The global market for product engineering services (PES) is not only big; it’s growing quickly. Recent research puts the market at about USD 1.26 trillion in 2024, with predictions going up to USD 1.81 trillion by 2030. Grand View Research Another source says that the amount will grow from about $1.38 trillion in 2025 to $2.64 trillion by 2032, with a CAGR of 9.7%. Fortune Business Insights
To put it another way, companies that offer product engineering services – like hardware, software, embedded systems, and product life-cycle services – are working in a field where demand is high, and things are getting more complicated. For a product engineering company and its clients, this means that success isn’t just about finishing projects or writing a lot of code. It’s about giving real value. And you need the right metrics to keep track of that value.
In this blog post, we’ll explain why traditional engineering metrics aren’t enough. Then We’ll list the most important product engineering metrics and show how a product company can use them in real life. Finally, we’ll give you tips on how to use these metrics well. At the end, you’ll know exactly how to define success in product engineering services in a way that makes sense.
Why Traditional Metrics Aren’t Enough
You can still hear people in a lot of engineering companies talk about velocity (how many story points were completed), lines of code, pull requests, and deployments. These seem familiar and easy to count. The problem is that they don’t tell the whole story.
- There is no customer experience. You might be able to deploy code quickly, but does the product make the user happy? Does it help them? Code churn doesn’t make sure that something will be useful or have an effect.
- People don’t pay enough attention to operational efficiency. Are teams always putting out fires? Are there extra costs for maintenance or technical support that aren’t obvious? That isn’t shown by traditional output metrics.
- There is no connection between how well a product works and how well a business does. A product can be delivered on time, but if it crashes a lot or doesn’t meet key performance indicators (KPIs) like user growth or revenue, it hasn’t really worked.
- People don’t care about engineering health or long-term sustainability. Standard dashboards often don’t show metrics like technical debt or maintainability, but they are what will decide if you can scale or iterate.
What this really means is that if you only think about “did we ship?” or “how many commits did we make?” you might miss whether you shipped something useful, usable, maintainable, and valuable. The next step is to switch to metrics that connect engineering output to user outcomes and business value.
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Key Product Engineering Metrics That Matter
Let’s go over the metrics that are important to keep track of. I’ll explain what each one means, why it matters, and how a company that offers product engineering services might use it.
Lead Time for Changes
- Definition: The time it takes to go from a code commit (or starting work) to deploying it in production.
- Why it is important: A shorter lead time means the team can work faster, take feedback, try new things, and make changes. In today’s world of product engineering, flexibility is often needed.
- How to use: Keep an eye on this over time. Find the things that are slowing things down (like delays in code review, QA, or the environment). A company that provides services might promise customers a “time-to-market” improvement and use this measure to see how well they are doing.
Change Failure Rate
- Definition: The percentage of deployments or changes that fail (rollback, hot-fix, outage) or need to be fixed right away.
- Why it is important: It’s a sign of stability and trust. High speed doesn’t mean much if every deployment goes wrong.
- How to use: Check by deployment batch or release. For clients in embedded systems (like when you update the firmware on a device), keeping the failure rate low builds trust and lowers the cost of fixing things in the field.
Mean Time to Recovery (MTTR)
- Definition: The time it takes for a team to fix a system failure or outage and get things back to normal.
- Why it is important: Many products, especially those in the Internet of Things, industrial, or embedded fields, need to be up and running all the time. The less time it takes you to get better, the less risk and cost.
- How to use: Make sure that incident tracking is part of your service agreements for product engineering support. Tell clients what your average MTTR is and how you can make it better.
Customer Satisfaction (CSAT) or Net Promoter Score (NPS)
- Definition: CSAT is a measure of how users (or client stakeholders) feel about the product or service, and NPS is a measure of how likely they are to recommend it.
- Why it is important: In the end, engineering is for people or business results. No amount of code commits matter if users are unhappy.
- How to use: After delivery (and every so often after that), ask the client’s end users or stakeholders how things are going. For a service company like Silicon Signals, getting feedback from your clients’ CXOs or product managers helps you figure out how much of an impact you’ve had.
Usage Analytics
- Definition: Information about which product features are used, how often, by whom, and whether usage is increasing.
- Why it is important: You wasted time if you make features that no one uses. Usage analytics link engineering work to actual user adoption.
- How to use: Add instrumentation to the product. For instance, if you make a hardware-software system for a client, you can use telemetry or logging to show how important parts are used. Use this to suggest changes for future versions in the post-delivery review.
Technical Debt Ratio
- Definition: The ratio of “quick fix” code, temporary workarounds, deprecated modules, or components that aren’t being maintained to clean, maintainable code. Sometimes it’s measured by the number of issues that need to be fixed, the complexity of the code, or the fact that there aren’t enough automated tests.
- Why it is important: Technical debt makes it harder to develop in the future, raises the risk of bugs, and costs more. In product engineering services, keeping debt low means being able to deliver iterations more quickly and reliably.
- How to use: At the start of the project, figure out how much debt there is from previous versions (if there is any). Use code review metrics, static analysis, or internal audits to figure out how much debt there is during development. Tell clients, “We added X new features and kept debt at Y”.
Deployment Frequency
- Definition: The number of times the team sends code (or firmware, or releases) to production (or field devices) in a certain amount of time.
- Why it is important: Frequent, reliable releases show that the process is mature, the automation is good, and the company is responsive. For product engineering services, being able to show that you can make small changes is a plus.
- How to use: Keep track of how many successful deployments of new releases there are each week or month. This could mean OTA updates or incremental firmware releases for hardware-software products.
How are Metrics applied?
Let’s see how this might work in real life by looking at two examples: one that is common in the industry and one that is specific to a company like Silicon Signals.
Take a look at India’s engineering research and design (R&D) field. A high-level employee at a consulting firm said that using generative AI in engineering and R&D cut development cycles by as much as 20%. This is basically an improvement in lead time, which shows that measuring and lowering time-to-value is real and important. In the context of product engineering services, this means that your clients want you to show them numbers like these.
Silicon Signals
Let’s say Silicon Signals is making a custom embedded vision system (hardware + firmware + application) for a client. Here are some ways you could use the metrics:
- Before starting, talk to the client about the target lead time for delivering a feature (for example, four weeks from spec to deployed module) and the target change failure rate (less than 5% of releases need to be rolled back).
- During development, keep an eye on lead time every week, keep track of any problems that cause rollback or remediation, and write down the MTTR for any test-lab failure.
- After the release, usage analytics on field devices to show that 70% of users engage the vision module and that 90% of users keep using it after three months.
- Ask the client’s product manager, “On a scale of 1 to 10, how satisfied are you with our delivery and impact?” Let’s say the score is 9.2
- Internally measure technical debt by looking at things like code coverage, the number of known design problems that still need to be fixed, and the refactoring backlog. Show that you kept your debt within an acceptable range and plan the next phase based on that.
- Use deployment frequency to show that you sent out small firmware updates every month (three in a quarter) instead of one big one. This shows that you are flexible.
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Silicon Signals show that they are not only giving the client “everything they asked for” by reporting these metrics in a roll-up dashboard to the client and internally. They are also doing it quickly, reliably, with high adoption and low risk. This is a clear definition of success in a product engineering engagement.
How to Use These Metrics Effectively
You can only get useful information from metrics if you use them correctly. Keep this in mind.
- Context matters: You can’t learn much from just the numbers. What is your base? What kind of business or feature is this? Look at things over time instead of just one picture.
- Avoid punishing teams: Metrics should help them get better, not hurt them. If you only reward speed, you’ll encourage people to rush and cut corners, which will lead to more failures or technical debt.
- Tie metrics to outcomes: Ask, “How does this metric help us reach our product goals?” For instance, a shorter lead time leads to a faster feedback cycle, which leads to more feature adoption, which leads to happier customers, which leads to more revenue or retention. Make the chain clear.
- Automate tracking where possible: Use tools like CI/CD dashboards, telemetry platforms, bug/incident trackers, and usage analytics platforms to collect data. Tracking by hand is prone to mistakes and won’t work on a large scale.
- Review and adapt: Metrics should change over time. Things that were important to you at the beginning may not be important later. For instance, after you’ve cut lead time by a lot, you might want to look at technical debt ratio or feature adoption metrics instead.
- Communicate with stakeholders: Make sure that both clients and internal leaders know what you’re measuring and why. Don’t use technical language; instead, explain what the metric means for them (like saving money, getting to market faster, or making users happy).
- Balance short-term and long-term: It’s easy to show metrics like “deployment frequency increased this sprint,” but long-term health is just as important: reliability, maintainability, and user loyalty. Things like the technical debt ratio or MTTR show that.
Conclusion
If you are working or managing a product engineering services company (or are thinking about hiring one), you should know that success isn’t measured by how many lines of code you wrote or how many deployments you made. What matters is how quickly you delivered real value, how well your product works, how users interact with it and feel about it, and how long your engineering processes will last.
You can get a full picture of product health, team efficiency, and business impact by keeping an eye on metrics like lead time for changes, change failure rate, mean time to recovery, customer satisfaction/NPS, usage analytics, technical debt ratio, and deployment frequency.
For a product engineering company like Silicon Signals, using these metrics and being open about them with clients makes you a partner who not only finishes projects but also gets results. That’s what makes product engineering services successful.
