Organizational, Engineering & Product Management KPIs and Metrics

Zones to Win Framework

Productivity Zone

Backend customer support & other support Functions like operations, IT, HR, sales, marketing Key is operational leverage, efficiency -Automate, reduce inefficient workflows

Performance Zone

Products, lines of business Key is Sustainable value creation - market share, margins, growth

Transformation Zone

Few bets for New products, business model, GTM -Will lead to hypergrowth & revenue share in next few years Key is how to win customers, get high market share- may require upfront investment and operating losses

Incubation Zone

Anticipating next big wave, technology innovations Being agile, lean, fail fast be the key – have clear entry-exit criteria for pathfinding, PoCs

Product Management Framework & Metrics

New Features

To add new customers, add more value to existing customers Try to maximize this but watch for RoI for any new features New features add more complexity to existing product, and over time products maintenance even more complex and costly

Enhancement, Extensions to existing features

To add value to existing customer, improve experience, scale existing product to adjacent customers Maximize this; but need to watch for backward compatibility, complexity addition

Re-Architecture, methodology improvements, Automations, Refactoring

To reduce technical debt, make team more efficient, improve DLE Keep fixed BW to keep product best in class, happy employees Tie up to continuous incremental efficiency gains

Defects, Customer Support

To retain current customers Try to automate as much as possible, reduce disruptions to core team and product

How much to allocate for each category

Much of it depends on phase of a project.

For a new project when in MVP and customer acquisition phase - New features and enhancements take most of engineering bandwidth.

For a project with established customers, you need to spend much more time on customer support, bug fixes than on new features

For a old legacy project; it will make sense to spend bandwidth on refactoring, and methodology improvements.

Engineering Metrics

Topics

Why Metrics - Benefits of Metrics What kind of metrics Popular Metrics How to implement

Basic Principles of Engineering Management

Great Software Engineering Managers craft excellent SW process and environment so that Great Software Engineers can craft Excellent SW Products efficiently Basic Principles for SW Engineering Management Objectives as measured by Metrics Repeatable processes for reliable systems

Why metrics –Benefits of Metrics

To get Clues of state of affair, hot spots, trends To measure progress and impact of any initiatives To make strategic and tactical decisions on portfolio, investments

What Metrics to measure

Engineering Productivity Metrics Measures effectiveness and efficiency of engineering workflow Team Well-Being Metrics Measures team well-being, satisfaction, engagement and alignment Investment & Planning metrics Measures where we invest, how much we invest, when we invest

Metrics Categories

Leading Metrics & KPIs Measures progress in implementation, adoption Typically measures about a unique part of workflow or initiatives Examples: Defects, DORA metrics, Automation rate, cycle time, Employee engagement survey results Lagging Metrics and KPIs Measures end impact to end users, business, productivity (or against any other objective some effort was undertaken) Examples - Cost Saving, increase in revenue, more products, less customer issues/quality, Time to market, retention of employees etc)

Popular metrics & frameworks

Team Well-being Metrics Typically implemented through surveys Engineering Productivity Metrics DORA Metrics SPACE framework Product Management Metrics Zones to Win framework

Team Well Being Metrics

Team well-being can be measured how a team member feel at the start and end of the day Some key aspects

Accomplishment

Whether team members feel they accomplished what they started out Focus Flow and Distractions, effective meetings Are they working on right things, product/business strategy and direction

Resources

whether they feel they have resources to achieve their objectives, obstacles

Team & Comms

What do they feel about rest of team, Collaboration, Communication, inclusion, ability to raise issues, provide feedback

Learning and growth

• whether they feel they have opportunities to grow their skills and careers
• Team well-being metrics are still hard to define except lag metrics like attritions(which are also little vague as most attritions could be due to many other factors than well-being)
• Typically well-being are measured through customized surveys at different levels
• Organization level Employee satisfaction; Manager feedback; targeted surveys for a specific aspect
• Managers/Leaders can/should gauge this aspect in regular 1:1s, skip level meetings, HR connect sessions, leadership

Other well-being metrics

Innovations/Patents/Papers Cross-Group ideas Diversity Ratios (check if team is not very homogeneous specially state/language in India)

Engineering Workflow Metrics

• Development
• Release & Deployment
• SRE and Customer Support
• Validation
• Issue Accepted
• Issue Received
• PR validated
• PR Released
• PR Merged
• PR Raised
• Speed Metrics
• Development Cycle
• PR Merge Cycle Time
• Validation Cycle Time
• PR to Deployable Cycle time (Lead time for changes)
• Release Frequency
• Customer Escapes
• Quality Metrics
• Change failure rate %
• Development Failures, Defects %
• Uptime %
• Reliability
• MTBF
• Ticket Escalation Rate to L3
• Response SLA
• Service Support
• MTTR - Mean time to restore or Mean time to resolve
• Resolution SLAs

Engineering Team level Metrics

• Customer Support
• Development
• Integration & Validation
• Release & Deployment
• Unit tests coverage
• Number of PRs merged/week
• Number of backlog of defects
• Defect Analysis and Fix time – average resolution time
• Commit vs do ratio (Scrum velocity)
• CI cycle time
• Number of defects escaped
• 1st Response SLA
• Resolution SLAs
• L1-L2 Support Efficiency ( % issues solved at L1-L2)
• System Build & Frequency
• Validation cycle time
• Requirement Test Coverage
• Automation Rate
• Customer Defect %
• Release & Deployment Frequency
• Release stability & reliability (release revert ratio)
• Lead time to release & deploy

Metrics to track with Gen-AI assisted SW Engg

• Lead Metrics
• How many defects found by AI assistants and acceptance ratio
• How much is test coverage by AI generated unit test frameworks
• AI assisted coding – how much code being generated by AI assistants
• Amount of test coverage by AI generated tests
• Lag Metrics
• Acceptance and density of AI assistants used in team
• Amount of features/fixes shipped per sprint/month
• Speed of a bug fix
• Bugs found by AI
• Documentation improvement by AI