Intermediate Vocabulary #growth#aarrr#ab-testing#metrics

Growth Engineering Vocabulary

5 exercises — Practice the terms growth engineers use every day: AARRR metrics, experimentation, engagement, viral growth, and conversion funnels.

Core Growth Engineering vocabulary clusters

AARRR funnel: acquisition, activation, retention, revenue, referral
Engagement: DAU, MAU, DAU/MAU ratio, engagement rate, feature adoption
Experimentation: A/B test, holdout group, treatment group, statistical significance, uplift
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?

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?

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?

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?

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?

Vocabulary Reference

Key growth engineering terms and their definitions.

AARRR (Pirate Metrics): A five-stage framework for measuring product growth: Acquisition (users find you), Activation (first value experience / aha moment), Retention (users return), Revenue (monetisation), Referral (users invite others). Each stage has its own conversion rate; the weakest stage is the highest-leverage improvement target.
DAU / MAU: Daily Active Users and Monthly Active Users — counts of unique users who perform at least one meaningful action in a day or month. The DAU/MAU ratio (stickiness) measures how frequently monthly users return. A ratio above 0.50 indicates a highly habit-forming product.
North Star metric: A single metric that best captures the core value a product delivers to customers. All growth experiments and roadmap decisions are evaluated against it. A good North Star reflects user value (not just revenue), is hard to game in isolation, and serves as a leading indicator of long-term business health.
A/B test (experiment): A controlled experiment that randomly assigns users to a control group (existing experience) and one or more treatment groups (new feature or change). The causal impact of the change is measured by comparing a primary metric between groups after the experiment reaches statistical significance.
Statistical significance: A result is statistically significant when the probability of observing the measured difference by chance alone (the p-value) falls below a pre-set threshold (typically p < 0.05). It does not imply practical importance — a tiny effect can be statistically significant with a large sample size.
Uplift: The percentage improvement in a metric caused by a treatment, relative to the control. For example, if the control activation rate is 20% and the treatment activation rate is 21%, the uplift is 5% (relative). Uplift is the primary output used to decide whether to ship a change.
Holdout group: A segment of users permanently excluded from a set of experiments over a longer period (weeks or months). Used to measure the cumulative, long-term impact of multiple shipped changes compared to the product as it was at the holdout's creation date — distinct from a per-experiment control group.
Conversion funnel: A sequence of steps users must complete to reach a desired outcome (e.g., sign up → verify email → complete profile → first action → paid upgrade). Funnel analysis measures the conversion rate at each step, revealing drop-off points that are the highest-priority improvement opportunities.
Growth loop: A self-reinforcing cycle where user actions generate new users or deeper engagement: viral loops (users invite users), content loops (user-generated content attracts organic search traffic), and product-led growth loops (individual users bring in their teams). Loops compound over time; traditional marketing funnels are linear and do not.
Viral coefficient (K): The average number of new users generated by each existing user. K = (invitations sent per user) × (acceptance rate). K > 1 means the product grows exponentially without additional paid acquisition. Cycle time — how long one loop iteration takes — determines the speed of compounding growth.

← Back to Vocabulary Exercises