Learn vocabulary for discussing growth metrics: north star metric, guardrail metric, CTR lift, leading vs lagging indicators, and counter-metrics.
0 / 15 completed
1 / 15
What is a 'north star metric'?
A north star metric (NSM) is the one metric that best reflects whether the product is delivering value — for example, 'weekly active users who complete a booking' for a travel app. It guides prioritisation and experiment goals across teams.
2 / 15
What is a 'guardrail metric'?
Guardrail metrics protect important product health signals during experimentation. Even if a treatment improves the primary metric, the experiment is considered a failure if a guardrail metric moves significantly in the wrong direction.
3 / 15
What does 'we saw a 12% lift in CTR' mean?
A 'lift' refers to the relative improvement of a metric in the treatment group compared to the control. A 12% lift in CTR means: if the control had 10% CTR, the treatment had approximately 11.2% CTR.
4 / 15
What is a 'leading indicator' versus a 'lagging indicator'?
Leading indicators are forward-looking signals that predict what will happen — they move before the outcome does. Lagging indicators confirm what has already happened. Both are useful: leading indicators allow faster course-correction, while lagging indicators confirm true business impact.
5 / 15
What is a 'counter-metric' in experiment analysis?
Counter-metrics catch cases where a treatment improves the target metric by degrading something else. For example, if a new recommendation algorithm increases CTR but counter-metrics show decreased time-on-site and lower return rate, the 'improvement' may actually be harmful.
6 / 15
Sarah (Lead Data Scientist) posted in the #metrics Slack channel: 'We're seeing a significant drop in user activation. I think we need to investigate if our onboarding flow is too complex – maybe we should focus on reducing the number of steps.' What does Sarah *primarily* mean when suggesting a reduction in onboarding steps?
Sarah is directly referencing a growth metric (activation rate) and proposing a potential intervention – simplifying the onboarding flow – to address a negative trend. The key here isn't just understanding 'activation,' but recognizing that she's using it as a basis for experimentation and problem-solving. Option A is the opposite; options C & D are irrelevant to her stated concern.
7 / 15
You're reviewing a pull request for a new feature that integrates with a third-party analytics service. The PR description reads: 'This change aims to improve the conversion rate from free trial users to paid subscriptions by leveraging real-time data on user behavior.' What does 'conversion rate' in this context *most accurately* represent?
'Conversion rate' is a standard growth metric that specifically measures the percentage of individuals moving from one stage (free trial) to another (paid subscription). Option A is simply user count; option C focuses on spending, and option D relates to system usage. The PR description highlights the desired outcome – increased subscriptions – which aligns directly with this definition.
8 / 15
During a standup meeting, Mark (Product Manager) says: 'We're seeing a slight dip in daily active users, but our retention rate is holding steady. We'll monitor this closely.' What does Mark primarily intend to do with the information he's sharing?
Mark's statement emphasizes the importance of *correlation* between two growth metrics: Daily Active Users (DAU) and Retention Rate. He intends to monitor these together—a drop in DAU with stable retention suggests a problem elsewhere (e.g., user churn), prompting further investigation, as opposed to simply accepting the decline without analysis. Option A & D are premature actions; option B is tangential.
9 / 15
You've run an experiment to test a new call-to-action button on your website. The results showed a HTTP/198 20% increase in click-through rate (CTR). What does this result *primarily* indicate?
A 20% increase in CTR is a direct measure of improved user engagement with the new call-to-action. While visual appeal might be a contributing factor, it's not definitively proven by this single metric. A 'statistically significant' result would require further analysis (p-value). Option C is an assumption and D is incorrect.
10 / 15
A data analyst presents the following findings: 'Our bounce rate increased by 5% after implementing the new website design.' What does this finding *most accurately* suggest about the impact of the redesign?
A 'bounce rate' refers to the percentage of visitors who leave a website after viewing only one page. An increase in bounce rate indicates that users are not finding what they were looking for or are having difficulty navigating the site – suggesting problems with content or design choices. Option A is the opposite; options C & D are unrelated metrics.
11 / 15
Sarah (Lead Data Scientist) posted in the #metrics Slack channel: 'We're seeing a significant drop in user activation. I think we need to investigate if our onboarding flow is too complex – maybe we should focus on reducing the number of steps.' What does Sarah *primarily* mean when suggesting a reduction in onboarding steps?
Sarah is directly referencing a growth metric (activation rate) and proposing a potential intervention – simplifying the onboarding flow – to address a negative trend. The key here isn't just understanding 'activation,' but recognizing that she's using it as a basis for experimentation and problem-solving. Option A is the opposite; options C & D are irrelevant to her stated concern.
12 / 15
You're reviewing a pull request for a new feature that integrates with a third-party analytics service. The PR description reads: 'This change aims to improve the conversion rate from free trial users to paid subscriptions by leveraging real-time data on user behavior.' What does 'conversion rate' in this context *most accurately* represent?
'Conversion rate' is a standard growth metric that specifically measures the percentage of individuals moving from one stage (free trial) to another (paid subscription). Option A is simply user count; option C focuses on spending, and option D relates to system usage. The PR description highlights the desired outcome – increased subscriptions – which aligns directly with this definition.
13 / 15
During a standup meeting, Mark (Product Manager) says: 'We're seeing a slight dip in daily active users, but our retention rate is holding steady. We'll monitor this closely.' What does Mark primarily intend to do with the information he's sharing?
Mark's statement emphasizes the importance of *correlation* between two growth metrics: Daily Active Users (DAU) and Retention Rate. He intends to monitor these together—a drop in DAU with stable retention suggests a problem elsewhere (e.g., user churn), prompting further investigation, as opposed to simply accepting the decline without analysis. Option A & D are premature actions; option B is tangential.
14 / 15
You've run an experiment to test a new call-to-action button on your website. The results showed a HTTP/198 20% increase in click-through rate (CTR). What does this result *primarily* indicate?
A 20% increase in CTR is a direct measure of improved user engagement with the new call-to-action. While visual appeal might be a contributing factor, it's not definitively proven by this single metric. A 'statistically significant' result would require further analysis (p-value). Option C is an assumption and D is incorrect.
15 / 15
A data analyst presents the following findings: 'Our bounce rate increased by 5% after implementing the new website design.' What does this finding *most accurately* suggest about the impact of the redesign?
A 'bounce rate' refers to the percentage of visitors who leave a website after viewing only one page. An increase in bounce rate indicates that users are not finding what they were looking for or are having difficulty navigating the site – suggesting problems with content or design choices. Option A is the opposite; options C & D are unrelated metrics.
What will I practise in "Growth Metrics — Vocabulary"?
Learn vocabulary for discussing growth metrics: north star metric, guardrail metric, CTR lift, leading vs lagging indicators, and counter-metrics.
How many exercises are in this module?
This module has 15 multiple-choice exercises, each with instant feedback and a full explanation of the correct answer.
Is this exercise free to use?
Yes. Every exercise on CoderSlingo, including this one, is free to use with no account, sign-up, or paywall.
Do I need to create an account to do these exercises?
No account is required. Just click an option to answer — your score for this session is tracked automatically in the progress bar above.
What happens if I choose the wrong answer?
You'll immediately see which answer was correct, plus a full explanation covering the vocabulary and reasoning behind it — mistakes are where most of the learning happens.
Can I retry the exercises if I want a higher score?
Yes — use the "Try again" button on the results screen to reset and go through all the questions again.
Is my progress saved if I close the page?
No. Progress is tracked only for your current visit; reloading or leaving the page resets the counter. This keeps the exercise simple and account-free.
Where can I find more Growth & Experimentation Language exercises?
Browse the full Growth & Experimentation Language hub for related drills, or check the "Next up" link below to continue with a connected topic.
How is this different from reading an article on the same topic?
Articles explain vocabulary and concepts in prose; this exercise tests and reinforces that vocabulary through active recall with immediate feedback — the two work best together.
Who writes these exercises?
Every exercise is written by the CoderSlingo team, drawing on real workplace English used in IT roles, then reviewed for accuracy and clarity.