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 Support Role Performance
1. Customer Satisfaction (CSAT)
Measures customer satisfaction with the service provided, usually through post-interaction surveys
What good looks like for this metric: 75% to 85% satisfaction rate
Ideas to improve this metric- Provide personalised responses
- Ensure timely follow-ups
- Offer multi-channel support options
- Use clear and concise language
- Continuously train staff on customer service
2. Quality Assurance (QA) Score
Evaluates the quality of support interactions based on predefined criteria
What good looks like for this metric: 85% to 95% QA score
Ideas to improve this metric- Regularly update QA criteria
- Conduct team workshops
- Implement a feedback loop for staff
- Use role-playing scenarios
- Review and analyse call recordings
3. Average Handle Time (AHT)
Tracks the average duration of handling a customer request from start to finish
What good looks like for this metric: 4 to 8 minutes
Ideas to improve this metric- Implement time-saving tools
- Provide comprehensive training
- Streamline internal processes
- Utilise call scripts effectively
- Regularly review AHT goals
4. Tickets Handled Per Hour
Measures the number of tickets resolved by a support agent in an hour
What good looks like for this metric: 8 to 12 tickets per hour
Ideas to improve this metric- Use ticket management systems
- Prioritise tasks effectively
- Encourage team collaboration
- Automate repetitive tasks
- Regularly assess ticket assignment
5. First Call Resolution (FCR)
Indicates the percentage of calls resolved without the need for follow-up
What good looks like for this metric: 70% to 85% FCR rate
Ideas to improve this metric- Provide extensive product training
- Empower agents to make decisions
- Enhance access to information
- Use customer feedback effectively
- Address common issues promptly
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 Customer Application Support
1. First Response Time
The average time taken to respond to a customer's initial request for support
What good looks like for this metric: Less than 60 minutes
Ideas to improve this metric- Implement automated response systems
- Prioritise tickets based on urgency
- Train staff on efficient support processes
- Increase staffing during peak times
- Regularly review and adjust response protocols
2. Customer Satisfaction Score (CSAT)
A metric that measures how satisfied customers are with support service provided
What good looks like for this metric: Above 80%
Ideas to improve this metric- Conduct regular feedback surveys
- Provide comprehensive training for support staff
- Implement a robust Quality Assurance process
- Ensure clear communication with customers
- Continuously optimize support processes
3. Resolution Rate
The percentage of customer issues that are resolved upon first contact
What good looks like for this metric: Above 70%
Ideas to improve this metric- Implement a comprehensive knowledge base
- Allow agents access to customer history
- Offer advanced training sessions
- Utilise AI tools for troubleshooting
- Encourage proactive problem resolution
4. Average Resolution Time
The average amount of time taken to resolve a customer's issue
What good looks like for this metric: 2-4 hours
Ideas to improve this metric- Ensure efficient ticket triaging
- Provide agents with advanced tools
- Identify and eliminate bottlenecks
- Streamline communication channels
- Regularly review unresolved cases
5. Net Promoter Score (NPS)
A measure of customer loyalty and how likely they are to recommend the service
What good looks like for this metric: Above 50
Ideas to improve this metric- Focus on enhancing customer experience
- Identify and support brand advocates
- Deliver consistent and reliable service
- Engage in active listening and feedback
- Implement loyalty programmes
Metrics for Assessing Data Quality Maturity
1. Percentage of Basic Data Quality Checks Implemented
Measures the proportion of datasets with basic data quality checks applied
What good looks like for this metric: 80% or higher
Ideas to improve this metric- Prioritise the implementation of basic checks on all datasets
- Provide training for team members on basics of data quality
- Allocate resources for implementing basic checks
- Automate basic data quality checks to ensure consistency
- Regularly review and update checklists for basic checks
2. Percentage of Advanced Data Quality Checks Implemented
Measures the proportion of datasets with advanced data quality checks applied
What good looks like for this metric: 60% or higher
Ideas to improve this metric- Identify datasets requiring advanced checks
- Develop a strategic plan for advanced data quality implementations
- Seek external expertise for complex checks
- Increase budget for advanced data quality tools
- Regularly review advanced check requirements
3. Month-Over-Month Improvement in Data Quality Maturity
Tracks the percentage change or improvement in the implementation of data quality checks month-over-month
What good looks like for this metric: 5% increase
Ideas to improve this metric- Set monthly targets to improve data quality metrics
- Analyse bottlenecks from previous months and address them
- Ensure consistent reporting and monitoring of progress
- Incorporate regular feedback loops from data teams
- Recognise and reward teams exceeding targets
4. Data Quality Issue Resolution Time
Measures the average time taken to resolve data quality issues
What good looks like for this metric: Less than 48 hours
Ideas to improve this metric- Streamline issue reporting processes
- Establish clear guidelines for issue prioritisation
- Provide tools and training for faster issue resolution
- Monitor and analyse common issue types
- Implement a rapid response team for data quality issues
5. User Feedback on Data Quality
Collects user feedback regarding the perceived quality and reliability of data
What good looks like for this metric: 80% user satisfaction
Ideas to improve this metric- Conduct regular surveys to gather user feedback
- Engage with users for detailed feedback sessions
- Communicate improvements to users regularly
- Set up feedback loop in data systems
- Address user concerns and demonstrate improvements
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 Process Improvement
1. Cycle Time
The total time from the beginning to the end of your process, as defined by you and your customer.
What good looks like for this metric: Varies widely by industry
Ideas to improve this metric- Identify and eliminate bottlenecks
- Automate repetitive tasks
- Streamline workflow with software
- Implement continuous feedback loop
- Enhance staff training and skills
2. First Pass Yield
The percentage of products that are manufactured correctly and to specifications the first time without any rework.
What good looks like for this metric: 80-95%
Ideas to improve this metric- Improve employee training programs
- Regularly maintain and calibrate equipment
- Implement quality checks at every stage
- Analyse defect patterns
- Develop clear manufacturing protocols
3. Cost Per Unit
The total expenditure to produce one item or serve one customer, including both fixed and variable costs.
What good looks like for this metric: Industry-specific, varies
Ideas to improve this metric- Negotiate better rates with suppliers
- Optimise production scheduling
- Reduce material waste
- Adopt energy-saving practices
- Invest in more efficient technology
4. Process Efficiency
The percentage of time spent producing value-added work versus non-value-added work.
What good looks like for this metric: 60-80%
Ideas to improve this metric- Use lean methodology
- Map and analyse the process steps
- Reduce waiting times between process steps
- Simplify complex processes
- Train employees in efficiency techniques
5. Customer Satisfaction Rate
Measures how satisfied customers are with a company's products, services, and capabilities.
What good looks like for this metric: 70-80%
Ideas to improve this metric- Solicit regular customer feedback
- Improve customer-facing processes
- Accelerate response time to queries
- Enhance product or service quality
- Personalise customer interactions
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 Development Efficiency
1. Lead Time
The amount of time it takes from commit to production release
What good looks like for this metric: 2 to 4 weeks
Ideas to improve this metric- Implement continuous integration and delivery
- Optimise code review processes
- Automate testing procedures
- Reduce work-in-progress limits
- Regularly review and improve deployment pipelines
2. Deployment Frequency
How often the team deploys new changes to production
What good looks like for this metric: Once per day or more
Ideas to improve this metric- Adopt trunk-based development
- Automate deployment processes
- Encourage small, frequent updates
- Reduce feature development cycle time
- Establish a standard release schedule
3. Change Failure Rate
The percentage of changes that result in a failure in production
What good looks like for this metric: 0 to 15 percent
Ideas to improve this metric- Enhance testing procedures
- Conduct thorough post-deployment reviews
- Use feature flags for new releases
- Implement rollback strategies
- Invest in robust monitoring tools
4. Mean Time to Recover
The average time taken to recover from a failure in production
What good looks like for this metric: Less than 1 day
Ideas to improve this metric- Enhance incident response protocols
- Conduct root cause analysis
- Implement automated recovery scripts
- Provide regular training for incident recovery
- Utilise canary releases to minimize impact
5. Code Quality
The quality of code determined by metrics such as cyclomatic complexity, duplication, and adherence to coding standards
What good looks like for this metric: A high percentage of code meeting set standards
Ideas to improve this metric- Conduct regular code reviews
- Use static code analysis tools
- Adhere to coding standards
- Refactor code regularly
- Encourage pair programming
Metrics for Backend Code Quality
1. Code Review Rounds
Number of review rounds required before code approval
What good looks like for this metric: 1-2 rounds
Ideas to improve this metric- Organise code review workshops
- Implement a coding standards guide
- Assign senior developers as mentors
- Use static code analysis tools
- Establish a consistent review checklist
2. Code Coverage
Percentage of code covered by automated tests
What good looks like for this metric: Above 80%
Ideas to improve this metric- Increase unit and integration tests
- Regularly update test cases
- Utilise code coverage tools
- Prioritise critical code paths
- Automate test execution
3. Defect Density
Number of defects per thousand lines of code
What good looks like for this metric: Less than 1 defect per KLOC
Ideas to improve this metric- Conduct regular code audits
- Adopt pair programming
- Implement a bug triage system
- Encourage post-deployment analysis
- Provide regular feedback to developers
4. Number of Code Commits
Frequency of commits made by developers
What good looks like for this metric: Multiple small commits per day
Ideas to improve this metric- Encourage daily code submissions
- Streamline the commit process
- Divide tasks into smaller units
- Utilise version control best practices
- Promote a collaborative environment
5. Code Complexity
Measurement of code's structural complexity
What good looks like for this metric: Cyclomatic complexity less than 10
Ideas to improve this metric- Refactor overly complex methods
- Adopt design patterns
- Review complexity scores regularly
- Simplify code logic
- Use tools to measure complexity
Metrics for Software Engineering Research
1. Defect Density
Defect density measures the number of defects confirmed in the software during a specific period of development divided by the size of the software.
What good looks like for this metric: Less than 1 defect per 1,000 lines of code
Ideas to improve this metric- Implement peer code reviews
- Conduct regular testing phases
- Adopt test-driven development
- Use static code analysis tools
- Enhance developer training programmes
2. Code Coverage
Code coverage is the percentage of your code which is tested by automated tests.
What good looks like for this metric: 80% - 90%
Ideas to improve this metric- Review untested code sections
- Invest in automated testing tools
- Aim for high test case quality
- Integrate continuous integration practices
- Regularly refactor and simplify code
3. Cycle Time
Cycle time measures the time from when work begins on a feature until it's released to production.
What good looks like for this metric: 1 - 5 days
Ideas to improve this metric- Streamline build processes
- Improve collaboration tools
- Enhance team communication rituals
- Limit work in progress (WIP)
- Automate repetitive tasks
4. Technical Debt
Technical debt represents the implied cost of future rework caused by choosing an easy solution now instead of a better approach.
What good looks like for this metric: Under 5% of total project cost
Ideas to improve this metric- Regularly refactor existing code
- Set priority levels for debt reduction
- Maintain comprehensive documentation
- Conduct technical debt assessments
- Encourage practices to avoid accumulating debt
5. Customer Satisfaction
Customer satisfaction measures the level of contentment clients feel with the software, often gauged through surveys.
What good looks like for this metric: Above 80% satisfaction rate
Ideas to improve this metric- Gather feedback through surveys
- Implement a user-centric design approach
- Enhance customer support services
- Ensure frequent updates and improvements
- Analyse and respond to customer complaints
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 Halal Accreditation Compliance
1. Non-Conformance Rate
The frequency of non-conformances in Halal standards detected during audits or inspections
What good looks like for this metric: Less than 10 incidents annually
Ideas to improve this metric- Implement regular internal audits
- Provide comprehensive staff training
- Use checklists for routine checks
- Engage external experts for consultation
- Establish a corrective action plan
2. Audit Pass Rate
The percentage of successfully passed Halal compliance audits out of total audits conducted
What good looks like for this metric: Above 90%
Ideas to improve this metric- Conduct mock audits
- Ensure up-to-date documentation
- Set clear compliance instructions
- Regular management reviews
- Invest in audit management software
3. Supplier Halal Compliance Rate
The percentage of suppliers that are compliant with Halal standards out of the total suppliers
What good looks like for this metric: Above 95%
Ideas to improve this metric- Establish strict supplier guidelines
- Require supplier certification
- Conduct supplier audits/reviews
- Communicate regularly with suppliers
- Establish supplier improvement programs
4. Corrective Action Implementation Time
The average time taken to implement corrective actions after a non-conformance is identified
What good looks like for this metric: Within 30 days
Ideas to improve this metric- Prioritise action steps
- Assign dedicated resources
- Monitor progress weekly
- Automate reminders and follow-ups
- Create specific action deadlines
5. Training Completion Rate
The percentage of employees that have completed Halal compliance training out of total employees
What good looks like for this metric: 100%
Ideas to improve this metric- Schedule mandatory training sessions
- Offer e-learning modules
- Incentivise training completion
- Track training progress
- Regularly update training content
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
We have more examples to help you below.
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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