Guardrail metrics: The complete guide to balanced product growth

Did you ever go bowling on “easy-mode” as a kid, with guardrails that stop the ball from falling into the gutter? Or even as an adult, we don’t judge. 

No matter how wildly you throw the bowling ball, it bounces off the rails and stays in the lane. The guardrails protect it from going too far off-track, so that kids can enjoy the game without getting frustrated every time they strike out. 

Guardrail metrics are just like that (minus the smelly bowling shoes). They’re metrics that digital companies use to safeguard experiments and protect product teams from accidentally sending their product off-course while they conduct A/B tests and experiments. A negatively-impacted guardrail metric is an early sign that an experiment is having unintended consequences in other parts of the product, so you can evaluate if it’s actually performing as hoped (and pause it if it isn’t). 

Guardrail metrics (sometimes called counter-metrics) are secondary metrics that you monitor while conducting experiments and A/B testing. A test can appear successful at first glance, but have an unintended impact on other aspects of your product and user experience. Using guardrail metrics during A/B testing gives you fast and easy insight into the broader impact of an experiment. 

Let’s look at an example. 

Let’s say you’re Instagram, and you want to increase how many Stories users watch. You make a small change and A/B test to see if it boosts engagement. At the same time, you don’t want increased engagement with Stories to lead to a decrease somewhere else, like the main feed, so you choose engagement with the main feed as a guardrail metric. 

If engagement with Stories goes up while engagement with the main feed stays steady, you can start to consider your experiment a success. But if an increase in engagement with Stories means engagement with the main feed decreases, that’s a signal that you need to dig deeper before implementing the change. Using a guardrail metric can save you from making changes that are costly or detrimental over time.

Having guardrail metrics in place fosters experimentation without fear. Product teams can test theories without worrying that they’ll accidentally break something and not realize it. 

In a broader sense, guardrail metrics also limit risk for the company and product as a whole, since they serve as an early warning of negative impact. They allow product teams to gain more visibility and be more confident in the results that they get, knowing that their impact has been measured more thoroughly. 

Guardrail metrics can also improve cross-team collaboration, both between product teams and with other teams across functions. If you’re worried about a product change having a ripple effect on marketing efforts, for example, you can set up guardrail metrics that will monitor for those changes. This ensures that one team’s successes don’t cost another team their own KPIs.

North star metrics are guiding metrics that a company uses to determine long-term strategy. They serve to get the company aligned and working towards a shared goal. North Star metrics can change, but they’re intended as overarching metrics that guide companies over time. 

For shorter-term projects and goals, companies will choose primary metrics to monitor success—some teams also use "The One Metric That Matters" (OMTM), a concept from the book Lean Analytics. Primary metrics, or OMTM (depending on which framework you’re using), are useful for specific projects and timeframes, usually a few months. 

Guardrail metrics are secondary metrics. If primary metrics tell you if a project is successful, secondary metrics help you see the broader context and measure potential unintended impacts that your experiment can have on your product. 

The priority is to select metrics that are important for product and business performance. Otherwise, it wouldn’t matter that an experiment affects them. You can choose two or three guardrail metrics for an experiment, but don’t go wild either—tracking too many guardrail metrics will increase the risk of false positives. 

When choosing guardrail metrics, you can think about what an experiment is likely to affect, like we mentioned in the previous section, where engagement with feature X harmed engagement with feature Y. 

You can also consider overall product health metrics and include those as guardrail metrics (since you never want to impact those negatively, even on a small scale). 

Technical metrics are a good measure of unintended technical repercussions. For example, a streaming platform will probably steer clear of changes that impact page loading times, even if they have other positive effects. Slow loading creates frustration for users trying to watch a movie or listen to music. 

Let’s look at how a couple of tech giants use guardrail metrics to keep their own businesses on track. 

Airbnb uses what they call an “Experiment Guardrails Framework” to monitor and prevent negative impact on key metrics while they experiment and conduct A/B tests. 

At Airbnb, different teams focus on different outcomes: “When making launch decisions, each team is often focused on different evaluation criteria — for example, the Trust team prioritizes Fraud Identification, while the Experiences team may prioritize discovery of the Online Experiences product in our Homepage,” their tech blog explains. “Experiments that positively impact one team’s metrics can also harm another team’s metrics, and it’s not always obvious how to weigh these trade-offs — for example, if house rules are not displayed in Checkout, we might see an increase in bookings but lower ratings.” 

To mitigate risk, Airbnb developed their Experiment Guardrail system. If an experiment triggers a guardrail (harms a key metric that the company wants to protect), it goes through an escalation process to discuss results before proceeding. This allows Airbnb to foster experimentation while still protecting key growth and revenue targets. 

As teams at Spotify mature and get better at testing and iteration, they move from simpler to more complex experimentation. They apply the same philosophy to using guardrail metrics: 

“Guardrail metrics often require additional input from the experimenter, which can raise the barrier for those in the early stages of their experimentation journey. Luckily, it's possible to simplify the usage of guardrail metrics by lowering the requirements on rigor. This way, more experimenters can start benefiting from guardrail metrics earlier in their experimentation journey. Later on, they can move up the funnel and level up their practices,” as their blog explains. 

Guardrail metrics aren't just nice-to-have safety nets—they're essential for scaling experimentation responsibly. When you can confidently test new features while knowing you'll catch negative side effects before they impact users, you enable your team to innovate faster and take bigger swings.

Start with 2-3 guardrails that matter most to your product health. As your experimentation practice matures, you can add more sophisticated monitoring. The key is building that safety net now, so you're ready when your next big experiment opportunity comes along.

Mixpanel can help you track, monitor, and analyze all of your metrics in a single convenient platform, from your North Star to your guardrails. Understand your experiments in real-time, uncover new opportunities, and explore your data with Mixpanel today for free.