Strategic Growth Hacking glossary

Definition: A customer lifecycle model (also called “pirate metrics”): Awareness, Acquisition, Activation, Revenue, Retention, Referral. Used to find bottlenecks and choose what to improve next.

Why it matters: Without a shared lifecycle model, teams often optimize local metrics (e.g. clicks) while the real constraint sits elsewhere (e.g. activation or retention). AAARRR makes bottlenecks visible and keeps prioritization honest.

Definition: The “getting users/customers” stage. In AAARRR, it’s the step where people first start using or signing up.

Why it matters: If acquisition is the bottleneck, improving conversion rates upstream can have immediate impact. If it is not the bottleneck, more traffic can create noise rather than growth.

Definition: A way to understand whether your acquisition investment is economically sound (what you get back vs. what you spend).

Why it matters: It prevents “growth” from turning into performance marketing theater. If acquisition isn’t profitable (or trending toward it), scaling acquisition compounds losses.

Definition: The moment a user experiences the core value (“aha moment”). In AAARRR, it’s the step where the user becomes meaningfully active.

Why it matters: If activation is weak, acquisition improvements often fail to translate into durable growth. Activation is one of the highest-leverage stages in many products.

Definition: The first phase of the four-phase process: analyze the current situation and/or learnings from the previous cycle before forming hypotheses.

Why it matters: Without analysis, experimentation becomes random activity. With it, hypotheses are grounded and prioritization becomes tractable.

Definition: Customer acquisition cost calculated in a blended way (not per-channel attribution), often used when attribution is incomplete.

Why it matters: It keeps decision-making grounded in economics even when channel attribution breaks down.

Definition: The biggest constraint limiting growth right now. The growth team finds the bottleneck, solves it using the process, and then moves on to the next one.

Why it matters: Bottleneck thinking prevents scattered prioritization and turns growth work into sequential problem solving.

Definition: The total cost to acquire a customer. For more accurate CAC, include costs like salaries, tools, outsourcing, and other acquisition-related overhead.

Why it matters: CAC determines whether growth compounds profitably or compounds losses.

Definition: A combined view of conversions (often from your CRM) used as a reliable signal when channel-level attribution is noisy.

Why it matters: When attribution is broken, combined conversions can still be stable enough to guide weekly iteration and OKR tracking.

Definition: A defined action that indicates progress (e.g. lead, signup, purchase). Conversions must have consistent definitions across teams to avoid confusion.

Why it matters: If “lead” means different things to marketing and sales, the organization can look like it is growing while revenue stalls.

Definition: Customer relationship management system. Often the source of truth for leads and conversions (and useful for “combined conversions”).

Why it matters: When analytics attribution is unreliable, CRM-based outcomes keep growth work tied to business reality.

Definition: The percentage of people who click after seeing something (e.g. an ad or message). Useful for evaluating acquisition experiments.

Why it matters: CTR helps you understand whether messaging resonates, but only matters insofar as it improves qualified conversions or revenue.

Definition: A spreadsheet-based dashboard used as a single place to collect growth-related information and support decision-making.

Why it matters: A single source of truth reduces coordination overhead and makes the testing cycle visible across functions.

Definition: A test designed to create learning and move a KPI. An “optimal experiment” is tied to OKRs, measurable, learning-focused, and executable within a short cycle (e.g. a week).

Why it matters: Experiments create compounding knowledge: even failed tests improve future prioritization and expose execution bottlenecks.

Definition: The core operating cycle: Analysis → Hypothesis → Prioritization → Testing. Repeat each cycle consistently.

Why it matters: It prevents “random acts of growth” by enforcing a consistent loop that produces learnings and momentum.

Definition: EU privacy regulation that impacts tracking and attribution. In practice, it often makes channel-level attribution less reliable.

Why it matters: If you depend on perfect attribution, your decision system becomes fragile. GDPR pushes teams toward more robust measurement practices.

Definition: A person who centralizes and drives the growth process (e.g. monitoring OKR deployment, maintaining the cadence, and keeping the team aligned).

Why it matters: Without a clear owner of the process, cycles slip, learnings vanish, and growth work becomes ad hoc.

Definition: A specific, testable statement about what change could improve a KPI. In the process, you typically form 1–3 hypotheses per cycle.

Why it matters: Hypotheses prevent “busy work” and force clarity about what you expect to happen and why.

Definition: A clear description of the customer type you’re best positioned to serve. ICP is iterated continuously as you learn.

Why it matters: A clear ICP reduces wasted acquisition, improves conversion quality, and sharpens messaging and prioritization.

Definition: A measurable number used to guide decisions and evaluate progress. The first step in the process is choosing a KPI that can be measured periodically.

Why it matters: Without a KPI, you cannot evaluate experiments, prioritize rationally, or compound learning.

Definition: A shared set of definitions for metrics like “lead” so different teams (e.g. marketing and sales) mean the same thing when they talk about numbers.

Why it matters: If teams use different definitions, you can hit targets without moving the business. Definitions make performance discussable and actionable.

Definition: A mindset where outcomes (including failures or execution issues) are treated as learning that improves future decisions and process quality.

Why it matters: Learning compounds. Treating failures as learning increases the rate of iteration and reduces fear-driven stagnation.

Definition: The authority to make decisions needed to remove bottlenecks (e.g. buying tools or outsourcing). Lack of mandate is a common pitfall.

Why it matters: Many growth bottlenecks are organizational (skills, time, ownership). Mandate enables solutions.

Definition: A guiding top-level goal that aligns the team’s work (often revenue in cross-functional growth). In this content, it’s expressed as a “Strategic Goal” and its sub-goals.

Why it matters: A clear north star reduces wasted effort and makes trade-offs explicit.

Definition: In OKRs, the Objective is the qualitative goal you want to achieve. It’s supported by measurable Key Results.

Why it matters: Objectives create shared direction; Key Results create measurability.

Definition: A goal-setting method that connects an Objective (what you want) to Key Results (how you measure progress). Used to steer experiments toward meaningful outcomes.

Why it matters: OKRs prevent growth work from drifting into unconnected tactics. They also make progress (or lack of it) visible.

Definition: The accountable person for a metric, area, task, or experiment. Clear ownership prevents work from stalling.

Why it matters: Without owners, tasks and experiments become “everyone’s job”, which usually means no one does them.

Definition: A shared view of the sales pipeline used to analyze pipeline efficiency and connect marketing and sales performance.

Why it matters: It prevents “lead volume” from hiding poor downstream conversion or process bottlenecks.

Definition: The phase where you select what to test next based on likelihood of success, resource intensity (time/money), and scalability.

Why it matters: Great ideas are abundant; time and attention are scarce. Prioritization protects the cadence and learning rate.

Definition: The stage where existing users bring in new users (word-of-mouth, invites, sharing). In AAARRR, it’s the last step.

Why it matters: Referral can create compounding, lower-CAC growth—but usually only after earlier stages are working.

Definition: How well users/customers keep coming back. Strong retention compounds growth and is often a better lever than more acquisition.

Why it matters: Poor retention turns acquisition into a leaky bucket. Great retention makes almost every other growth effort more effective.

Definition: The monetization or conversion event in the lifecycle. In AAARRR, it’s the point where value is captured (purchase, subscription, etc.).

Why it matters: Revenue is the most direct growth outcome. But improving it often requires upstream work (activation, retention, pipeline efficiency).

Definition: A top-level goal that serves as the “north star” for experiments and tasks. Sub-goals break it into actionable areas.

Why it matters: It aligns cross-functional work and ensures experiments are not isolated tactics.

Definition: Operations turned into repeatable routines that can be executed consistently and improved over time.

Why it matters: Systematic work compounds: each cycle improves execution quality and learning speed.

Definition: The focus on running tests where outcomes aren’t known upfront. Testing is executed as part of a repeatable cycle and documented thoroughly.

Why it matters: Testing turns uncertainty into learning, which is the core engine of improvement.

Definition: A recurring cadence (often weekly or biweekly) where you run the four-phase process end-to-end and document learnings.

Why it matters: Without a cadence, growth work becomes reactive and learnings don’t accumulate.

Definition: Metrics that look good but don’t reflect real business progress (e.g. impressions without conversions). A common red flag is chasing them.

Why it matters: Vanity metrics can create false confidence and lead to scaling the wrong things.