Strategy: North Star metric, growth loop, viral coefficient, conversion funnel
0 / 5 completed
1 / 5
A growth PM presents the AARRR framework to new team members: "We track five stages — Acquisition, Activation, Retention, Revenue, and Referral. Right now our biggest leak is between the first and second stage: users sign up but never reach their aha moment. We need to fix that before spending more on paid ads."
Which AARRR stage is described as the moment a user first experiences the core value of the product?
Activation is the second stage of AARRR (also called Pirate Metrics). It marks the moment a newly acquired user first experiences the product's core value — the aha moment. Examples: Dropbox users who upload their first file; Slack teams that send 2,000 messages; Twitter users who follow 30 accounts. Activation is considered the highest-leverage stage because improving it affects every user who joins: poor activation means you are paying to acquire users who immediately churn. The full framework: Acquisition — how users find you (SEO, paid, referral); Activation — first value experience; Retention — users returning (D1/D7/D30); Revenue — monetisation (ARPU, LTV, MRR); Referral — users bringing others (viral coefficient).
2 / 5
An analyst reviews the engagement dashboard: "Our DAU is 120,000 and our MAU is 600,000. That gives us a DAU/MAU ratio of 0.20. Facebook targets 0.55+. We need to understand why users aren't coming back daily — it could be a habit-formation problem or a missing notification strategy."
What does the DAU/MAU ratio measure, and what does a higher ratio indicate?
DAU/MAU ratio (also called the stickiness ratio) measures how often monthly users return on a daily basis. Formula: DAU ÷ MAU × 100 = stickiness %. A ratio of 0.50 means a monthly user uses the product 15 days out of 30 on average. Benchmarks: social/messaging apps (Facebook, WhatsApp) target 0.50–0.65; productivity tools (Slack) often hit 0.40–0.55; e-commerce typically sees 0.05–0.10. Definitions: DAU (Daily Active Users) — unique users who perform at least one meaningful action in a 24-hour period; MAU (Monthly Active Users) — unique users active in the last 30 days. The definition of "active" is critical — opening the app is a weak signal; completing a core action (sending a message, creating a task) is stronger. A low DAU/MAU often indicates poor habit formation, weak notifications, or a product used only for occasional tasks (e.g., tax software).
3 / 5
A growth engineer describes an experiment setup: "We split users into two groups. The treatment group sees the new onboarding flow. The ________ group sees the existing experience and acts as our baseline for comparison. Any difference in activation rate between the two groups tells us the causal impact of our change — provided the split was random and the experiment ran long enough."
Which term correctly fills the blank?
Control group: the group in an A/B test that receives no change (or the current experience). It is the baseline against which the treatment group is measured. Without a control group you cannot attribute metric changes to your intervention — external factors (seasonality, product changes, market events) affect all users equally, so they cancel out when comparing treatment vs. control. Key experiment vocabulary: Treatment group — users who receive the new feature or change; Holdout group — a longer-term group excluded from a set of experiments to measure cumulative impact (different from control — it spans multiple experiments over weeks or months); Statistical significance — the probability that the observed difference is not due to chance (p < 0.05 is a common threshold); Uplift — the percentage improvement in a metric caused by the treatment (e.g., "3% uplift in activation rate"); Randomisation unit — usually user ID, ensuring each user always sees the same variant. Random assignment is essential: if the split is not random, the experiment is confounded and results are unreliable.
4 / 5
A growth lead presents the company strategy: "We have a single metric that every squad ties their experiments back to — it represents the core value we deliver to users. If that number goes up sustainably, revenue follows. We don't optimise for pageviews or sign-ups; we optimise for this one number, and everything else is a lever or a guardrail."
Which term describes the single metric that best represents the value a product delivers to its users?
North Star metric: a single metric that captures the essence of the value a product delivers. All squads, experiments, and roadmap decisions are evaluated against it. Examples: Airbnb — nights booked; Spotify — time spent listening; Slack — messages sent within a team; Facebook — daily active users; Duolingo — daily active learners. Properties of a good North Star: it reflects customer value (not just business value); it is leading, not lagging (it predicts revenue, not just reports it); it is hard to game in isolation. Common pitfalls: choosing a vanity metric (total sign-ups — easy to inflate via paid ads without delivering real value); over-optimising a proxy that diverges from true value. Related terms: Guardrail metric — a metric that must not degrade while optimising the North Star (e.g., don't increase engagement at the cost of support ticket volume); Input metric — a lever teams control to move the North Star (e.g., onboarding completion rate); Counter metric — a secondary metric monitored to catch regressions.
5 / 5
A growth engineer explains the referral programme metrics: "Each existing user sends an average of 4 invitations per month, and 30% of those invitees sign up and become active. That gives us a viral coefficient of 1.2. Any coefficient above 1.0 means the product grows on its own — every 100 users generate 120 more, who generate 144, and so on. The cycle time matters too: if the loop takes 3 months to complete, growth is slow even with K > 1."
What does a viral coefficient (K) greater than 1.0 indicate?
Viral coefficient (K): K = (average invitations sent per user) × (invitation acceptance rate). K > 1 means each user generates more than one new user on average, leading to exponential growth. K = 0.3 is common in non-viral products (users grow linearly, requiring continuous acquisition spend). K = 1.2 is viral. Formula example: 4 invitations × 30% acceptance = 1.2. Cycle time matters: K = 1.5 with a 1-week cycle grows far faster than K = 1.5 with a 6-month cycle. Most consumer products have K < 1 and rely on a mix of paid acquisition, SEO, and word-of-mouth rather than pure virality. Growth loop: a broader concept — a self-reinforcing cycle where user actions generate more users or more engagement. Types: Viral loop — users invite users (WhatsApp, Dropbox); Content loop — user content attracts organic search traffic (Quora, Pinterest); Product-led growth (PLG) loop — individual users discover the product, love it, and bring in their teams (Slack, Figma, Notion); Data loop — more users improve the product's ML model, attracting more users (Spotify recommendations). Loops compound; funnels are linear. Building a loop is what separates category leaders from ordinary businesses.
What does the "Growth Engineering Vocabulary" vocabulary exercise cover?
This exercise tests real IT vocabulary related to growth engineering vocabulary through 5 multiple-choice questions, each built from realistic workplace sentences rather than abstract definitions.
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How is this different from reading a glossary or blog article?
Exercises like this one are active recall drills — you have to choose the correct term or phrasing yourself, which builds retention faster than passively reading a definition.
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