How Product Management Can Use Engineering Metrics to Drive Better Delivery Outcomes

Let's be honest—as product managers, we're obsessed with user metrics and business KPIs. But here's what I've learned after years of working with engineering teams: the PMs who really nail delivery are the ones paying attention to engineering metrics too.

Think about it. You can have the most brilliant product strategy in the world, but if you don't understand how your team actually builds and ships features, you're flying blind. These technical indicators aren't just "engineering stuff"—they're your secret weapon for better sprint planning, realistic stakeholder conversations, and ultimately shipping products that don't break.

The Metrics That Actually Move the Needle

Here's where I focus my attention, and trust me, these out of the box Allstacks metrics will change how you think about delivery:

Lead Time and Cycle Time are like your GPS for predictability. Lead time is the whole journey—from "wouldn't it be cool if..." to users actually clicking buttons in production. Cycle time is just the active building part. When your cycle times are consistent, you can actually give stakeholders realistic timelines instead of the classic "it depends" answer we're all guilty of.

Deployment Frequency tells you how nimble your team really is. The best teams I've worked with ship multiple times a day. Sounds scary? It's actually less risky than those massive monthly releases that keep everyone up at night. If you're deploying weekly or less, there's probably something in your pipeline that needs attention.

Change Failure Rate is your quality reality check. Good teams keep this under 15%. If yours is higher, don't panic—but do start asking whether you're rushing features out the door or if technical debt is catching up with you.

Developer Surveys can be absolute gold mines for understanding what's really happening. I run quick quarterly surveys asking things like “Do we have the tools to be effective?,  "Does the approach to scoping align with the team's skills and resources?" or “Is our release schedule allowing enough time for testing?" The answers often explain why your metrics look the way they do. Maybe that high change failure rate isn't about rushing—maybe it's because the team feels pressure to skip testing steps they know are important. Or perhaps your deployment frequency is low because developers are genuinely scared of the deployment process. These human insights give context to your numbers that you'd never get otherwise.

Making These Numbers Work for You

Now here's where it gets interesting. Velocity trends aren't just pretty charts—they're your early warning system. If velocity has been sliding for three sprints straight, something's up. Maybe it's time to tackle that technical debt before promising new features. On the flip side, when velocity is climbing, that's your green light to be more ambitious.

Code review times might seem like an engineering problem, but they directly impact your delivery timelines. Long reviews usually mean either your user stories aren't clear enough (ouch, that's on us) or knowledge is stuck in one person's head. Either way, it's fixable.

And here's one that surprised me: when bugs get discovered matters way more than how many you have. Finding issues during development? That's expensive but manageable. Finding them after release? That's when things get really costly and your users start tweeting angry things.

One metric that's been a game-changer for me is tracking intraprocess code bouncebacks, through with Allstacks' native view—basically how often code gets kicked back during the development workflow before it ever reaches users. At first glance, bouncebacks seem bad, right? Wrong. They're actually incredibly valuable because they catch issues early when they're cheap to fix.

Having Better Conversations with Stakeholders

This is where the magic happens. Instead of saying "the feature is delayed because of engineering stuff," you can say "our cycle time data shows this feature is 30% more complex than we estimated, which explains the delay." Suddenly you're not making excuses—you're providing insights. 

Another Allstacks view to use is Investment Intelligence.  Through Allstacks proprietary approach, you can know when the development team is working on targeted focus items and maybe more importantly when the team is being distracted into “other” work and to be able to make real time course corrections that will save the feature timeline! 

I love creating dashboards that show business metrics right next to engineering ones. When you can demonstrate that higher deployment frequency correlates with better customer satisfaction scores, or that reducing our change failure rate improves user retention, suddenly everyone cares about engineering excellence. It's not just developer happiness—it's business impact.

Don't Be That PM

Please, please don't use these metrics to evaluate individual developers. I've seen this go sideways so many times. These numbers tell you about your team's health and your processes, not whether Sarah is a better developer than Mike.

Focus on trends, not absolute numbers. And for the love of all things agile, consider context. A spike in cycle time during a major platform migration isn't the same as a spike during routine feature work.

Also, be careful about accidentally gaming the system. If you emphasize deployment frequency too much, you might end up with a bunch of tiny, meaningless releases that don't actually move the needle for users.

Where to Start

Keep it simple and use a “Crawl, Walk, Run” mentality. Pick lead time, deployment frequency, and change failure rate. Get baseline measurements and check them monthly alongside your product KPIs. You don't need fancy tools—Allstacks is always here to help.  With our baseline metrics capability, all of your teams can look at virtually every aspect of your teams to gain a full picture of what’s going well and where to lean in for some help.

Here's the thing: engineering metrics aren't just for engineers. They're product intelligence. The teams that figure this out become incredibly predictable, build unshakeable stakeholder trust, and create development practices that actually scale as they grow.

Start small, measure consistently, and prepare to have way better conversations about why things take as long as they do. Your engineering team will love you for it, and your stakeholders will finally understand what's really happening behind the scenes.