Funnel-to-Done: Measuring Feature Time-to-Market End-to-End (The RESTRAT Approach)
- RESTRAT Labs
- Oct 15
- 14 min read
Updated: Nov 25
How long does it take for your features to reach customers? That's the question Feature Time-to-Market (TTM) answers. Unlike traditional metrics that only track development progress, TTM measures the entire journey - from idea to delivery. The RESTRAT approach divides this process into two phases: Pre-Work (Funnel → In Progress) and Commit (In Progress → Done), helping teams identify delays, improve workflows, and deliver value faster.
Key Takeaways:
Pre-Work Phase: Focuses on refining ideas and preparing them for development. Metrics like Funnel-to-Start lead time and variance highlight inefficiencies in backlog management and prioritization.
Commit Phase: Tracks active development, testing, and delivery. Metrics like cycle time and throughput show how efficiently teams complete work.
Unified Definitions: Consistent "Definition of Ready" and "Definition of Done" ensure accurate, comparable metrics across teams.
Tools and Automation: Automated tracking (e.g., Jira) reduces errors and ensures reliable data for analysis.
Economic Perspective: Reducing delays and variability in TTM directly impacts revenue, market readiness, and customer satisfaction.
By measuring TTM end-to-end, organizations can pinpoint bottlenecks, align teams, and make data-driven decisions that improve delivery timelines and business outcomes.
Phase 1: Pre-Work Time Measurement (Funnel → In Progress)
The Pre-Work phase focuses on the early activities that shape feature ideas, ensuring they're polished and ready for development. Measuring this phase is crucial because it often reveals hidden delays that can significantly impact the overall Feature Time-to-Market (TTM).
What Defines the Funnel Stage
The Funnel stage includes all the steps where raw ideas evolve into development-ready tasks. This involves product discovery, refining backlogs, defining requirements, and applying prioritization frameworks aligned with the product roadmap.
Key tasks during this stage include managing the product backlog, which holds potential work items like epics and user stories. Teams conduct backlog refinement sessions to clarify and prioritize these items. A well-defined Definition of Ready ensures that tasks are fully understood, refined, and prioritized before moving to development. This reduces delays caused by incomplete details or unresolved dependencies. These clear criteria enable consistent tracking of Pre-Work activities across teams.
Standardizing Workflows for Effective Pre-Work Tracking
To ensure consistent tracking, teams need standardized workflows with clear distinctions between the "Funnel" and "In Progress" stages. This consistency allows for accurate cross-team TTM comparisons.
Product owners play a key role in maintaining a clean backlog by closing outdated or low-priority issues that are unlikely to progress to development.
"Unchecked backlog growth over time. This results from product owners not closing issues that are obsolete or too low in priority to ever be pulled into development." - Dan Radigan, Agile Coach, Atlassian [1]
Consistent backlog refinement practices are essential. These sessions ensure that tasks are properly estimated and broken down during the Pre-Work phase. Without this, teams risk discovering gaps in understanding or sizing later, which can disrupt development velocity. Once workflows are standardized, the next step is to measure Pre-Work time using meaningful metrics.
Key Metrics for Pre-Work Time
The most insightful metrics for this phase include Funnel-to-Start lead time and variance percentage. These metrics highlight delays in refining and prioritizing ideas without requiring complex tracking systems or extra workflow states.
Cumulative Flow Diagrams (CFD) are particularly useful for spotting bottlenecks in the Funnel stage. By analyzing "bubbles or gaps" in the color bands representing the Funnel, teams can identify areas where work is either piling up or being neglected.
"Bubbles or gaps in any one color indicate shortages and bottlenecks, so when you see one, look for ways to smooth out color bands across the chart." - Dan Radigan, Agile Coach, Atlassian [1]
Tracking the average duration and variance within the Funnel stage can expose issues like inconsistent prioritization or unclear requirements, which often signal uncertainty around scope or resource limitations. Pairing these agile metrics with business-focused ones tied to the program’s roadmap ensures that Pre-Work activities align with both internal goals and market demands.
Regular retrospectives are an excellent tool for addressing the root causes of prolonged or erratic Pre-Work times. By resolving these issues, teams can improve the overall efficiency of their development cycle. These metrics not only shed light on pre-development delays but also prepare teams for the Commit phase, enabling a smooth and comprehensive TTM evaluation.
Phase 2: Commit Time Measurement (In Progress → Done)
The Commit phase is where the real action happens - development, testing, and delivery. This is the stage where planned features move into active production, and tracking the time it takes to complete this process sheds light on how efficiently teams convert ideas into deliverables.
Defining 'Done' Clearly and Consistently
One of the biggest hurdles in measuring Commit time is pinning down what "Done" actually means. Without a shared understanding of this term, your time-to-market (TTM) metrics lose accuracy, and comparing performance across teams becomes pointless.
A solid Definition of Done should include clear, measurable criteria that everyone on the team can agree on. This often involves specifying when code is complete, testing is finished, documentation is ready, and the feature is deployment-ready. The goal is to make these criteria explicit so there's no room for guesswork.
However, the meaning of "Done" can vary widely depending on the team. For instance, a frontend team may consider a feature done after user interface testing, while a backend team might include performance benchmarks and security checks. These differences can make it tough to measure progress consistently.
The answer lies in creating organization-wide standards while allowing teams to layer on their own specific requirements. For example, all teams might adhere to core criteria like completing code reviews, passing automated tests, and securing stakeholder approval. Beyond that, individual teams can add their unique needs, such as accessibility compliance or API documentation.
To ensure alignment, regular cross-team reviews are essential. When teams discuss their Definition of Done during retrospectives or planning sessions, any inconsistencies become apparent. This collaborative process ensures your TTM metrics reflect actual delivery completion rather than arbitrary endpoints. With a unified understanding of "Done", teams can track their workflows more accurately.
Leveraging Flow-Time Metrics in the Commit Phase
Flow-time metrics, particularly cycle time, are invaluable for measuring performance during the Commit phase. Cycle time tracks how long an issue stays in the "in progress" stage until it’s marked as "done", offering precise insights into delivery efficiency [1].
"Teams with shorter cycle times are likely to have higher throughput, and teams with consistent cycle times across many issues are more predictable in delivering work." - Dan Radigan, Agile Coach, Atlassian [1]
A well-defined "Done" directly impacts cycle time stability, ensuring these metrics accurately reflect team performance.
Control charts are your go-to tool for monitoring cycle time trends. They provide a snapshot of average delivery times and highlight any inconsistencies in performance. If cycle times start creeping up, it’s a red flag for bottlenecks or process inefficiencies that need immediate attention.
What makes cycle time so powerful is its quick feedback loop. Unlike velocity, which requires waiting until the end of a sprint, cycle time changes can be observed almost immediately. This allows teams to test process changes and see results within days, not weeks.
Cumulative Flow Diagrams (CFDs) are another useful tool. They visually show how work moves through your process, highlighting bottlenecks through color band patterns. Smooth, even bands indicate a healthy workflow, while jagged or uneven bands point to trouble spots.
If cycle times are increasing, start by reviewing your Definition of Done. Teams often add new requirements mid-sprint without adjusting their capacity, which can lead to delays. Regular retrospectives are the perfect time to address these shifts and their impact on delivery flow.
Gaining Deeper Insights with Throughput Distribution
While cycle time measures efficiency, throughput distribution provides a broader view of delivery patterns, helping teams improve predictability. Unlike velocity, which only shows how much work was completed in a sprint, throughput distribution reveals how consistently that work is delivered.
For example, control charts filtered by story point values can show whether your team is completing similarly sized tasks in a predictable timeframe. If five-point stories take anywhere from three to fifteen days, it’s a sign that your estimation process needs work. Variability like this makes planning unreliable and frustrates stakeholders waiting for deliverables.
Inconsistent cycle times often point to deeper issues, such as frequent context-switching, unclear requirements, or uneven review processes. Filtering control charts by work type, story points, or even individual team members can help uncover these patterns.
The ultimate goal is to achieve consistent, shorter cycle times across all types of work - whether it’s new features or technical debt. This level of consistency makes forecasting more accurate and builds confidence in delivery timelines.
Blockages are another major disruptor to throughput. When work gets stuck, it creates backups in some areas while leaving others idle. CFDs make these blockages easy to spot with irregular color patterns, enabling teams to address the root causes rather than just the symptoms.
Common TTM Measurement Mistakes to Avoid
Even the most diligent teams can unintentionally derail their Feature Time-to-Market (TTM) tracking efforts through practices that seem harmless but cause more harm than good. These missteps don’t just distort the data - they create misleading confidence in delivery timelines and block real process improvements. Spotting these common errors is a critical step toward building a reliable system that genuinely drives better results.
Problem: Manual Tracking and Logs
At first glance, manual tracking might seem like a quick and easy solution. But it often introduces errors and leaves room for manipulation, ultimately undermining the accuracy of your TTM metrics.
The biggest pitfall? The temptation to tweak completion dates under pressure. Teams may mark work as "done" prematurely to improve metrics artificially. Dan Radigan, Agile Coach at Atlassian, cautions against this: "But don't let that tempt you to fudge the numbers by declaring an item complete before it really is. It may look good in the short term, but in the long run, it only hampers learning and improvement" [1].
Automated tools can eliminate this issue. Platforms like Jira automatically track progress and generate accurate metrics like burndown charts, team velocity, and cycle times. These tools also automate tasks such as creating sub-tasks, updating workflows, and escalating overdue items, ensuring consistent and reliable data capture. This kind of automation provides a solid foundation for accurate TTM measurement and minimizes the risk of human error.
Another challenge lies in how teams define "done." When definitions vary, it undermines the accuracy and usefulness of TTM metrics.
Problem: Different "Done" Definitions Across Teams
Inconsistent definitions of "done" across teams create significant challenges for enterprise-level measurement. When each team has its own interpretation of what constitutes completion, TTM metrics lose their value for cross-team comparisons and broader portfolio reporting. This inconsistency also makes forecasting unreliable and complicates overall delivery efforts.
The problem becomes even more pronounced when teams operate under different estimation cultures. For instance, adding new requirements to a Definition of Done mid-sprint without adjusting capacity or criteria can inflate cycle times, masking real inefficiencies.
Dan Radigan explains the importance of a uniform approach: "'Done' only tells half the story. It's about building the right product, at the right time, for the right market. Staying on track throughout the program means collecting and analyzing relevant data along the way" [1]. To address this, organizations should establish a baseline Definition of Done that includes essential elements like code reviews, automated testing, and stakeholder approval. Teams can add their own requirements, but these must be documented and communicated consistently.
How to Fix Cross-Team Alignment Issues
To resolve these challenges, standardize measurement processes while respecting team-specific workflows. Automated tools not only reduce manual errors but also help enforce consistent workflow states across teams. The goal isn’t to make every team identical - it’s to ensure their measurement practices contribute to meaningful enterprise-level insights.
Avoid directly comparing velocity across teams. As Dan Radigan advises: "Resist the temptation to compare velocity across teams. Measure the level of effort and output of work based on each team's unique interpretation of story points" [1]. Instead, focus on creating uniform workflow states and transition criteria that all teams can adopt, while still allowing for unique estimation practices.
Team retrospectives are a great way to identify inconsistencies in how "done" is defined or how handoff processes are managed. Tools like cumulative flow diagrams can highlight bottlenecks and areas where shared standards need reinforcement. By combining TTM metrics with retrospective feedback, organizations can uncover the root causes of misalignment and make targeted improvements.
The most effective organizations strike a balance between shared standards and team autonomy. This ensures TTM metrics provide reliable, enterprise-wide insights while allowing individual teams the flexibility to optimize their workflows in ways that suit their unique needs.
TTM KPIs and Economic Framework
Measuring Feature Time-to-Market (TTM) effectively requires more than just tracking how many days have passed. It’s about connecting key performance indicators (KPIs) with economic principles to guide decision-making and deliver better results. By integrating these perspectives, organizations can refine their delivery processes while staying aligned with the workflow efficiency and predictability principles outlined earlier.
Core TTM Performance Indicators
A strong TTM measurement system relies on specific KPIs that offer a clear view of workflow health and delivery consistency:
Average TTM Days: This is the cornerstone metric, representing the average time it takes for features to move from the Funnel stage to completion (Done). Breaking this down by work type - like new features, technical debt, or bug fixes - can highlight differences in workflow dynamics.
Variance Percentage: This metric reflects delivery process consistency. A lower variance indicates a stable and predictable system, while higher variance could signal instability, making it harder for stakeholders to plan and allocate resources effectively.
Throughput Distribution: This tracks the proportion of features completed within specific timeframes, helping teams identify bottlenecks and optimize individual workflow stages for smoother delivery.
Another critical metric is Cycle Time, which measures the time from "In Progress" to "Done." Control charts can help visualize cycle time trends, revealing whether the process is stable. Shorter, more consistent cycle times typically indicate better throughput and predictability.
Velocity: While velocity provides team-specific forecasting, it’s important to avoid comparing it across teams, as estimation practices often vary.
These KPIs serve as a foundation for understanding how delays and variability impact overall performance from an economic perspective.
Donald Reinertsen's Flow Economics Principles
Beyond the metrics, an economic lens provides deeper insights into the cost of delays. Donald Reinertsen, in his book Principles of Product Development Flow, explains how delays accumulate costs with every additional day a feature remains in the pipeline. This perspective elevates TTM from being just a process metric to a key economic indicator, helping organizations focus on improvements that deliver the best return on investment.
Reducing variability in delivery times often creates more value than simply lowering the average delivery time. Predictable TTM enables better planning, clearer communication with stakeholders, and more accurate market timing.
Reinertsen also highlights the importance of managing queues in the Pre-Work phase (Funnel → In Progress). Features sitting idle in queues don’t generate value but still consume resources. His insights suggest that reducing queue sizes can enhance overall flow more effectively than focusing solely on optimizing individual steps in the process.
Jonathan Smart's Outcome-Based Predictability Framework
Adding to this economic perspective, Jonathan Smart stresses the importance of linking TTM to measurable business outcomes. His framework goes beyond delivery speed, emphasizing the role of TTM in supporting learning cycles and achieving results.
Smart recommends tracking TTM alongside metrics that measure value realization. It’s not just about when a feature is marked "Done" but also about when it starts delivering its intended business benefits. This approach ensures that reducing TTM doesn’t come at the expense of quality or market fit.
The principle of continuous learning loops further supports this idea. Regular retrospectives should evaluate both delivery performance and business outcomes. In environments where psychological safety is prioritized, teams can provide more accurate estimates and quickly identify bottlenecks, leading to better TTM data and ongoing process improvements.
Aligning business metrics with TTM measurement is equally important. As one expert explains:
"Business metrics should be rooted in the program's roadmap, with Key Performance Indicators (KPIs) mapping to program goals and success criteria for product requirements (e.g., adoption rate, code coverage)" [1]
This alignment ensures that TTM improvements contribute to broader organizational goals rather than simply chasing faster delivery. By connecting these frameworks with TTM metrics, organizations can create a shared language that ties together predictability, flow health, and business success.
Conclusion: TTM as Your Organization's Flow Language
Feature Time-to-Market (TTM) measurement turns TTM into a shared language that drives predictability and smooth operational flow across enterprises. By adopting RESTRAT's two-phase approach - measuring both the Pre-Work phase (Funnel → In Progress) and the Commit phase (In Progress → Done) - organizations create a solid foundation for clear communication across teams, departments, and leadership.
TTM becomes the go-to framework for aligning everyone. This shared understanding eliminates the disconnect that often arises when teams rely on different metrics or when stakeholders struggle to make sense of progress reports.
Key Points to Remember
One of the most valuable takeaways from implementing end-to-end TTM measurement is this: visibility fuels progress. When teams can pinpoint exactly where features are delayed in the pipeline, they naturally focus on the bottlenecks that have the biggest impact. Industry insights confirm that reducing variability directly boosts value creation.
"Consistent and short cycle times lead to higher throughput and increased predictability in delivering work, which are crucial for effective governance and planning" [1].
This level of predictability becomes a game-changer when organizations need to make strategic decisions, whether it's about reallocating resources or timing a market launch.
However, common challenges - like manual tracking, unclear definitions of "Done", and misaligned cross-team goals - can create gaps in the data needed for informed decision-making.
"Tracking and sharing sound agile metrics reduces confusion and provides transparency into a team's progress and setbacks throughout the development cycle" [1].
This transparency fosters trust between development teams and business stakeholders, enabling better forecasting and smarter strategic planning.
Armed with these insights, organizations can use TTM measurement to redefine how they approach agility and flow.
The Future of Feature TTM Measurement
By leveraging both Pre-Work and Commit metrics, organizations can elevate Feature TTM into a standard for enterprise predictability and flow management. Mastering TTM measurement today allows businesses to respond more quickly to market shifts, allocate resources with precision, and deliver consistent value.
"TTM-related metrics help ensure that the right product is built at the right time for the right market, connecting development efforts directly to business outcomes" [1].
This integration transforms TTM from being purely an internal efficiency measure into a strategic business tool that influences everything from product planning to investment strategies.
As organizations refine their TTM practices, they find that aligning delivery performance with strategic goals creates a powerful feedback loop. Delivery outcomes inform strategy, while strategic priorities drive meaningful process improvements.
The future belongs to organizations that focus on what flows through their system, not just what starts. By adopting Feature TTM as the foundation of their flow language, enterprises can optimize their entire value delivery system. Conversations about priorities, capacity, and delivery will no longer rely on assumptions - they’ll be rooted in actionable data.
FAQs
How does the RESTRAT approach make feature delivery more predictable and efficient?
The RESTRAT approach enhances feature delivery by tracking Feature Time-to-Market (TTM) through two critical phases: Pre-Work (Funnel → In Progress) and Commit (In Progress → Done). Instead of complicating workflows with extra states, it prioritizes flow efficiency and focuses on achieving predictable outcomes.
Drawing from Donald Reinertsen’s flow economics and Jonathan Smart’s outcome-driven agility, RESTRAT offers practical insights to address inefficiencies. For example, it helps pinpoint issues like unclear "Done" definitions or dependence on manual logs. The result? Improved cross-team alignment, stronger governance, and faster delivery cycles - all backed by economic principles and measurable data.
What are the biggest challenges in measuring Feature Time-to-Market (TTM), and how can they be addressed?
One of the biggest hurdles in tracking Feature Time-to-Market (TTM) is the reliance on manual logs. These can often be riddled with errors and inefficiencies. Another common problem? Teams sometimes have differing interpretations of what "Done" actually means. This lack of alignment can lead to confusion and unreliable metrics.
To tackle these issues, start by establishing clear and consistent definitions for every workflow state - especially "Done." This ensures everyone is on the same page about progress. Also, consider automating data collection wherever you can. Automation not only boosts accuracy but also cuts down on the time spent on manual tasks. Together, these steps make your TTM metrics more dependable, actionable, and better aligned with your organization's goals.
How can teams align on a consistent 'Definition of Done' to ensure accurate Time-to-Market (TTM) metrics?
Ensuring a unified Definition of Done (DoD) across teams is essential for keeping Time-to-Market (TTM) metrics accurate and reliable. When teams have differing interpretations of what 'done' means, it can create inconsistencies in data and make it difficult to compare workflows effectively.
Here’s how organizations can address this:
Define a standard DoD with clear, measurable criteria that apply to all teams. While some flexibility for specific team contexts is fine, the core criteria should remain consistent.
Encourage cross-team collaboration to align on expectations and clarify any ambiguities. This helps ensure everyone is on the same page regarding delivery milestones.
Continuously review and update the DoD to reflect process changes. Incorporate feedback and lessons learned to keep it relevant and effective.
Focusing on alignment and clarity not only enhances TTM tracking but also boosts predictability and improves workflow efficiency across the organization.
