Measuring success is a key step in any software development project. To do this effectively, you need to know the proper metrics to track. These are called key performance indicators, also known as KPIs.

KPIs are particularly useful for companies that outsource development to a third-party software development company, as they help verify if the project has been properly executed.

In this guide, we will take a deep dive into the world of key performance indicators for software development, exploring the top 23 metrics that you should be paying attention to.

Key Performance Indicators (KPIs) are quantifiable metrics used in software engineering to evaluate a project’s success and efficiency.
Tracking KPIs helps improve transparency, identify bottlenecks, and boost developer productivity and product quality.
To implement KPIs successfully, you should define clear ownership, align them with business goals, set realistic baselines, and review them regularly to drive continuous improvement.
KPIs can be categorized into different groups based on the outcomes they measure.

What Are Software Development KPIs?

In software engineering, Key Performance Indicators refer to quantifiable metrics used to evaluate the success and efficiency of a development project. KPIs track different aspects of any project, including delivery health, team dynamics, alignment with project goals, and strategic objectives, among other things.

Software development KPIs differ from generic metrics meant to track everything. The team isn’t merely collecting data related to the software system. Instead, tracking KPIs is about measuring specific data points that determine how well a project is aligned with expected outcomes.

Choosing the wrong KPIs or measuring vanity metrics (that aren’t tied to clear or specific goals) can be counterproductive.

Why Tracking KPIs Matters in Software Development

When building complete software, the last thing you want to do is rely on anecdotal evidence or subjective assessments. Instead, you should use KPIs, which tie data to specific goals to gain significant insights into different aspects of your project. Some of the most notable benefits of KPI tracking for engineering teams and organizations are highlighted below.

Improved transparency – Your software development project shouldn’t be a black box. In a project with no KPIs set, it’s impossible to tell when the project is behind schedule or which team is responsible for missing deliverables. KPIs turn opinions into facts and numbers, so you know precisely to what extent and who has fulfilled or not fulfilled their task. Similarly, incorporating testing metrics into your KPIs can provide even clearer insights into the project’s health.
Easier bottlenecks identification – Large-scale product development projects often have multiple wheels turning simultaneously. A problem in one area of development can carry over into another. Identifying issues when they arise and pinpointing the exact area where your project is slowing down can be difficult, especially if you’re not tracking KPIs. By doing so, you can make informed decisions on how to optimize processes for greater efficiency.
Enhanced productivity – Tracking the right metrics will help engineering teams focus on what matters most. This boosts the productivity and overall performance of the team. KPIs will also help you establish where your project stands at any point in time and the areas where you need to make improvements to boost productivity.
Better product quality – KPI tracking will also increase the overall quality of your software product. To start with, tracking relevant metrics ensures that your product is fully aligned with its set objectives. It also establishes a clear feedback loop. This provides insights that can be used to optimize the development process for better results. Similarly, incorporating testing metrics into your KPIs can provide even clearer insights into the project’s health. Automated testing frameworks like Cypress and Selenium are widely used to ensure code quality and catch issues early.

Choosing the Right Framework for Measuring KPIs

With every software project, there are multiple data points and metrics to track. Choosing the right number and type of software development KPIs is critical. Too few, and you’ll end up with blind spots. But too many KPIs or wrongly focused ones are just as detrimental. To choose the right KPIs, engineers can use different frameworks. Some of the most notable frameworks they use are highlighted below:

DORA Metrics: The Industry Standard for DevOps Performance

DORA stands for DevOps Research and Assessment. This is a set of standards used to determine the velocity and stability of a software development project. The DORA framework has four software delivery metrics. They include:

Deployment Frequency – how often new code is released
Lead Time for Changes – the time it takes to go from commit to production
Change Failure Rate – percentage of failed deployments
Mean Time to Recovery – the amount of time it takes to fix failures.

DORA metrics are important for evaluating a project’s success and progress. They can also help identify performance trends within the development lifecycle while assessing best practices across engineering teams.

The SPACE Framework: Measuring Developer Experience

SPACE is a framework that measures the productivity of development teams. This approach goes beyond conventional productivity KPIs like lines of code and number of pull requests. These metrics are effective for measuring speed but fail to accurately capture developer productivity and team satisfaction.

SPACE is an acronym representing the five dimensions of this productivity-focused framework. These dimensions include:

Satisfaction
Performance
Activity
Communication/collaboration
Efficiency and flow

Across each of these dimensions, there are individual, team, and system-level metrics. Software development managers can track these metrics to gain a multi-dimensional view of the factors blocking team productivity.

Goals-Signals-Metrics (GSM): A Practical Approach

The Goals-Signals-Metrics Framework is an approach for choosing KPIs based on business goals. This approach begins by defining what goals you’d like to achieve with your product. Next, you decide on what signals can be used to examine these goals and finally choose the metrics that best capture these signals (since they are hard to measure directly).

Signals are indicators that show if you’re on the right path toward achieving your goals. For instance, comments or reviews of your product are good signals of product adoption. Metrics quantify these signals with concrete data so you can accurately measure how well your goals are being achieved.

Value Stream Mapping: Seeing the End-to-End Flow

Value stream mapping (VSM) is a framework that software development teams can use to visualize the flow of work from ideation to delivery. This approach maps the entire process, directly linking software development KPIs to business outcomes to provide a clear end-to-end view of how value is delivered. VSM makes the abstract process of software development visible and quantifiable. Mapping the flow this way helps development teams identify bottlenecks and improve metrics like cycle time, throughput, and predictability.

Key Categories of Software Development KPIs

Software development KPIs can be grouped into different categories based on the project outcomes they help you measure:

delivery & flow,
quality,
reliability & operations,
developer experience,
business outcomes,
security & compliance.

Below is an in-depth exploration of these categories and the specific metrics to measure in each case.

Delivery and Flow Metrics

Delivery and flow metrics refer to KPIs that measure how efficiently software engineering teams deliver software. They are crucial for understanding and improving the entire software development lifecycle, from idea to delivery, as they measure speed, predictability, and throughput. Some of these metrics include:

Lead Time for Changes

Lead time for changes measures the time from laying the first lines of code to releasing the code to users. It is measured by calculating the time elapsed from the moment a developer’s code commit is made to the moment the change starts running in a live production environment.

While the concept of lead time is simple, accurately measuring it can be challenging, especially when there are no tools to automatically track timestamps. Also, while measuring lead time is straightforward for certain projects, it might be difficult to measure for projects with complex workflows, inconsistent start and end points, and long wait times.

Cycle Time

Cycle Time determines how much time the team spends completing a specific task. Managers use this one a lot because it objectively assesses the pace of the team’s work and allows them to estimate and evaluate the speed of future sprints.

Cycle time is counted from the moment the team accepts the task to its completion; there is no room for opinions in this KPI, but you need to ensure clear rules for receiving and completing tickets by the team.

To understand cycle time, you need to break an entire development cycle down into its constituent phases, such as coding time, review time, and waiting time. Improving cycle time requires a focus on reducing both active work time and, more importantly, idle waiting time. This can be achieved by implementing smaller pull requests, better collaboration, and introducing as much automation as you can manage.

Deployment Frequency

Deployment Frequency measures how often the software development team delivers code ready for deployment. Thanks to this software development KPI, you can access a team’s continuous delivery maturity and their ability to provide tangible value, like a new feature or a bugfix, over time.

This is a fundamental metric for teams working in short development intervals, like startups that want to verify various business assumptions quickly and need to release functional prototypes regularly.

Tracking deployment frequency involves simply counting the number of successful deployments over a specific time period (e.g., daily, weekly, or monthly). This data can be collected automatically by a Continuous Integration/Continuous Delivery (CI/CD) pipeline.

Note that the goal isn’t merely to get teams to deploy more often. In fact, high-performing teams tend to focus more on deploying small, incremental code changes, which dramatically reduces risk because it makes it easier to isolate bugs, recover from issues, and gather feedback for immediate improvements.

Throughput and Work in Progress

Throughput is a Key Performance Indicator that shows how many tasks the team has done in a given sprint. It doesn’t evaluate progress, only the total number and type of completed tasks. It is measured with metrics like the number of issues, pull requests, or features delivered in a given period.

Thanks to this KPI, managers can assess what the team spends the most time on and why. Tracking this metric makes the most sense in projects conducted in Kanban, but you can occasionally use it in Scrum-driven projects too.

While it may seem counterintuitive, too much concurrent work slows delivery and increases bottlenecks. This is where work-in-progress (WIP) limits come in. WIP limits set a maximum cap for the number of tasks in a specific stage of the workflow. This focus on completion over starting new work will also help improve cycle time, along with the project throughput.

Flow Efficiency and Predictability

Flow efficiency is a KPI that compares the active time spent on a project to the total time. This is an important metric, since a good percentage of the time spent on a project is spent standing still rather than working actively.

On the other hand, predictability shows how likely a team is to consistently deliver on its commitments. A high ratio of delivered-to-planned work indicates that a team has a highly predictable process. On the flip side, a consistently low predictability ratio could be a sign of estimation issues, too much Work in Progress (WIP), or process bottlenecks.

These KPIs can help the software development team determine the amount of time wasted in a workflow.

Quality Metrics

Quality metrics are general software engineering KPIs that are used to track a software’s quality and technical health. These metrics evaluate defects, code coverage, code churn, and overall maintainability of the software. The goal is to help teams deliver more reliable software over time by following thorough code review standards.

Defect Rate and Defect Density

The defect rate is a measure of the total number of bugs that have been identified in a software product during testing, either within a specific timeframe or per thousand lines of code. It can also be used to compare defect rates for different projects or against a specific benchmark. Developers can use this rate to set goals for reducing the total number of bugs. While the defect rate measures the number of bugs, the defect density is a measure of the severity of these bugs.

These metrics are important for gauging the overall quality of a product. A consistently high defect rate shows that the quality is declining and points to a need to optimize the development process, improve testing, and quality assurance.

Defect Leakage and Defect Detection Ratio (DDR)

The goal of the testing and review phase of any software project is to limit the number of bugs in a product when it goes live. However, since bugs are practically unavoidable, some bugs may still find their way into the production environment. The KPI that measures this is Defect Leakage, which is the total number of bugs that manage to find their way into production after a release.

Similarly, the Defect Detection Ratio (DDR) is a metric that quantifies the effectiveness of catching these bugs before they make their introduction. This is done by comparing the number of defects found before release to the total number of defects found both before and after release. A high DDR score means the testing process is highly effective at catching bugs before they reach customers.

To reduce defect leakage in future tasks and raise the DDR, developers need to implement some measures in testing, such as increasing test coverage and introducing some automation into the software delivery process.

Test Coverage

Test coverage (code coverage) is a quality metric that measures how much of the software has been tested. This is determined by dividing the number of lines of code that have been tested by the total lines of code, expressed as a percentage. Different types of test coverage exist. This metric can measure the total lines of code (statement coverage), branches, or paths that have been tested.

Generally, code coverage shows how much effort has gone into testing a software product, but it is not a definite indication of quality. A test suite could have 100% code coverage while still being ineffective at catching bugs. The best way to determine the overall quality of the code is to combine test coverage with defect trends and other relevant software KPIs.

Code Churn and Rework

Code churn is a metric that shows the amount of code that has been rewritten within the software development cycle. This is an important KPI that can be used to evaluate code quality as well as the efficiency of the coding process.

Generally, you want to keep code churn as low as possible. That’s because the more lines of code your team has to rewrite, the more time and resources you’ll have to expend to complete the project. A high churn rate is an indication of inefficiencies in the development process due to factors such as unclear requirements, unstable architecture, or knowledge gaps.

To minimize code churn, you should follow a development approach that allows you to test the code early on. This way, you can catch bugs and fix them before they lead to bigger problems down the line.

Code Complexity and Maintainability

Code complexity and maintainability refer to a series of metrics used to measure how stable and understandable a software’s codebase is. This KPI is tracked using a combination of metrics such as Cyclomatic Complexity, Maintainability Index, and Class Coupling. Tracking these metrics helps the team gain a proactive understanding of the project’s technical requirements. These metrics also help to measure code simplicity and prevent the software from becoming a tangled, unmanageable mess.

Reliability and Operations Metrics

The overall quality of your software hinges on the efficiency and reliability of your software development process. To measure this, you can focus on key software engineering KPIs that measure system stability, incident response, and the overall health of your operation. Some of the core operational metrics are highlighted below:

Change Failure Rate

Change failure rate measures the percentage of deployments that fail at the production stage. It can serve as an indicator of just how effective your development team is. A low change failure rate shows the team’s ability to release high-quality software that requires little to no corrections. On the flip side, a high failure rate shows that there are major problems in the production and delivery lines. These can be fixed by improving testing, monitoring, and deployment strategies.

Mean Time to Restore (MTTR)

Software failures are largely inevitable regardless of how well-built a product is. The Mean Time to Restore (MTTR) is a metric that shows how well software is able to recover from these failures when they occur. It is important to track this metric since long periods of outages after failures negatively affect user experience and business continuity. This is why organizations have to focus on reducing MTTR.

Some strategies that can help achieve this include automating system monitoring for quicker bug detection, streamlining the incident response system, and enabling fast rollbacks to ensure continuity while bugs are being fixed.

Mean Time Between Failures (MTBF) and Uptime

The Mean Time Between Failures is similar to the MTTR. However, instead of focusing on how fast a product can recover from failure, it measures how long it can stay without any incident. MTBF metrics are an important measure of a software’s overall reliability. A metric closely similar to this is Uptime, which measures how much time a service stays operational and accessible to users.

Engineering leaders can use these reliability metrics to define and manage service expectations for future projects, as indicated in the Service Level Agreements with users and the internal service level business objectives. The MTBF and uptime can also be linked to the software’s incident management protocol, which ensures that teams learn from every failure.

Incident Volume and Severity

Incident Volume and Severity metrics measure how often incidents occur in a system as well as the impact of these incidents. Incident volume is a raw count of incidents over a given time period, while severity shows how much it impacted business or the number of users affected. These KPIs measure the overall health of a system. They give the engineering team visibility into potential systemic issues that must be fixed to make the system more resilient.

Developer Experience and Process Metrics

Some KPIs measure developer experience (DevEx) and the efficiency of processes within the software development lifecycle. These metrics provide a vital link between team morale and overall productivity.

Pull Request Size and Review Time

Tracking pull request size and review time helps you decipher how efficient your collaboration is and spot bottlenecks in your feature delivery and review process. The pull request size measures the number of lines of code that have been added or changed in any pull request. Generally, a smaller PR size (less than 200 lines) is a sign of a healthy workflow.

Pickup time, on the other hand, refers to the time from when a pull request is opened until someone starts working on it. The goal is to minimize pickup time. Long pickup times indicate that the team lacks enough reviewers or that they aren’t prioritizing pull requests. Similarly, the review duration measures how much time it takes to review any pull request, factoring in all back-and-forth interactions for revisions.

Time in State and Waiting Times

This KPI provides a close-up view of your workflow by tracking how much time a specific work item (such as a new feature request or bug fix) spends in a particular state within the workflow (coding, review, testing, and deployment).

This metric is important because, instead of simply tracking the total cycle time of a work item, it measures how much time is spent (or wasted) in each development stage. This granular measurement helps pinpoint the specific state where you have a bottleneck so you can implement process improvements.

For instance, a work task may have a total cycle time of 10 days, but a closer analysis of the time in state and waiting time may reveal that five of those days were wasted in a review queue, indicating this phase of your workflow needs optimization to improve overall delivery speed and reduce bottlenecks.

Developer Satisfaction and Surveys

Certain metrics measure how happy, engaged, and satisfied the development team is with your current workflow. Organizations can measure this by collecting feedback directly from software developers through surveys or by using comprehensive frameworks like SPACE metrics. In addition to measuring developer satisfaction directly, the SPACE framework connects qualitative data from surveys to quantitative data from the tools developers use for work (Performance, Activity, etc.).

Business and Outcome Metrics

The ultimate goal of any software development project is to build a solution that meets users’ needs and achieves desired business outcomes. Metrics like feature adoption, retention, and NPS can be used to measure how successful your product is at meeting these goals. Below is an overview of these business and outcome-related metrics.

Feature Adoption and Usage

Feature adoption is a metric that shows the number of users who have used a new feature at least once within a given time frame. Feature usage takes this further by measuring how frequently users who have adopted a feature use it.

These two metrics go a long way in showing the success (or otherwise) of a new feature integration. Engineering teams can use these data points to identify potential problems with the feature release, such as a lack of awareness, poor user experience, or poor market fit.

Net Promoter Score (NPS) and Customer Satisfaction (CSAT)

Net Promoter Score examines clients’ general satisfaction with the company through two simple questions:

On a scale of 0 to 10, what is the chance you will recommend our services to your friends?
Why did you give such an answer?

The NPS survey captures the subjective feelings of clients towards the company’s work. The survey can be conducted online or live and is typically repeated periodically, at intervals of 3 to 12 months.

NPS allows companies to discover what works well and what doesn’t in their product. The feedback derived from measuring this metric can be combined with technical KPIs to get a complete view of a product’s performance. For instance, a low NPS score shows that customers are dissatisfied with your product, while a technical KPI like MTTR provides the “why” behind the low score. NPS results can also be used for marketing or sales purposes, such as upselling or cross-selling to companies that score high.

NPS is measured by subtracting the percentage of detractors (scores of 0 to 7) from the percentage of promoters (scores of 9 and 10). It’s generally assumed that a Net Promoter Score between 0 and 30 is decent, while above 60 is excellent. An alternative to NPS is CSAT (Customer Satisfaction Score), which is measured on a scale of 1 to 5.

Time-to-Value and Experiment Velocity

Time-to-Value (TTV) and Experiment Velocity are two KPIs that help demonstrate the link between a company’s internal speed and the direct business value it creates for customers. Time-to-Value measures how quickly a new feature delivers tangible benefits to users. Experiment Velocity measures how fast the engineering team can experiment with new features and learn from them. Understanding the interaction between these two metrics is vital for business growth and innovation. It helps the team discover and build features that truly matter to users and iterate over time as they build.

Cost per Feature and ROI

Cost per Feature and ROI are KPIs that link financial investment directly to the value an engineering team creates. This analysis is instrumental in future decision-making, particularly regarding resource allocation and project planning.

The average cost per feature is calculated by dividing the total engineering team cost by the number of features delivered. Note that this metric should not be taken in isolation. It’s not enough for a team to deliver relatively “cheap” results by cutting corners, as this will only lead to a high failure rate later on. Instead, the focus should be on balancing investment against delivery speed and product quality.

Security and Compliance Metrics

KPIs can also track how secure and reliable the software product you’re building is. Tracking metrics like vulnerability backlogs, time to remediate, and compliance readiness gives development teams insight into their software’s security status. Details of these metrics are summarized below.

Vulnerability Backlog and Remediation Time

A vulnerability backlog is a list of all bugs in a software product that are yet to be fixed. It is a critical metric that helps you keep track of all bugs and vulnerabilities being discovered. The backlog can be further categorized based on severity, ensuring that the most serious bugs are fixed first instead of being left for later.

Remediation time measures how much time it takes software developers to fix security vulnerabilities once they’re identified. It is ultimately a measure of operational efficiency, but also provides a glimpse into how secure a product is, since a fast response time helps minimize exposure.

Policy Coverage and Compliance Readiness

To ensure compliance readiness, organizations must track how well their software development lifecycle adheres to security standards and policies. This can be achieved through a set of policy coverage KPIs.

It is also recommended to integrate these KPIs into an engineering dashboard. This provides a single, unified view of the entire product, allowing you to proactively recognize gaps and fix them ahead of internal or external audits.

Building KPI Dashboards and Reports

Knowing which KPIs to track is only the first step. To gain insights and drive meaningful actions with these KPIs, you need to figure out how to combine these metrics into visually informative dashboards. Some visualization techniques include:

Cumulative Flow Diagrams

A CFD is a type of stacked area chart used to show the progress of work in a software development workflow. The CFD is useful for visualizing metrics related to work progress, such as Cycle Time, Throughput, and Work in Progress. Using the cumulative flow diagram, development teams can determine how long each task cycle takes. It can also identify potential bottlenecks within a particular task workflow.

Control Charts

A control chart is a statistical tool that shows the stability or predictability of a specific data point over time. For instance, this chart can plot the time cycle of individual work items. Predictable data points fall within the chart’s control limits, while outliers fall outside these limits. Outliers represent software development processes that are out of control and require further investigation.

Burnup Charts

A burnup chart visually represents the work completed compared to the total scope of work. One line represents the total volume of work that needs to be done, while another shows how much work has been done. The gap between these lines shows the remaining work yet to be done. Burnup charts help teams track progress toward a goal and identify scope creep.

Service-level Dashboards

Service-level dashboards visualize and monitor the compliance of a software product with its service-level objectives. This tool provides a real-time view of the health of a service and can visualize software metrics like uptime, error rates, request latency, and other related data points.

The dashboard displays a grid of service level objectives, listing each metric and its goal or target value. When an objective is selected, a gauge or chart shows if the target objective has been achieved. Service-level dashboards use simple visualizations such as “traffic light” indicators (green, yellow, red) to provide an immediate status check, with green corresponding to success and red showing failure.

Best Practices For Presenting Metrics Without Overwhelming Teams

Data and metrics can be a dry subject. While visualization can help clarify information, there are best practices to avoid overwhelming your team with too much data. These include:

Keep your dashboard simple and uncluttered.
For each dashboard, focus on presenting 5 to 10 KPIs directly related to the team’s goals.
Tell a clear story with your software metrics.
Don’t just present numbers; provide context and supporting information for clarity.
Ensure that your dashboard offers real, actionable insights.

Common Pitfalls and Anti-Patterns

Data is a double-edged sword. When handled wrongly or out of context, measuring KPIs can negatively affect business outcomes and customer satisfaction. To avoid this, watch out for these common mistakes when measuring key performance indicators:

Tracking vanity metrics – Not all data is useful. Certain metrics might look good on paper, but don’t provide clear, actionable insights about your team’s performance or business value. For instance, tracking the number of lines of code written might seem like a good way to measure productivity, but it has no real bearing on code quality, maintainability, or value to customers.
Comparing teams without context – Every team is unique and should be treated independently when comparing KPIs. Factors like team size, project complexity, tools, and dependencies affect metrics. Blindly comparing teams without considering their unique contexts is unhelpful.
Incentivizing speed over quality – The goal of measuring KPIs isn’t merely to boost speed. As you measure software metrics related to product delivery speed, such as lead time and throughput, you must also consider quality metrics like defect rate or failure rate. This prevents your team from cutting corners for speed while compromising quality.
Goodhart’s Law – According to Goodhart’s Law, when a measure becomes a target, it ceases to be a good measure. The metrics you’re tracking are meant to measure the true and current state of your project. If these metrics become numbers or benchmarks your team is trying to hit, the KPI loses all meaning.

Best Practices for Implementing KPIs for the Development Team

Measuring metrics is only half the equation. Figuring out how to rally your development team to implement the insights you gain from measuring KPIs is arguably the most important part of the process. Here are some actionable recommendations for introducing KPIs to your team:

Define clear ownership – Before measuring anything, define who is responsible for tracking data, analyzing it, and applying insights.
Align KPIs to business goals – Don’t track metrics just for the sake of it. Start with a business objective and work backward to find the right technical KPIs for measuring how well this goal is achieved.
Set realistic baselines – Avoid arbitrary targets and unrealistic goals. Measure the true state of things now and introduce gradual improvement over time.
Review metrics regularly to drive continuous improvement – Tracking KPIs isn’t about one-off reporting. Collect data, measure performance, and regularly review metrics to drive continuous improvement.

How Do We Measure KPIs in CrustLab?

As mentioned, projects differ in dynamics, and not all KPIs are always worth tracking, although the ones listed above are necessary in most cases. But let’s move from theory to practice. Here’s how we handle team performance metrics at CrustLab.

Your Key Performance Indicator is Ours Too

We start each project by mapping the business mission and vision. When a client comes to us, we carefully examine their business goals, such as when they want to put the product’s final version into use and what a “final version” means to them.

Then we break down these goals into tangible pieces and estimate how much time is needed to implement such a plan. This is when we transform the client’s vision into numbers and define goals numerically – that is, we set KPIs.

Finally, we familiarize the software development team with the defined KPIs. From now on, the client’s goals become our goals, and the product’s success is our success.

Sprints and Sprint Retrospectives

At CrustLab, we work using Agile software development methodologies. They assume work in 1- or 2-week intervals, during which we open and close a clearly defined project section, be it a feature or a whole product.

After such an interval, called a Sprint, we hold a team meeting to present progress (called Demo) and gather feedback from all stakeholders. This meeting is supplemented by the Sprint Retrospective, and based on both meetings, supported by our KPIs, we analyze what went well and what needs improvement. These are key activities to ensure we don’t lose sight of the business goal.

Why is it important? Because working in Scrum requires ongoing monitoring of project progress. We measure the KPIs of both sprints and the entire project. This gives our clients detailed insight into work progress on both micro and macro scales.

Access to KPIs

In our daily work, we use Jira, a task management tool accessible to our clients, where they can check KPIs such as sprint burndown, workload, workflow, work in progress, and many others. Depending on the project and business needs, those KPIs can be supported by many different metrics that help us understand the environment we work in and adjust to its needs.

Conclusion

If we had to summarize KPIs in five main points, they would be:

KPIs indicate how well your outsourcing partner is doing, evaluate the software team’s productivity, and help optimize processes within project development.
KPIs allow you to work with facts, not opinions.
KPIs enable improvement because you can only improve what you can measure.
You should measure KPIs at various levels, such as the company as a whole, a specific project, or an individual software engineer.
When seeking help from a development agency, choose those who reduce business and software assumptions to straightforward KPIs.

If you’re looking for help determining KPIs for your product or need clarification on any of the above indicators, contact us! We’ll gladly assist you.

Meanwhile, if you’d like to learn more about the software engineering outsourcing process, check out our articles on How to prepare for sports betting mobile app development?, How to cut the costs of app development? and How to verify if the project’s progress is going in the right direction?

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