Don’t just ship it. Study it.

Launching is only the beginning.

You’ve just launched a new product or feature. The press release is out, the Product Hunt upvotes are rolling in, and sign-ups are spiking. It’s tempting to declare victory. But initial popularity can be deceiving. Think of a movie that dominates opening weekend only to fade from memory weeks later, or a fitness app that gains thousands of users quickly, only to lose most of them within months.

These products appeared successful at first, but they failed to stick. In fact, one study found the average mobile app loses 95% of its daily active users within 90 days of launch: a sobering reminder that a big launch day does not guarantee a lasting product. The real work begins after you ship. Post-launch, the question isn’t “Did we launch on time?”. It’s “Are users finding real value, and how do we know?”

The fallacy of “ship it and forget it”

In startup culture, there’s an almost mythical belief that once you ship a product, the hard part is over. This mindset is a fallacy. Launching is not a finish line. It’s the start of a continuous learning cycle.  

Even at world-class companies, a huge portion of new ideas flop when exposed to users. According to Medium, Former Microsoft VP Ron Kohavi revealed that at Airbnb about 92% of product experiments failed to improve the metrics they targeted (Microsoft saw ~66% fail, Amazon ~50%).

In other words, most new features don’t deliver the impact their teams expected. If you “ship it and forget it,” you might be patting yourself on the back for a release that isn’t actually working for your customers.

Metrics that matter: Vanity vs. Value

Shipping fast is great, but what are you measuring after you ship?

Too often, teams cling to vanity metrics, numbers that look good on a dashboard but don’t translate into real value or insights. As product evangelist John Cutler quips, “Vanity metrics make us feel good but don’t help us do better work or make better decisions.” They “put optics before rigor, learning, and transparency”.

In other words, metrics like raw sign-ups, page views, or downloads might give you a warm fuzzy feeling (or something impressive to show the board), but on their own, they tell you little about whether your product is succeeding.

The metrics that truly matter are those tied to user value and outcomes, sometimes called actionable metrics or value metrics. For example, instead of boasting about “10,000 downloads,” you might track how many users actually activated (i.e. completed a key action that represents getting value from your product). A high download count (a vanity metric) is meaningless if most users abandon the app after one try. Likewise, “time spent in app” can be a misleading vanity metric if your goal is to help users save time.

Value metrics focus on what users are doing with your product (retention, engagement depth, conversion to paid plans, task success rate). Vanity metrics often lack context or intent (e.g. “registrations are up!”) and do not guide action or learning.

To avoid the vanity trap, define what success really looks like for your product before you launch. Is it a higher conversion rate in your onboarding funnel? A lower churn rate after 30 days? Faster task completion for users? Make those your north stars. If a metric isn’t actionable, if seeing it go up or down wouldn’t change your next step, then it’s probably vanity.

Beyond GA: The case for behavioral analytics

It’s the difference between knowing “500 people didn’t complete checkout” and knowing “most of them got stuck on the payment info page because the promo code field was broken.”

Another post-launch mistake is relying on the wrong tools to understand your users. Many teams default to Google Analytics (GA) for post-launch analysis. GA is a powerful tool for web traffic and marketing metrics. It tells you about pageviews, bounce rates, and campaign UTM performance. But GA alone won’t tell you the full story of in-app user behavior. It’s like trying to read a novel through a keyhole.

Behavioral analytics tools (like Amplitude, Mixpanel, or FullStory) have emerged to fill this gap, giving product teams a richer view into what users are doing inside the product. Behavioral analytics means moving beyond aggregate page stats to understand user journeys, flows, and friction points on a granular level.

Investing in these deeper analytics pays off. You gain the ability to segment users by behavior (e.g. users who use feature X vs. those who don’t), track cohorts over time, and analyze retention in meaningful ways. You can even integrate these tools with your data stack to close the loop (more on that next).

Closing the feedback loop

Data and analytics by themselves don’t move the needle – it’s what you do with the insights that drives improvement. Post-launch learning must translate into action. This means setting up a tight feedback loop: observe user behavior, identify opportunities or problems, then rapidly iterate the product or experiment with solutions.

It sounds obvious, but many teams stumble here. They collect metrics and maybe even identify issues, but fail to act on them quickly (or at all). To truly benefit from “studying” your launch, you need to bake iteration into your process: insight → decision → new experiment/feature → and back again.

Let’s say your behavioral analytics show 60% of users who click “Sign Up” never complete the registration. That’s a huge drop-off. Closing the loop might mean digging into session replays to see why (maybe the password requirements are deterring people), then launching a quick A/B test with a simplified sign-up form. If the change improves completion rate, you’ve just turned an insight into a product win. If it doesn’t, you’ve still learned something and can try a different approach.

Some of the most successful product teams in the world attribute their growth to this relentless cycle of experimentation. According to Harvard Business Review, Microsoft’s Bing team, for example, uncovered a $100 million revenue opportunity from a low-priority idea only because an engineer decided to test it post-launch. That simple A/B experiment (tweaking the way ad headlines were displayed) unexpectedly increased revenue by 12% almost overnight.

Remember, a launch is essentially an opportunity to learn in the real world. Make sure your team is primed to seize that.

As soon as that feature is live, be in learning mode: What are users doing? Where are they dropping off? What unexpected things are they using (or not using)? Approach it with curiosity. Then take those insights and immediately feed them back into improving the product. That’s how you turn a one-off launch into a continuous cycle of growth.

To sum up: Rethink how you measure product impact

In the startup world, there’s an emotional high that comes with shipping something new. Enjoy that moment, but don’t bask in it too long. The real success of a product isn’t determined at launch, it’s determined in the days, weeks, and months that follow. It’s determined by how real users respond and how your team adapts.

So, the next time you launch a feature or product, challenge yourself and your team to go beyond the ship. Set up the analytics that will tell you not just what happened, but why. Define what success looks like in measurable terms and track it religiously. If something isn’t working, acknowledge it and iterate. Share these findings with your whole team, so everyone learns.

Don’t just ship it and leave it: study it, learn from it, and keep improving. Your users (and your bottom line) will thank you for it!