Strategy: North Star metric, growth loop, viral coefficient, conversion funnel
0 / 10 completed
1 / 10
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 / 10
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 / 10
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 / 10
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 / 10
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.
6 / 10
Reviewer: 'This PR introduces a new user onboarding flow. The key metric we're tracking is activation – specifically, the percentage of users who complete the first tutorial. Let's ensure this flow doesn't negatively impact that.' What does 'activation' primarily measure in this context?
'Activation' in growth engineering refers to the rate at which new users begin engaging with the core value of your product. It's about measuring whether users are converting from sign-ups into active, committed users – not just acquiring accounts. Option A is an acquisition cost, option B is simply user count, and option C is a related but less precise metric.
7 / 10
Growth Engineer (to team): 'Okay, the cohort analysis shows users acquired through our latest influencer campaign have a significantly lower retention rate. We're seeing high churn within the first week. It seems like they weren't finding value quickly enough.' What is the *primary* concern highlighted in this message?
The message focuses on a drop-off in retention *after* initial engagement. This strongly suggests users aren't finding immediate value from the influencer's promotion or that the onboarding process wasn't effectively guiding them to this value. Options A and D are tangential concerns; option B is the core problem.
8 / 10
API Response (from a user analytics dashboard): `{"metric": "daily_active_users", "value": 75000, "segment": "mobile_users", "time_period": "last_7_days"}`. What does the 'segment' field in this response indicate?
The 'segment' field specifies that this data relates to *mobile* users specifically. This allows for granular analysis and comparison – understanding mobile user behavior separately from other device types is crucial for targeted growth strategies. The API response provides segmented metrics, not just aggregate ones.
9 / 10
PR Description: 'Implementing a new referral program to incentivize existing users to invite their friends. We'll track the conversion rate from invitation to signup and the number of referrals generated per user. A higher referral coefficient will indicate a successful implementation.' Which metric is *most* directly tied to the success of this referral program?
The PR description explicitly mentions tracking the 'conversion rate.' This is the crucial metric – it measures the percentage of invitations that actually result in new users signing up. While all options are relevant to the program's overall success, conversion rate is the most direct indicator of its efficiency.
10 / 10
Growth Engineer (to team): 'We ran an A/B test on the homepage button color. The treatment group – with the blue button – saw a slight increase in click-through rates to the signup flow. However, activation rates didn't improve significantly. It's worth investigating if the new colour was distracting.' What is the *primary* question this engineer should investigate?
The engineer is specifically noting a lack of impact on *activation*. This means the core problem isn't simply increased clicks but whether those clicks translate into users actually starting to use the product. Option A is too broad; option C focuses on the experiment itself, and option B is an indirect consequence.
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