How to measure delivered business value by the engineering team in a high-growth startup

As a startup grows, particularly in the pre-seed, seed, and Series A stages, engineering teams face increasing pressure to scale rapidly while delivering value. However, too often, engineering output is measured through metrics like pull requests, lines of code, or the number of features delivered—metrics that offer little insight into the actual business impact of the engineering team.

I’ve created this article for CTOs and Engineering managers in high-growth startups to help them navigate the metrics they should focus on in order to measure delivered business value effectively, align the engineering team with the broader business goals, and ultimately drive meaningful outcomes.

Why measuring business value is more important than counting lines of code

Traditional engineering metrics like the number of pull requests, lines of code, or even the number of features delivered are easy to quantify. But these metrics are often detached from what truly matters—whether or not the work being done is driving the business forward. In high-growth environments, where resources are limited and impact is paramount, measuring the wrong things can lead engineering teams to prioritize "shipping more" over delivering the right value.

Here’s how this looks like: 

• Lines of code: Measuring lines of code encourages verbosity rather than efficiency. A thousand lines of code could signify new features, or it could be an indicator of complexity or technical debt. In startups, reducing code can be as impactful as adding it, especially if it simplifies the system and makes future changes easier.
• Number of features: Simply counting features delivered can encourage teams to ship anything without regard to whether those features meet customer needs or provide value. A more nuanced metric is how many customers use the new feature and how it impacts user satisfaction.

Instead of these traditional metrics, we should focus on business value metrics, which tie engineering output directly to key performance indicators (KPIs) that matter to the growth of the business. Here are a few examples:

• Revenue impact: Measuring how much revenue a feature contributes, or whether engineering efforts are reducing customer churn or increasing conversion rates.
• Customer acquisition and retention: Focusing on whether an engineering project helps acquire new customers or improve the retention rate.
• Cycle time to learn: Instead of measuring only cycle time to deploy code, look at how quickly the team can ship an experiment, measure results, and learn from it. This supports an agile approach that aligns with business needs.
• Cost  efficiency: Technical projects that result in reduced operational costs, such as infrastructure optimizations that save money, can be a significant value-add for early-stage companies.

Measure teams, not individuals

Another pitfall for high-growth startups is focusing too much on individual performance rather than the performance of the team. Engineering is a collaborative effort, particularly in environments that are fast-moving and require adaptability. Measuring individual productivity can foster unhealthy competition, create silos, and diminish the team’s overall ability to work cohesively. Here’s an alternative approach:

• Collaboration over individual output: A great feature often requires the collaboration of several engineers, designers, and product managers. Measuring business value at the team level encourages collaboration rather than creating incentives for individuals to act in isolation.
• Cross-functional outcomes: Successful engineering teams work in line with design, product, and other departments. Measuring individual contributions ignores the critical team dynamic required to deliver meaningful outcomes.
• Resilience and adaptability: By measuring team outcomes, you foster an environment where engineers support one another, reducing bus factors (where too much knowledge is isolated within one individual). This also allows teams to respond more flexibly to changes in requirements—a key factor for startup success.

Add business value to feature tasks

To effectively align engineering work with business outcomes, it is crucial to make the business value of every feature explicit. One way to achieve this is by adding a dedicated field to every feature task in your project management system—whether you’re using Jira, Linear, or another tool—where the business or dollar value for each feature is clearly defined. This allows the engineering team to understand the impact of their work on the broader business objectives. What are the benefits of doing this?

1. Transparency: By adding a business value field, you ensure that everyone in the team is aware of the significance of each feature in terms of revenue, customer acquisition, retention, or other business metrics.
2. Prioritization: When engineers can see the direct business value of a feature, it helps them make informed decisions during development, prioritize tasks that have the highest impact, and avoid spending time on low-value activities.
3. Motivation: Understanding the dollar value or business outcome associated with their work can be highly motivating for engineers, giving them a sense of purpose and a clear understanding of how their contributions are driving the company's success.

Metrics to track at each growth stage

Pre-seed stage

At a pre-seed stage, it’s all about validating an idea with minimal resources. The focus should be on learning, rapid iteration, and ensuring product-market fit. Here's what should you track:

• Cycle time to learn: Measure how quickly the engineering team can build and deploy experiments to validate hypotheses.
• Customer feedback loop time: Track how quickly you can gather user feedback and iterate on that feedback.
• Prototype completion: Focus on the delivery of prototypes that can be tested with real users. Here, speed matters more than anything, as you want to determine viability.

Seed stage

By the seed stage, startups are expected to show traction and validate the core product. The focus here is on improving user experience, enhancing product reliability, and initial retention. You should focus on:

• Retention metrics: Measure how well your features are retaining users. Engineering efforts should be directed towards enhancing stickiness and engagement.
• Scalable architecture investments: Begin to track work that aims to support growth, like reducing tech debt or increasing system reliability. An over-engineered solution can be a waste, but starting to track progress towards scalable infrastructure is key at this point.
• Speed of deployment: Track how quickly your team can safely deliver new features, with an emphasis on continuous delivery and integration. This directly impacts your ability to respond to customer needs.

Series A stage

At Series A, you need to focus on scaling both the team and the product. You have customers, you have traction, and now you need to scale. What matters here is:

• Reliability metrics: Measure system uptime, bug counts, and response times to incidents. Reliability is crucial as customer expectations grow.
• Cost to serve: Track the cost of supporting additional users, ensuring your infrastructure can scale cost-effectively.
• Business metrics alignment: Tie engineering metrics directly to business objectives, such as revenue growth, improved conversion rates, or NPS (net promoter score). For example, if a feature aims to drive a 10% improvement in user conversion, track whether your engineering work delivers on that goal.
• Team growth and efficiency: Measure the onboarding time for new team members. As the team grows, it's crucial to ensure new engineers become productive quickly without negatively impacting the existing team’s productivity.

How to measure things effectively

1. Define metrics that reflect company goals: Startups need to be focused on survival, and each engineering initiative should align with key company objectives. For instance, if the startup is focused on gaining market traction, engineering should be focused on customer-facing improvements, and the metrics should reflect impact on those objectives.

2. Use a mix of leading and lagging indicators: Lagging indicators like revenue and retention are important but can take time to manifest. Leading indicators, like improvements in onboarding experience, page load speed, or reduced bug count, can show early signs of progress towards broader business outcomes.

3. Regularly re-evaluate metrics: As the company grows, the metrics that make sense will change. The priorities at the seed stage are different from those at Series A. Regularly take stock of whether what you’re measuring still aligns with where the company needs to go.

4. Leverage OKRs (objectives and key results): Use OKRs to align the engineering team with company objectives. This framework helps ensure everyone is rowing in the same direction and that each engineering deliverable is linked to a broader business goal.

5. Encourage (regular) team reflection: Create a culture of retrospection, where the engineering team regularly looks at past work and assesses whether they delivered the intended business value, what worked well, and where improvements can be made.

Conclusion

In high-growth startups, engineering teams can be a powerful lever for delivering business value—but only if they’re measured in the right way. Moving beyond traditional engineering metrics like lines of code or the number of features shipped helps align the team with the company's mission and long-term goals. Instead, measuring delivered business value, focusing on team outcomes, and adapting metrics based on the company’s growth stage can drive impactful results.

Remember, what gets measured gets managed. Measuring the right things can create a shared sense of purpose, align engineering efforts with the business needs, and help you move from simply writing code to delivering meaningful impact.

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