Choosing success metrics: What to measure

You know that sinking feeling when you launch a feature and realize you have no idea if it's actually working? I've been there. We spent three months building what we thought was a game-changing onboarding flow, only to discover we'd been tracking page views instead of actual user activation.

That's the thing about success metrics - pick the wrong ones and you're essentially flying blind. But get them right, and suddenly every product decision becomes clearer, every experiment more focused, and every team discussion more productive.

Understanding the importance of success metrics

Success metrics are basically your product's scoreboard. They tell you if you're winning or losing, and more importantly, why.

The tricky part? Choosing metrics that actually matter. I've seen teams obsess over daily active users while their revenue tanks. Or celebrate reduced support tickets while missing that users stopped complaining because they stopped caring. One Reddit discussion nailed this challenge - sometimes the most important outcomes are the hardest to measure immediately.

The best metrics do three things:

• Show you're solving real user problems

• Connect directly to business health

• Change based on actions you can actually take

Take user delight, for instance. Another product manager on Reddit pointed out that some features exist purely to make users happy, not to drive revenue. In those cases, you might track sentiment scores, feature adoption rates, or time spent in the experience - whatever captures that "this is awesome" feeling.

The key is picking metrics that tell a story about your product's impact. Not just numbers on a dashboard, but indicators that guide your next move.

Principles for selecting the right success metrics

Here's a framework that's saved me countless metric debates: the DEM principle. Your metrics should be defendable (you can explain why they matter), explainable (your intern gets it), and meaningful (they drive real decisions).

Let me show you what this looks like in practice. Walgreens completely changed their game by switching from revenue per store to profit per customer visit. Suddenly, stores stopped competing for size and started optimizing for efficiency. One metric change transformed their entire business model.

The balance between user happiness and business needs is always tricky. I've found that pairing metrics helps:

• Track NPS alongside revenue per user

• Monitor feature adoption with support ticket volume

• Measure engagement depth, not just frequency

As one UX designer discovered, the metrics that matter most often emerge from talking to actual users. They'll tell you what success looks like to them - you just have to listen.

Platform products face unique challenges here. Building infrastructure platforms requires metrics that capture both sides of the marketplace. You're not just measuring user satisfaction; you're tracking ecosystem health. Think about metrics like time-to-first-integration, developer retention, and API reliability scores.

Implementing and tracking success metrics effectively

Setting up metrics without a plan is like buying a gym membership in January - good intentions, poor follow-through. Start by defining what problem you're solving before you pick any metrics.

I learned this the hard way at my last startup. We jumped straight to tracking everything, then drowned in dashboards nobody looked at. The infrastructure platform playbook suggests a smarter approach: map out user pain points first, then choose metrics that show you're addressing them.

Here's what actually works:

1. Pick 3-5 core metrics max (yes, really)

2. Set up automated reporting so you can't ignore them

3. Review and adjust quarterly - your metrics should evolve with your product

The teams that succeed treat metrics like a conversation, not a scorecard. They use quantitative data to spot trends, then dig into qualitative feedback to understand why. One product team shared how they combine usage analytics with community feedback to get the full picture.

Regular check-ins keep metrics relevant. Every quarter, ask yourself: are these numbers still driving the right behavior? If not, it's time to evolve.

Overcoming challenges in choosing success metrics

The biggest trap I see? Teams picking metrics that look good in presentations but provide zero actionable insights. You need leading indicators - metrics that give you time to course-correct, not just document failure.

This Reddit thread highlights a common struggle: finding simple, measurable metrics that actually predict success. The solution isn't always obvious. Sometimes you need proxy metrics - if you can't measure long-term retention immediately, track early engagement signals that correlate with it.

Watch out for metric manipulation too. Edmond Lau's analysis shows how the wrong metrics create perverse incentives. I've seen support teams close tickets without solving problems just to hit resolution targets. Every metric can be gamed - design yours to make gaming them actually improve the product.

Your metrics strategy should grow with your product. Early on, you might focus on activation and retention. As you mature, shift toward efficiency and profitability metrics. Patrick Kua's framework for metric evolution has helped me navigate these transitions.

The teams at Statsig have found that combining quantitative metrics with qualitative insights creates a more complete picture. Numbers tell you what's happening; user feedback tells you why.

Closing thoughts

Choosing the right success metrics isn't about finding the perfect formula - it's about creating a feedback loop that makes your product better. Start simple, focus on metrics that drive action, and don't be afraid to change them when they stop being useful.

Remember: the best metric is the one that changes how you build.

If you're looking to dive deeper, check out Statsig's guide on product metrics or browse the product management discussions on Reddit for real-world examples. The community is surprisingly generous with sharing what's worked (and what hasn't).

Hope you find this useful! Now go measure something that matters.