback received
- Improve stakeholder communication
- Deliver regular project updates
- Ensure project deliverables meet expectations
Tracking your Quality Assurance metrics
Having a plan is one thing, sticking to it is another.
Setting good strategies is only the first challenge. The hard part is to avoid distractions and make sure that you commit to the plan. A simple weekly ritual will greatly increase the chances of success.
A tool like Tability can also help you by combining AI and goal-setting to keep you on track.
Tability's check-ins will save you hours and increase transparencyMore metrics recently published
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Planning resources
OKRs are a great way to translate strategies into measurable goals. Here are a list of resources to help you adopt the OKR framework:
- To learn: What are OKRs? The complete 2024 guide
- Blog posts: ODT Blog
- Success metrics: KPIs examples