5 exercises — DAU/MAU stickiness ratio, LTV/CAC/churn unit economics, cohort retention curves, NPS (Net Promoter Score), and OKR goal-setting vocabulary.
0 / 17 completed
1 / 17
In a product review, the team discusses: "DAU/MAU ratio is at 42% — what does that tell us?" A new engineer asks what DAU/MAU means and why the ratio matters.
DAU, MAU, and the stickiness ratio:
DAU (Daily Active Users) — unique users who perform at least one meaningful action in a single day MAU (Monthly Active Users) — unique users who perform at least one meaningful action in a 30-day period
DAU/MAU ratio (stickiness): Formula: DAU ÷ MAU × 100% • 100% = every monthly user visits every single day (impossible in practice) • 50%+ = extremely sticky (messaging apps, core productivity tools) • 25-40% = healthy consumer engagement (Facebook historically ~50-65%) • 10-20% = typical for many B2B tools • <10% = low engagement; most users are not forming a habit
What "active" means matters: Teams define what counts as "active" — this definition dramatically affects the metric: • Weak: "logged in" • Strong: "completed one core action" (sent a message, ran a query, closed a task) • The definition should reflect the product's North Star Metric
WAU (Weekly Active Users) — weekly variant; useful for products designed for weekly cadences (code review tools, weekly report generators)
Related vocabulary: • stickiness — how often users return; habit formation • activation rate — % of new sign-ups who complete the "aha moment" (first valuable action) • churned user — a user who was active but stopped using the product • resurrection — a churned user who returns and becomes active again
2 / 17
A growth dashboard shows: "Monthly churn: 3%. LTV: $1,200. CAC: $180. LTV/CAC ratio: 6.7." The CEO says this is healthy. Why?
LTV, CAC, churn — the unit economics vocabulary:
CAC (Customer Acquisition Cost) Total spend to acquire one paying customer: (sales + marketing spend) ÷ new customers acquired • $180 CAC = the company spent $180 in sales/marketing to get one paying customer
Churn Rate The percentage of customers who stop using the product each period. • 3% monthly churn = 3% of customers leave each month • Annual churn ≈ 1 - (1 - 0.03)¹² ≈ 31% (rule of thumb: monthly × 12 slightly overestimates) • Churn directly limits LTV: lower churn = customers stay longer = more revenue per customer
LTV (Customer Lifetime Value) Total revenue expected from one customer over their entire lifecycle. Simple formula: LTV = ARPU ÷ Churn Rate • Where ARPU = Average Revenue Per User per month • If ARPU = $36/month, churn = 3% → LTV = $36 ÷ 0.03 = $1,200 ✓
LTV/CAC ratio — the health benchmark: • <3: danger zone; you're losing money acquiring customers • 3:1: minimum healthy threshold • 3-5: solid • >5: excellent; strong margin and room to invest in growth • Too high (>10+): possibly under-investing in growth
Vocabulary: • unit economics — the revenue and cost metrics for a single customer • retention rate — inverse of churn; 3% monthly churn = 97% retention • ARPU — Average Revenue Per User (monthly) • payback period — CAC ÷ monthly ARPU; months to recover acquisition cost
3 / 17
A data analyst presents cohort retention data: "Week-1 cohort retained at 60%; Week-4 at 35%; Week-8 at 28%; Week-12 at 27% — the curve is flattening." What does this mean for the product?
Cohort retention curves — reading and interpreting retention data:
What is a cohort? A cohort is a group of users who signed up (or started) in the same time period — for example, "all users who signed up in January 2024."
The retention curve shape — what it tells you:
Declining then flattening (this example): • Many users drop off early (normal — discovering the product isn't for them) • A core group stabilises and remains long-term • Flattening = product has found its core audience • The flatline retention % is the "Long-Term Retention" benchmark
Still declining at the end (no flatline): • Indicates no retained core; even committed users eventually leave • Product does not have product-market fit yet • Engineering focus: find the "aha moment" and make it happen faster for more users
Reading the numbers in this exercise: • Week 1: 60% retained — roughly expected drop from new user spike • Week 4: 35% — typical early attrition • Week 8: 28% — slowing churn • Week 12: 27% — essentially flat; the retained core
These 27% are the power users. Understanding them — who they are, why they stay, what their workflow is — is the key to improving the product for the 73% who left.
Vocabulary: • cohort — a group of users sharing a starting time period • cohort retention curve — a chart showing % of a cohort who remain active over time • flatline — when the retention curve stops declining (found stable retained users) • D1/D7/D30 — retention at 1 day, 7 days, 30 days after sign-up • power user — a highly engaged user who uses the product deeply and frequently
4 / 17
A growth meeting agenda item: "NPS score dropped from 52 to 38 this quarter — we need to review our detractors." What is NPS and what do detractors, passives, and promoters mean?
NPS (Net Promoter Score) — the customer satisfaction vocabulary:
NPS was created by Fred Reichheld (2003) and became a standard business metric. The single survey question: "How likely are you to recommend [Product] to a friend or colleague? (0-10)"
Scoring segments: • Promoters (9-10) — enthusiastic fans; actively refer others; low churn • Passives (7-8) — satisfied but unenthusiastic; vulnerable to competition • Detractors (0-6) — unhappy; may churn; may actively warn others against the product
Benchmarks: • Above 0: more promoters than detractors (positive) • Above 30: good • Above 50: excellent • Above 70: world-class (Apple, Tesla range) • B2B SaaS industry average: ~30-40
A drop from 52 to 38: A 14-point drop is significant. Investigating detractor responses reveals the specific pain points. Common triggers: degraded performance, a major UX change, pricing change, competitor launches.
Vocabulary: • NPS (Net Promoter Score) — loyalty metric; % promoters - % detractors • CSAT (Customer Satisfaction Score) — satisfaction at a specific interaction: "How satisfied were you with this support response?" (1-5 or 1-10) • CSAT vs NPS: CSAT measures a transaction; NPS measures the overall relationship • churn survey / exit survey — asked when a customer cancels
5 / 17
A company's OKR for Q3: "Objective: Grow into the enterprise market. Key Result 1: Sign 5 enterprise logos (ACV > $50K). Key Result 2: Reduce enterprise onboarding time from 8 weeks to 4 weeks. Key Result 3: Achieve 90% CSAT for enterprise accounts." At quarter end, KR1: 3/5, KR2: 6 weeks (not 4), KR3: 88% CSAT. How should the engineering team read these results?
OKRs (Objectives and Key Results) — the goal-setting framework:
OKRs were popularised by John Doerr and adopted by Google, Intel, and most major tech companies.
The two components: • Objective — qualitative, inspiring, directional: "Grow into the enterprise market" • Key Results — quantitative, measurable, time-bound: specific numbers that prove the objective was achieved
The "moon shot" philosophy: Google's OKR guideline: if you consistently achieve 100% of your key results, your goals aren't ambitious enough. Target 70% completion as "healthy ambitious." 100% completion often means the bar was set too low.
Reading the results in this exercise: • KR1: 3/5 logos = 60% — below goal but meaningful progress; blockers worth understanding • KR2: 8→6 weeks = 50% reduction achieved (vs. 50% target reduction from 8→4) — directionally strong • KR3: 88% vs 90% CSAT = very close; 97.8% of target — essentially achieved
Overall: the direction is right. The quarter generated real enterprise signal. Retrospect on blockers, not blame.
OKR vocabulary: • Objective — qualitative direction-setting statement • Key Result (KR) — measurable milestone proving the objective • OKR cycle — typically quarterly (Q1-Q4) with annual objectives • stretch goal — an ambitious target designed to push the team beyond comfortable delivery • company-level OKR / team OKR / individual OKR — OKRs cascade through the organisation • OKR check-in — a regular (weekly or biweekly) review of OKR progress • KPI (Key Performance Indicator) — ongoing operational metrics (different from OKRs which are cyclical goals)
6 / 17
Product Manager Sarah: "Okay team, we've seen a spike in new user signups this week. Our Daily Active Users (DAU) are up 15% compared to last week. What does DAU tell us?"
DAU (Daily Active Users) measures how many unique users engage with your product at least once during a specific day. A 15% increase suggests growth in daily engagement, which is generally positive. While MAU (Monthly Active Users) provides a broader picture, DAU offers insights into the stickiness and habitual use of the application.
7 / 17
David (Senior Growth Analyst): "Hey team, we're seeing a significant drop in our weekly active users. The current DAU is 12k, down from 15k last week. What's the immediate implication of this trend?"
DAU (Daily Active Users) represents the number of unique users who engage with your product on a given day. While fluctuations can occur, a consistent downward trend like this warrants investigation – it suggests something is driving users away or that current engagement isn't sufficient to retain them. Simply dismissing it as 'normal fluctuation' risks overlooking critical problems.
8 / 17
As a Growth Analyst, you're reviewing the results of a new feature launch. The API response shows a 20% increase in daily active users (DAU) after the release. However, retention rates haven't significantly improved. Which metric is MOST important to investigate next?
What does DAU tell us?
DAU (Daily Active Users) measures how many users are actively using your product on a given day. While an increase indicates initial interest, it doesn't guarantee long-term engagement. The critical follow-up is to understand if the new feature led to improved retention – otherwise, the DAU surge might be temporary or driven by non-engaged users. This highlights the importance of looking beyond raw numbers.
9 / 17
You're working with a Slack channel discussing a recent product update. A developer asks: "Our LTV/CAC ratio is currently 3.5. What does this *really* mean for our business?" Which of the following best explains what the ratio represents?
What does LTV/CAC mean?
The LTV/CAC ratio is a key indicator of marketing and sales efficiency. It measures the return on investment (ROI) for acquiring new customers. A higher ratio (e.g., 3.5 or above) generally indicates that you're efficiently generating revenue from your customer base, suggesting a healthy business model – this means your customers are staying with you longer and spending more.
10 / 17
During a standup meeting, the Head of Growth says: "We're seeing a drop in our Net Promoter Score (NPS) from 68 to 52. What does this signal?" A junior developer asks what NPS is.
What is NPS?
NPS (Net Promoter Score) is a widely used metric that gauges customer loyalty and willingness to recommend your product or service. It's based on the question: 'On a scale of 0-10, how likely are you to recommend [Product/Service] to a friend or colleague?' The result is categorized into Promoters (9-10), Passives (7-8), and Detractors (0-6) – understanding these segments is crucial for growth.
11 / 17
You're reviewing a PR description for a new feature aimed at enterprise clients. The description includes the following: "We've implemented a new API endpoint to allow seamless integration with Salesforce. Initial data suggests a 30% increase in user engagement among our enterprise customers, measured by DAU. However, we need to monitor cohort retention closely.". Which action should be prioritized next?
What's the most important thing to track?
While a 30% increase in DAU is positive, it's crucial to understand *why* this change occurred and whether it's sustainable. Monitoring the LTV/CAC ratio among enterprise clients is paramount because it directly reflects the financial impact of the new feature – a drop here would be a major red flag.
12 / 17
Growth Analyst Mark: "Our new feature's API endpoint has triggered a spike in DAU – up 25% compared to the previous week. However, we're not seeing an immediate improvement in user retention. What is the most important next step for understanding this situation?"
The scenario highlights a common situation: new user acquisition without corresponding retention. Analyzing cohorts allows you to pinpoint which segments are driving the DAU increase and identify potential issues impacting retention *within those specific groups*. This is crucial for targeted intervention.
13 / 17
Senior Analyst David: "We've launched a new feature aimed at small businesses. The API response shows an increase in daily active users (DAU) of 10% after the release, but we're seeing a slower-than-expected adoption rate within this segment. What should be our *immediate* priority?"
The scenario presents a situation where initial DAU growth doesn't translate into actual adoption within a key target segment. Cohort analysis is essential to understand *why* this segment isn't embracing the new feature – identifying barriers like usability issues or unmet needs.
14 / 17
Product Manager Emily says: "We've seen a significant increase in our Monthly Active Users (MAU) – up 30% month-over-month. However, the churn rate is also up 5%. What's the most critical thing to investigate first?"
The core issue here isn't just the growth itself; it's the combination of increased MAU and rising churn. This signals that while we're attracting more users, those users aren't sticking around – likely indicating a problem with product-market fit or user experience. Option A is too reactive, option C ignores the negative signal, and option D is misleading.
15 / 17
Growth Analyst Ben is reviewing a Slack conversation: 'User engagement metrics are down. Daily Active Users (DAU) dropped by 10% last week, and the average session duration decreased by 8%. What's the likely interpretation of this decline?'
The key here is understanding that decreasing DAU and session duration are strong indicators of declining user engagement. While technical issues or ineffective marketing could contribute, the immediate implication points to a core problem with the product itself – users aren't finding it as valuable or enjoyable as before.
16 / 17
A developer, Alex, is reviewing a PR description: 'Implemented new tracking for user feature usage. This will allow us to measure the adoption rate of our core features and identify areas for improvement.' What does 'adoption rate' primarily refer to in this context?
'Adoption rate' in this scenario focuses on how quickly and effectively users are learning and utilizing key features. It's about measuring the 'learning curve' and identifying areas where users might be struggling or not fully grasping the value proposition. Options A and B relate to usage frequency, while option D is too broad.
17 / 17
A Growth Analyst is analyzing API data. The response indicates a 20% increase in daily active users (DAU) after the launch of a new feature, but retention rates for this cohort remain low at 30% after one week. What's the most immediate action to take?
While scaling infrastructure is important, the *priority* here is to address the low retention. A spike in DAU without improved retention indicates a fundamental problem with the feature's value or usability for this specific cohort. Understanding why users are leaving quickly is crucial before investing further in growth.
What will I learn from the "Growth Metrics Language — Startup English | Exercises" exercise?
Practice growth metrics vocabulary in English. DAU/MAU stickiness, LTV/CAC/churn unit economics, cohort retention curves, NPS, and OKR goal-setting. 5 exercises for product engineers.
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This set contains 17 multiple-choice questions, each with a detailed explanation shown after you answer.
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This exercise is built for IT professionals and non-native English speakers who need to read, write, and discuss startup & product language topics confidently at work.
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