Glossary
The growth glossary
30 terms every founder and growth marketer actually uses — defined clearly, with a concrete example for each. No jargon, no fluff.
Growth Loop
A growth loop is a self-reinforcing system where the output of one user's actions becomes the input that drives new user acquisition or engagement, creating compounding growth without constant new spend. Unlike a linear funnel, the loop feeds back on itself: each cycle reinvests its result to power the next. Common types include viral, content, and paid loops.
Example: Dropbox's referral loop: a user invites a friend, both get free storage, the friend signs up and invites their own friends, restarting the cycle.
Funnel
A funnel is a linear model of the stages a user moves through from first contact to a target outcome, such as awareness, sign-up, activation, and purchase. It quantifies how many users advance versus drop off at each step, making it easy to spot the biggest leak. Funnels measure one-directional flow, whereas loops and flywheels reinvest output back into the system.
Example: If 10,000 visitors yield 1,000 sign-ups and 100 paying customers, the funnel shows a 10% visit-to-signup and 10% signup-to-paid conversion.
Flywheel
A flywheel is a growth model where momentum builds over time as each part of the business reinforces the next, so early effort compounds and customer satisfaction itself drives more growth. It reframes growth as a continuous, accelerating cycle rather than a funnel that ends at purchase. The harder it spins, the less energy each additional turn requires.
Example: Amazon's flywheel: lower prices attract more customers, which draws more sellers, which expands selection and further lowers prices.
Related:Growth LoopFunnelNetwork Effect
Product-Led Growth (PLG)
Product-led growth (PLG) is a go-to-market strategy in which the product itself is the primary driver of acquisition, conversion, and expansion, rather than sales or marketing teams. Users experience value firsthand through free trials or freemium plans before paying, shortening the path from sign-up to revenue. PLG depends heavily on fast activation and a clear aha moment.
Example: Slack lets a team adopt the product free and upgrade once usage grows, with no sales call required.
Growth Hacking
Growth hacking is a data-driven approach to growth that uses rapid experimentation across marketing, product, and engineering to find scalable, often unconventional ways to acquire and retain users. It prioritizes cheap, fast tests over large budgets, doubling down on what works and killing what doesn't. The discipline is built on continuous A/B testing and metric-driven decisions.
Example: Airbnb's early growth hack of cross-posting listings to Craigslist tapped an existing audience at near-zero cost.
AAARRR (Pirate Metrics)
AAARRR is a framework that maps the customer lifecycle into six measurable stages: Awareness, Acquisition, Activation, Retention, Referral, and Revenue. Coined by investor Dave McClure as "Pirate Metrics" (the acronym sounds like a pirate's "AARRR"), it gives growth teams a shared structure for tracking where users enter, succeed, or drop off. Each stage has its own conversion rate, so teams can pinpoint the single biggest bottleneck instead of optimizing blindly.
Example: A SaaS team finds 5,000 signups (Acquisition) but only 600 users complete onboarding (Activation), so they focus the next sprint on fixing that 12% activation rate.
North Star Metric
A North Star Metric is the single metric that best captures the core value a product delivers to its customers and predicts long-term, sustainable growth. It aligns every team around one number that reflects real customer value rather than vanity metrics like raw signups or pageviews. The best North Star Metrics measure a recurring action tied to the product's "aha" value, such as nights booked for Airbnb or messages sent for a messaging app.
Example: Spotify uses "time spent listening" as its North Star Metric because it reflects genuine engagement better than total registered accounts.
Activation
Activation is the stage where a new user experiences your product's core value for the first time, turning a signup into an engaged user. It is the second "A" in the AAARRR framework and the point at which users reach the "aha moment" that makes them likely to stick around. High activation rates are the strongest early predictor of long-term retention, which is why growth teams obsess over the onboarding flow that leads to it.
Example: For a project-management tool, activation might be defined as a new user creating their first project and inviting one teammate within the first session.
Aha Moment
The Aha Moment is the precise point at which a new user first understands and experiences your product's core value, making them significantly more likely to retain. It is identified by analyzing which early behaviors correlate most strongly with long-term retention, then defining the action and threshold that separate retained users from churned ones. Once found, growth teams redesign onboarding to push as many users as possible to that moment as fast as they can.
Example: Facebook's classic aha moment was a user adding 7 friends in 10 days, which dramatically increased their odds of staying active.
Time to Value (TTV)
Time to Value (TTV) is the elapsed time between a user signing up and reaching their first meaningful outcome with your product. A shorter TTV correlates with higher activation and retention, because users decide whether a product is worth keeping within their first few sessions. Growth teams reduce TTV by removing onboarding friction, using templates or pre-filled data, and guiding users straight to the aha moment.
Example: A reporting tool cuts its time to value from 20 minutes to 3 by offering a sample dashboard the moment a user signs up, instead of forcing manual setup.
Retention
Retention is the percentage of users who keep using a product or service over a defined period after they first sign up or purchase. It is the clearest signal of product-market fit because it measures whether a product delivers recurring value rather than just one-time interest. High retention compounds growth by lowering reliance on constant new acquisition.
Example: If 1,000 users sign up in January and 420 are still active in March, the 60-day retention rate is 42%.
Churn Rate
Churn rate is the percentage of customers or revenue lost over a given period, typically measured monthly or annually. It is the inverse of retention and a primary driver of whether a subscription business can grow sustainably. Logo churn counts lost accounts, while revenue churn counts lost recurring revenue.
Example: If you start the month with 500 customers and 25 cancel, monthly churn rate is 25 / 500 = 5%.
Related:RetentionMRR (Monthly Recurring Revenue)LTV (Lifetime Value)
Cohort Analysis
Cohort analysis is a method that groups users by a shared starting characteristic, usually their signup date, and tracks how each group behaves over time. It isolates how product changes affect different user generations, revealing trends that aggregate metrics hide. It is the standard technique for measuring retention, churn, and revenue patterns accurately.
Example: Comparing the January and February signup cohorts shows February users retain at 50% by week 4 versus January's 38%, confirming a recent onboarding change worked.
DAU / MAU (Stickiness)
DAU/MAU is a stickiness ratio that divides daily active users by monthly active users to measure how often people return within a month. A ratio of 0.2 means the average monthly user engages on roughly 6 of 30 days. Higher ratios indicate habitual, daily-use products and stronger engagement.
Example: A product with 20,000 daily active users and 100,000 monthly active users has a DAU/MAU stickiness of 0.20, or 20%.
Retention Curve
A retention curve is a graph plotting the percentage of a cohort still active against the time elapsed since signup. A healthy product shows the curve flattening into a plateau, which proves a stable base of users who stick around; a curve that decays to zero signals no product-market fit. The height of the plateau, not the initial drop, is the key indicator of long-term value.
Example: A retention curve that drops to 40% by week 1 but flattens at 30% from week 8 onward reveals a loyal core that keeps using the product.
K-Factor
K-Factor is the number of new users each existing user generates through invitations or referrals. A K-Factor above 1 means a product grows virally on its own, since every user brings in more than one additional user before churning. It is calculated by multiplying the number of invites sent per user by the conversion rate of those invites.
Example: If each user sends 10 invites and 15% convert, the K-Factor is 10 × 0.15 = 1.5, meaning the user base self-amplifies.
Referral Program
A Referral Program is a structured system that rewards existing users for inviting new ones, typically with incentives for both the referrer and the referred. It turns satisfied customers into an acquisition channel and is a primary lever for increasing a product's K-Factor. Effective programs offer a clear reward, a low-friction sharing flow, and tracking that attributes each signup to its source.
Example: Dropbox's program gave both the referrer and the new user 500 MB of free storage, driving a 60% increase in signups.
Related:K-FactorViral CoefficientCAC (Customer Acquisition Cost)
Network Effect
A Network Effect occurs when a product becomes more valuable to each user as more people use it. This dynamic creates a defensible moat, because the growing user base raises switching costs and makes the product harder for competitors to displace. Network effects can be direct (users benefit from other users, like messaging apps) or indirect (one user group benefits from another, like marketplaces).
Example: Slack becomes more useful as each additional teammate joins, since communication value rises with every new participant.
Net Promoter Score (NPS)
Net Promoter Score (NPS) is a metric that gauges customer loyalty by asking how likely users are to recommend a product on a 0-to-10 scale. It is calculated by subtracting the percentage of detractors (scores 0-6) from the percentage of promoters (scores 9-10), producing a score from -100 to +100. A high NPS signals strong word-of-mouth potential and correlates with retention and organic growth.
Example: If 60% of respondents are promoters and 20% are detractors, the NPS is 60 − 20 = 40.
MRR (Monthly Recurring Revenue)
MRR is the total predictable subscription revenue a business normalizes to a monthly amount. It includes new, expansion, and reactivation revenue while subtracting contraction and churn, giving SaaS teams a single recurring-revenue baseline that excludes one-time fees. MRR is the core operating metric for tracking growth momentum month over month.
Example: 100 customers paying $50/month equals $5,000 MRR; an annual plan billed at $600 contributes $50 to MRR.
Related:ARR (Annual Recurring Revenue)ARPU (Average Revenue Per User)Churn Rate
ARR (Annual Recurring Revenue)
ARR is the value of recurring subscription revenue normalized to a one-year period. It equals MRR multiplied by 12 and reflects only committed, repeatable revenue, excluding one-time charges and variable usage fees. ARR is the headline metric most B2B SaaS companies use to report scale to investors and boards.
Example: A company with $40,000 MRR has $480,000 ARR ($40,000 × 12).
Related:MRR (Monthly Recurring Revenue)ARPU (Average Revenue Per User)Churn Rate
ARPU (Average Revenue Per User)
ARPU is the average recurring revenue generated per active user or account over a given period. It is calculated by dividing total revenue (typically MRR or ARR) by the number of active users in that period. ARPU reveals monetization efficiency and is a key input for pricing, packaging, and segmentation decisions.
Example: $50,000 in MRR across 500 accounts gives an ARPU of $100 per account per month.
Related:MRR (Monthly Recurring Revenue)LTV (Lifetime Value)CAC (Customer Acquisition Cost)
LTV (Lifetime Value)
LTV is the total revenue or gross profit a business expects to earn from a customer over the entire duration of the relationship. It is commonly estimated as ARPU divided by churn rate, optionally multiplied by gross margin to reflect profit rather than revenue. LTV sets the ceiling on how much a company can profitably spend to acquire a customer.
Example: An ARPU of $100/month and a 5% monthly churn rate yields an LTV of roughly $2,000 ($100 ÷ 0.05).
Related:CAC (Customer Acquisition Cost)ARPU (Average Revenue Per User)Churn Rate
CAC (Customer Acquisition Cost)
CAC is the total sales and marketing cost required to acquire one new paying customer. It is calculated by dividing all acquisition spend over a period (ad budget, salaries, tools) by the number of new customers won in that period. CAC is most meaningful when compared against LTV, with a healthy SaaS benchmark of an LTV:CAC ratio around 3:1 or higher.
Example: Spending $30,000 on sales and marketing to win 100 customers gives a CAC of $300.
Related:LTV (Lifetime Value)ARPU (Average Revenue Per User)Conversion Rate
Conversion Rate
Conversion rate is the percentage of users who complete a desired action out of the total number who had the opportunity to do so. It is the core measure of how effectively a page, funnel, or campaign turns visitors into the next step you care about, such as signups, trials, or paid customers. Tracking it by segment and stage reveals exactly where users drop off.
Example: If 50 of 2,000 landing-page visitors start a trial, the conversion rate is 50 / 2,000 = 2.5%.
Related:FunnelA/B TestingBounce Rate
A/B Testing
A/B testing is a controlled experiment that compares two or more versions of a page, feature, or message by randomly splitting traffic and measuring which variant drives a target metric higher. It isolates the impact of a single change so decisions rest on evidence rather than opinion. Results are only trustworthy once they reach statistical significance and an adequate sample size.
Example: A team sends 50% of traffic to a green CTA button and 50% to a blue one, then compares the signup conversion rate of each variant.
Related:Statistical SignificanceConversion RateGrowth Hacking
Statistical Significance
Statistical significance is the likelihood that an observed difference between two groups reflects a real effect rather than random chance. In growth experiments it is typically expressed as a p-value below 0.05, meaning there is less than a 5% probability the result happened by luck. Reaching it requires a sufficient sample size before any winner is declared.
Example: An A/B test showing a 12% lift with a p-value of 0.03 is statistically significant, since 0.03 is below the 0.05 threshold.
Related:A/B TestingConversion Rate
Bounce Rate
Bounce rate is the percentage of visitors who land on a page and leave without taking any further action or viewing another page. A high bounce rate often signals a mismatch between traffic intent and page content, slow load times, or weak messaging. It is a leading indicator of friction at the very top of the funnel.
Example: If 700 of 1,000 visitors leave a blog post without clicking anything else, the bounce rate is 70%.
Related:Conversion RateFunnelActivation
Lead Magnet
A lead magnet is a free, high-value resource offered in exchange for a prospect's contact details, usually their email address. It converts anonymous traffic into known leads you can nurture toward a purchase, trading immediate value for permission to follow up. Common formats include ebooks, checklists, templates, free tools, and mini-courses.
Example: A SaaS offers a free "SaaS Metrics Calculator" template that visitors download by entering their work email.
Related:Conversion RateFunnelCAC (Customer Acquisition Cost)
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