Engineering Metrics That Matter to Your Board [Unlock Growth Secrets!]

Critical Engineering Metrics for Board-Level Decision-Making

The most successful engineering organizations focus on metrics that directly connect technical performance to revenue growth and strategic objectives. These organizations evaluate metric relevance quarterly to ensure alignment with evolving business priorities.

Selecting Metrics That Impact Business Outcomes

Board members need metrics that translate engineering work into business language. Engineering metrics should show how technical performance supports business goals rather than focusing on internal processes.

Revenue-Connected Metrics:

• Customer acquisition cost reduction through automated systems
• Revenue per engineering dollar invested
• Time-to-market for revenue-generating features
• System uptime impact on customer retention

Key Selection Criteria:

Criteria	Good Metric	Poor Metric
Business Impact	Feature delivery speed	Lines of code written
Financial Connection	Customer churn from bugs	Number of commits
Strategic Relevance	Platform scalability costs	Code coverage percentage

Engineering effectiveness becomes measurable when metrics connect to customer outcomes. Teams that track deployment frequency alongside customer satisfaction scores provide boards with actionable insights.

The most valuable metrics answer specific business questions. Does the engineering team support growth targets? Are technology investments reducing operational costs? Can the platform handle projected customer growth?

Aligning Engineering KPIs With Strategic Goals

Engineering KPIs must align with broader business objectives to maintain board confidence and resource allocation. This alignment requires mapping technical capabilities to market opportunities.

Strategic Alignment Framework:

Growth-Stage Companies:

• Developer velocity for faster feature delivery
• Infrastructure cost per customer
• Security incident response time

Mature Organizations:

• Technical debt reduction rate
• Legacy system modernization progress
• Engineering productivity per dollar spent

Teams should benchmark their performance against similar companies in their industry and growth stage. Comparing engineering metrics to industry benchmarks provides boards with context for decision-making.

The most effective engineering leaders present metrics that show progress toward specific business outcomes. Revenue growth requires different engineering focus than cost optimization or market expansion.

Engineering KPIs become strategic tools when they predict business performance. Teams that can demonstrate how technical improvements will impact next quarter's revenue gain stronger board support.

Evaluating Metrics Relevance Over Time

Successful engineering organizations review metric relevance quarterly as business priorities shift. Metrics that matter change as companies scale from startup to enterprise.

Lifecycle-Based Metric Evolution:

Early Stage (0-50 engineers):

• Feature delivery speed
• Product-market fit indicators
• Customer problem resolution time

Growth Stage (50-200 engineers):

• Team productivity scaling
• Infrastructure efficiency gains
• Quality metrics impact on growth

Mature Stage (200+ engineers):

• Engineering effectiveness across divisions
• Technical innovation pipeline
• Competitive advantage through technology

Companies should retire metrics that no longer drive business decisions. Tracking code coverage becomes less valuable than monitoring customer satisfaction trends in mature organizations.

The most valuable metrics adapt to changing business contexts. Engineering teams supporting international expansion need different KPIs than those focused on cost reduction or product diversification. For more on setting goals, see our guide on Engineering OKRs.

Regular metric reviews prevent dashboard bloat and maintain board attention on critical performance indicators. Teams that can explain why specific metrics matter today build stronger executive relationships.

Essential Software Engineering Metrics

Elite engineering organizations measure delivery velocity, system reliability, and deployment success to demonstrate technical execution to board members. These metrics translate development team performance into business language that executives understand.

Cycle Time and Lead Time for Changes

Cycle time measures how long work takes from start to completion within your development process. Lead time for changes tracks the full journey from code commit to production deployment.

Elite teams achieve lead time for changes of less than one day, while low-performing teams require one to six months. This difference directly impacts your ability to respond to market opportunities and customer needs.

Your board cares about these metrics because they indicate organizational agility. Short cycle times mean faster feature delivery and quicker responses to competitive threats.

Key benchmarks:

• Elite: < 1 day lead time
• High: 1 day to 1 week
• Medium: 1 week to 1 month
• Low: 1-6 months

Track both metrics together to identify bottlenecks in your development pipeline. Long lead times often signal problems in code review, testing, or deployment processes that require infrastructure investment.

Change Failure Rate and Mean Time to Recovery

Change failure rate measures the percentage of deployments that cause production issues requiring fixes or rollbacks. Mean time to recovery tracks how quickly your team restores service after failures.

Elite teams maintain a 5% failure rate while low performers reach 40%. Interestingly, high-performing teams often have higher failure rates than medium performers because they deploy more frequently and take calculated risks.

Elite teams recover from failures in under one hour, while low performers need weeks. This recovery speed directly impacts customer satisfaction and revenue protection.

Your board needs to understand the relationship between innovation velocity and system stability. Teams that deploy frequently but recover quickly often outperform those that deploy rarely but face long recovery times.

Recovery time benchmarks:

• Elite: < 1 hour
• High: < 1 day
• Medium: 1 day to 1 week
• Low: 1 week to 1 month

Deployment Frequency and Throughput

Deployment frequency shows how often your team ships code to production. Throughput measures the volume of work completed within specific time periods.

Top-performing teams deploy multiple times per day, while struggling teams deploy monthly or quarterly. Frequent deployments reduce risk by making changes smaller and easier to troubleshoot.

Throughput provides context for deployment frequency. A team deploying daily with low throughput may indicate over-engineering or excessive process overhead.

These metrics demonstrate your engineering organization's maturity to board members. High deployment frequency with steady throughput signals advanced DevOps practices and automated testing capabilities.

Deployment frequency tiers:

• Elite: Multiple times per day
• High: Once per day to once per week
• Medium: Once per week to once per month
• Low: Once per month to once every six months

Monitor these metrics together to balance speed with quality. Teams optimizing only for deployment frequency may sacrifice thoughtful feature development.

Measuring Quality and Productivity in Engineering

Engineering productivity measurement requires balancing defect tracking with team velocity indicators. Bug counts provide direct quality insights, while code coverage and pull request metrics reveal development practices that drive long-term maintainability.

Tracking Number of Bugs and Defects

Bug tracking serves as the most direct indicator of engineering quality that boards can understand. Engineering teams should focus on customer-affecting incident rates rather than internal bug counts alone.

Critical bug metrics include:

• Production incidents per release
• Time to resolution for critical issues
• Defect escape rate from testing to production
• Reopened bug percentage

The number of bugs tells only part of the story. Engineering leaders must contextualize these numbers by severity and customer impact.

A team shipping 50 minor UI bugs carries different business risk than one releasing 5 data corruption issues. Boards need this distinction clearly communicated.

Trending analysis matters more than absolute numbers. Teams with improving bug trends demonstrate maturing development processes. Those with flat or worsening trends signal technical debt accumulation or process breakdowns.

Code Coverage and Comments per Pull Request

Code coverage percentage provides a quantifiable measure of testing discipline. Teams maintaining 80-90% coverage typically demonstrate stronger quality practices than those below 70%.

However, coverage alone doesn't guarantee quality. Teams can achieve high coverage with poor test design. Engineering leaders should pair coverage metrics with mutation testing results when possible.

Comments per pull request indicate code review thoroughness and knowledge transfer effectiveness. Teams averaging 3-5 meaningful comments per PR show healthy collaboration patterns.

Low comment counts may signal rubber-stamp approvals or insufficient peer review. Excessive comments often indicate unclear requirements or architectural disagreements.

Metric	Good Range	Warning Signs
Code Coverage	80-90%	Below 70% or above 95%
PR Comments	3-5 per PR	Under 2 or over 10
Review Time	4-24 hours	Same day or over 48 hours

Developer Delta and Team Productivity

Developer delta measures individual contributor output changes over time. This metric helps identify productivity trends and capacity planning needs for technical executives managing engineering budgets.

Measuring engineering productivity requires tracking value stream metrics from commit to customer delivery. Simple velocity measurements miss critical bottlenecks in the development pipeline.

Key productivity indicators:

• Story points completed per sprint
• Code commits per developer per week
• Features delivered per engineering full-time equivalent
• Time from feature complete to production deployment

Team productivity extends beyond individual metrics. Cross-training levels, knowledge distribution, and collaboration patterns predict sustainable delivery capacity.

Teams with high individual productivity but poor knowledge sharing create organizational risk. Single points of failure emerge when key developers hold critical system knowledge.

Productivity measurement must account for technical debt work. Teams spending 40% of capacity on maintenance and refactoring may show lower feature velocity but higher long-term sustainability.

Lean, Manufacturing, and Operational Metrics

Manufacturing and operational metrics provide boards with critical visibility into production efficiency, resource utilization, and operational performance. These metrics directly impact revenue generation, cost control, and competitive positioning in manufacturing-intensive organizations.

Throughput and Production Attainment

Throughput measures the total units produced within a specific time period. Production attainment compares actual output against planned production targets.

Throughput = Units Produced ÷ Time Period

Engineering leaders track throughput across multiple dimensions. Daily throughput identifies immediate production issues. Weekly and monthly throughput reveals capacity trends and seasonal patterns.

Production attainment percentages above 95% indicate strong operational control. Rates below 85% signal systemic problems requiring immediate attention.

Lean manufacturing metrics help organizations maximize productivity while minimizing waste. Manufacturing executives use these indicators to optimize resource allocation and identify bottlenecks.

Key throughput factors include:

• Machine availability: Equipment uptime during scheduled production
• Labor efficiency: Worker productivity against established standards
• Material flow: Raw material availability and logistics timing
• Quality gates: Inspection and approval processes

First Pass Yield and Capacity Utilization

First pass yield measures the percentage of products manufactured correctly without rework or corrections. Capacity utilization tracks how much available production capacity gets used.

First Pass Yield = Quality Units ÷ Total Units Produced

High first pass yield rates reduce waste, lower costs, and improve customer satisfaction. World-class manufacturers achieve first pass yield rates above 99%.

Capacity utilization rates between 80-85% provide optimal balance. Higher rates risk equipment failures and quality issues. Lower rates indicate underutilized assets and higher per-unit costs.

Manufacturing KPIs enable data-driven decisions that strengthen operations and improve performance metrics.

Capacity utilization considerations:

• Scheduled maintenance windows: Planned downtime requirements
• Demand variability: Market fluctuations and seasonal patterns
• Flexibility needs: Ability to respond to urgent orders
• Quality requirements: Time needed for proper production processes

Cycle Time for Manufacturing and Changeover Time

Cycle time represents the total time required to complete one production cycle. Changeover time measures duration needed to switch production between different products or configurations.

Manufacturing cycle time includes all steps from raw materials to finished goods. Reducing cycle time improves cash flow and customer responsiveness.

Changeover time directly impacts production flexibility and efficiency. Quick changeovers enable smaller batch sizes and faster market response.

Cycle Time = Process End Time - Process Start Time

Lean manufacturing KPIs focus on eliminating waste and improving process efficiency across all production activities.

Typical changeover reduction targets:

• Single-digit minutes: World-class changeover performance
• External setup: Preparation work performed while machine runs
• Standardized procedures: Consistent changeover processes
• Quick-connect tooling: Faster equipment configuration changes

Machine Downtime Rate and Planned Maintenance

Machine downtime rate tracks the percentage of time equipment remains non-productive. Planned maintenance measures scheduled maintenance as a percentage of total maintenance activities.

Machine Downtime = Downtime Hours ÷ (Downtime Hours + Operational Hours)

Unplanned downtime costs manufacturers up to $50 billion annually according to industry research. Effective maintenance strategies significantly reduce these losses.

Planned maintenance percentages above 80% indicate proactive maintenance programs. Lower percentages suggest reactive maintenance approaches that increase costs and risks.

Manufacturing metrics tracking helps manufacturers take data-driven actions to strengthen their operations and reduce unexpected failures.

Maintenance optimization strategies:

• Predictive analytics: Sensor data predicting equipment failures
• Preventive scheduling: Regular maintenance based on usage patterns
• Condition monitoring: Real-time equipment health assessment
• Spare parts inventory: Strategic component availability planning

Project Delivery and Financial Performance Metrics

Board members focus on two critical questions: are engineering projects delivering on time and within budget, and what financial returns are they generating? These metrics translate engineering execution into business language that executives understand and use for strategic decisions.

On-Time Delivery and Outsourcing Rate

Engineering-on-time delivery measures the percentage of engineering tasks completed by their planned deadlines. This metric directly impacts revenue recognition, customer satisfaction, and market positioning.

Most engineering organizations struggle with delivery predictability. Teams that achieve 80% or higher on-time delivery rates typically implement rigorous sprint planning and risk identification processes.

Key tracking elements:

• Milestone completion dates vs. original estimates
• Scope change frequency and impact on timelines
• Resource allocation accuracy across projects

Outsourcing rate shows what percentage of engineering work goes to external vendors versus internal teams. This metric helps boards evaluate cost structure and capability gaps.

High outsourcing rates may indicate internal skill shortages or cost optimization strategies. However, excessive outsourcing can create knowledge transfer risks and reduce long-term technical capabilities.

Project Margin and Cost Performance Indicator

Project margin reveals the profitability of engineering initiatives by comparing project revenue to total project costs. This metric helps boards understand which types of engineering work generate the highest returns.

Engineering leaders should track both gross margin (direct costs only) and net margin (including overhead allocation). Projects with margins below 20% often indicate pricing problems or scope creep issues.

The Cost Performance Indicator (CPI) compares earned value to actual costs spent. CPI = Earned Value ÷ Actual Cost. A CPI above 1.0 means the project runs under budget, while below 1.0 indicates cost overruns.

CPI trends matter more than single measurements. Consistent CPI values between 0.95 and 1.05 suggest strong cost estimation and control processes.

Schedule Performance Indicator and Break-Even Point

The Schedule Performance Indicator (SPI) measures project progress against planned timelines. SPI = Earned Value ÷ Planned Value. Values above 1.0 indicate ahead-of-schedule performance.

SPI becomes critical during quarterly board reviews. Projects with SPI below 0.8 rarely recover without significant intervention or scope reduction.

Combined SPI and CPI analysis reveals project health:

• High SPI, High CPI: Excellent execution
• Low SPI, High CPI: Schedule risks with good cost control
• High SPI, Low CPI: Fast delivery but cost overruns

Break-even point (BEP) shows when engineering investments start generating positive returns. BEP = Initial Investment ÷ Annual Cash Inflows.

For product engineering teams, BEP typically ranges from 12 to 36 months depending on market dynamics and development complexity. Shorter BEP periods indicate stronger market validation and execution efficiency.

Client Growth and Consulting Engineering Metrics

Client growth metrics reveal the health of your engineering firm's revenue pipeline and market positioning. These measurements track acquisition rates, client retention patterns, utilization efficiency, and pricing power across your consulting practice.

Number of Clients and New Clients

The number of clients serves as a foundational metric for understanding market reach and business stability. Engineering consulting firms typically track both total active clients and new client acquisitions monthly and quarterly.

New client acquisition rates indicate market penetration and sales effectiveness. Most successful engineering firms aim for 15-25% new client growth annually, though this varies significantly by specialization and market conditions.

Client concentration risk becomes critical when any single client represents more than 20% of total revenue. Board members focus heavily on this ratio since losing a major client can severely impact financial stability.

Tracking methodologies:

• Monthly active clients (those with ongoing projects or retainers)
• Net new clients (new acquisitions minus client losses)
• Client concentration percentage by revenue contribution
• Geographic distribution of client base

Revenue From Existing Clients

The percentage of revenue from existing clients demonstrates client satisfaction and relationship depth. Strong engineering consulting practices generate 70-85% of revenue from existing relationships.

This metric directly correlates with client retention and satisfaction levels. Higher percentages indicate successful account management and service delivery quality.

Revenue expansion within existing accounts often proves more profitable than new client acquisition. Engineering firms with strong existing client revenue typically show 25-40% higher profit margins.

Key measurement approaches:

• Year-over-year revenue growth from existing clients
• Average contract value expansion rates
• Service line penetration within existing accounts
• Renewal rates for retainer agreements

Repeat Business Rate and Utilization Rate

Repeat business rate measures the percentage of clients who engage your firm for additional projects within a 12-month period. Top-performing engineering consultancies achieve 60-80% repeat business rates.

The utilization rate tracks billable hours as a percentage of total available hours. Engineering firms should monitor billable utilization rates to ensure optimal workforce productivity.

Industry benchmarks for utilization rates typically range from 65-85% for senior engineers and 70-90% for junior staff. Higher rates may indicate overwork, while lower rates suggest capacity issues or business development gaps.

Critical tracking elements:

• Project completion to follow-on engagement conversion rates
• Billable hours per consultant per month
• Non-billable time allocation analysis
• Capacity planning based on utilization trends

Average Fee Per Hour

Average fee per hour reflects pricing power and service value positioning in the market. This metric varies dramatically by engineering discipline, client type, and geographic location.

Senior engineering consultants typically command $150-$400 per hour, while specialized expertise in emerging technologies can reach $500+ per hour. Regional variations significantly impact these ranges.

Fee progression over time indicates market positioning strength. Firms unable to increase hourly rates annually often face margin compression and competitive pressures.

Analysis components:

• Blended hourly rates across all service levels
• Rate progression by consultant seniority and specialization
• Premium pricing for specialized technical expertise
• Market rate comparisons within specific engineering disciplines

Advanced Metrics: AI, Automation, and Continuous Improvement

Modern engineering organizations require sophisticated measurement approaches that leverage AI-powered analytics to automatically collect and analyze data patterns. These systems enable continuous tracking of improvement initiatives while providing real-time insights through integrated reporting platforms that connect directly to existing development workflows.

Incorporating AI and Automated Data Collection

AI-driven engineering analytics fundamentally shift measurement from activity counting to work understanding. Traditional metrics like lines of code show only 0.3 correlation with actual effort, while story points barely improve at 0.35.

AI systems analyze code complexity, review quality, and actual work accomplished automatically. They examine every pull request using machine learning models to quantify the effort an expert engineer would need for equivalent tasks.

Key AI-powered metrics include:

• Objective output measurement through complexity analysis
• Code review quality scoring via natural language processing
• AI tool impact tracking across development workflows
• Cross-team performance benchmarking using standardized algorithms

Teams using AI tools effectively can achieve 10x productivity gains over those without proper implementation. However, measuring this impact requires metrics that go beyond traditional approaches to capture nuanced productivity patterns.

Integrations and Reporting Solutions

Engineering metrics platforms integrate directly with existing development infrastructure to eliminate manual data collection overhead. These systems connect to version control, project management, and CI/CD tools to create comprehensive analytics dashboards.

Critical integration points:

• GitHub/GitLab repositories for code analysis and commit patterns
• Jira/Linear for work item tracking and completion rates
• Slack/Teams for collaboration pattern analysis
• CI/CD pipelines for deployment frequency and failure tracking

Modern reporting solutions automatically generate executive summaries that translate technical metrics into business impact. They provide real-time dashboards showing team velocity, quality trends, and resource allocation efficiency.

Automated reporting reduces the time engineering leaders spend on manual metric compilation from hours to minutes weekly. This enables faster decision-making and more frequent course corrections based on data-driven insights.

Monitoring Continuous Improvement Initiatives

Continuous improvement tracking requires baseline establishment and trend analysis over extended periods. Organizations measure improvement through velocity increases, quality metric improvements, and reduced cycle times across development workflows.

Essential improvement metrics:

• Deployment frequency changes month-over-month
• Lead time reduction for feature delivery
• Code review turnaround time improvements
• Defect detection rate increases during development phases

Teams establish improvement targets using historical performance data and industry benchmarks. Elite performers deploy multiple times daily with lead times under one hour, while high performers deploy weekly with sub-day lead times.

Effective monitoring systems track whether productivity improvements are sustainable without causing developer burnout. They measure satisfaction metrics alongside performance indicators to ensure long-term team health while driving organizational outcomes.

Improvement Area	Baseline Metric	Target Improvement	Measurement Frequency
Deployment Frequency	Current releases/week	2x within 6 months	Weekly
Code Review Time	Average hours to approval	50% reduction	Daily
Bug Detection Rate	% caught pre-production	15% increase	Sprint-based

Financial and Return on Investment Indicators

Technical executives need concrete financial metrics that translate engineering investments into board-level language. These indicators demonstrate how technology spending drives measurable business value and long-term competitive advantage.

Return on Assets, Net Present Value, and Internal Rate of Return

Return on Assets (ROA) measures how effectively engineering teams convert technology investments into productive business outcomes. Engineering leaders should track ROA by dividing net income generated from technology initiatives by total assets invested in infrastructure, tooling, and talent.

Most successful engineering organizations maintain an ROA above 8% for major technology investments. This metric helps boards understand whether engineering spending generates adequate returns compared to alternative investments.

Net Present Value (NPV) calculations prove essential for evaluating multi-year engineering initiatives. CTOs calculate NPV by discounting future cash flows from technology investments back to present value using the company's cost of capital.

Infrastructure modernization projects typically show positive NPV when projected benefits exceed $2M over three years. Engineering metrics that connect to financial outcomes enable better resource allocation and budgeting decisions.

Internal Rate of Return (IRR) provides the discount rate that makes NPV equal zero. Engineering projects with IRR exceeding 15% generally receive board approval, as they outperform typical corporate hurdle rates.

Platform investments often achieve IRR of 20-30% through developer productivity gains and reduced operational overhead.

Operating Cash Flow and Net Profit Margin

Operating Cash Flow from engineering initiatives tracks actual cash generation rather than accounting profits. Technical leaders measure this by subtracting operating expenses from revenue attributable to engineering investments.

Successful SaaS companies see engineering-driven cash flow improvements of $3-5 for every $1 invested in automation and platform development. This metric demonstrates immediate financial impact to board members.

Engineering decisions directly influence cash flow through infrastructure costs, development velocity, and operational efficiency. Cloud optimization initiatives frequently generate positive operating cash flow within 6-12 months.

Net Profit Margin improvements from engineering work show up in reduced operational costs and increased revenue per employee. Engineering organizations that focus on automation typically see profit margins increase by 2-4 percentage points annually.

Engineering Investment	Typical Margin Impact	Timeframe
CI/CD Implementation	+1.5-2%	6 months
Infrastructure Automation	+2-3%	12 months
Platform Consolidation	+3-5%	18 months

Payback Period, Avoided Cost, and Interest Coverage Ratio

Payback Period calculations help boards understand when engineering investments break even. Most technology initiatives should achieve payback within 18-24 months to maintain board confidence.

Developer tooling investments typically show 8-12 month payback periods through productivity improvements. Infrastructure modernization projects may extend to 24-36 months but generate larger long-term returns.

Avoided Cost metrics quantify expenses prevented through engineering decisions. Security investments demonstrate value through avoided breach costs, while automation prevents hiring additional operational staff.

Compliance automation often shows avoided costs of $500K-2M annually through reduced audit preparation and penalty avoidance. These metrics resonate strongly with board members focused on risk management.

Interest Coverage Ratio becomes relevant when engineering investments involve debt financing for major infrastructure or acquisition integration projects. Technical debt reduction initiatives improve this ratio by decreasing maintenance overhead that pressures operating income.

Companies with strong engineering practices maintain interest coverage ratios above 5:1, providing financial flexibility for growth investments.

Monitoring Ongoing Engineering Support Costs

Engineering support costs consume 20-40% of most engineering budgets, yet many boards lack visibility into these critical expenses. Tracking existing product support expenses and measuring system reliability through downtime metrics provides the foundation for controlling these hidden cost centers.

Existing Product Support Cost

Engineering teams typically allocate significant resources to maintaining existing products rather than building new features. Research shows that organizations spend 60-80% of their engineering time on maintenance activities rather than innovation.

Support cost categories include:

• Bug fixes and incident response
• Technical debt remediation
• Security patches and updates
• Customer support escalations
• Infrastructure maintenance

Teams should track these costs by measuring engineer hours spent on each category monthly. This data reveals whether support expenses are growing faster than revenue.

Key metrics boards need:

• Support cost ratio: Support expenses divided by total engineering costs
• Cost per incident: Total support costs divided by number of incidents
• Feature development ratio: New feature work versus maintenance work

High-performing teams maintain support costs below 30% of total engineering spend. When this ratio exceeds 40%, it signals technical debt or reliability issues that require immediate attention.

Measuring and Controlling Average Downtime

System downtime directly impacts revenue and customer satisfaction while driving up support costs. Elite DevOps teams recover from failures in under one hour, while average teams take significantly longer.

Critical downtime metrics include:

• Mean Time Between Failures (MTBF): Average operating time between incidents
• Mean Time To Detect (MTTD): How quickly teams identify problems
• Mean Time To Restore (MTTR): Recovery time from incidents

Industry benchmarks show that 99.9% uptime allows 8.77 hours of downtime annually. Each additional "nine" of reliability significantly reduces support costs and customer impact.

Cost control strategies:

• Implement automated monitoring to reduce MTTD
• Create incident response playbooks to minimize MTTR
• Invest in redundancy and failover systems
• Track downtime costs in terms of lost revenue

Teams that measure these metrics consistently report 40-60% reductions in support incidents within six months of implementation.