Growth Engineering Glossary

An experiment comparing two variants to determine which performs better on a key metric.

A growth model covering Acquisition, Activation, Retention, Referral, Revenue.

The process of gaining new users or customers through various channels.

The moment a user experiences the product’s core value for the first time.

A product analytics platform used to track user behaviour and optimise digital experiences.

Tools and practices used to measure user behaviour and product performance.

A way for different software systems to communicate and share data.

Assigning credit to marketing channels for conversions or user actions.

A method of tracking all user interactions on a website or app without manual instrumentation.

The server-side part of a web application that handles data processing and business logic.

Google’s fully-managed data warehouse for large-scale analytics.

The percentage of users who leave after visiting only one page.

The average cost to acquire a new customer.

Standardised naming conventions for events to maintain consistency in tracking.

A method used to determine whether one factor causes an effect in a given context.

A system that collects and unifies customer data from multiple sources.

The point at which adding more spend to a channel yields diminishing returns.

The percentage of users or customers who stop using a product over a given period.

The total worth of a customer over the entire relationship with a brand.

A method of analysing user behaviour grouped by shared characteristics over time.

The percentage of users who complete a desired action, like signing up or purchasing.

Testing different text variations to see which drives better engagement or conversions.

Software used to manage customer data and interactions.

Encouraging customers to purchase complementary products or services.

The ratio of users who click on a specific link compared to total views.

A user-defined event tailored to a specific product or business need.

Enhancing raw data with additional context or third-party sources.

A structured layer on a site that stores key user and event data for analytics tools.

A series of processes that move and transform data from source to destination.

The number of unique users engaging with a product daily.

The point in a funnel where users stop engaging or fail to complete a step.

A test that simulates real user scenarios to validate full system flow.

A rare or extreme condition that tests the limits of a system or feature.

The percentage of users who interact with a feature or content.

A structured system for naming and categorising events across products.

Capturing specific user actions (e.g. clicks, scrolls, signups) for analysis.

A group of users excluded from changes in an experiment to serve as a control.

How quickly a team can ship and learn from experiments.

A switch that enables or disables a feature without deploying new code.

The gradual release of a new feature to users, often using feature flags.

An obstacle or difficulty that hinders user progress or satisfaction.

A visualisation of the steps users take toward a conversion goal.

The latest version of Google Analytics, focused on event-based tracking.

A self-reinforcing system where user actions drive further growth.

A structured representation of how a product grows (users, revenue, etc.).

The ultimate metric guiding growth efforts across teams.

A secondary metric in experiments used to monitor unintended negative impact.

A visual tool showing where users interact most on a page.

A detailed description of the type of customer who gains the most value from a product.

The additional value generated by an action that wouldn’t have occurred otherwise.

A measure of virality based on how many new users each existing user brings.

An outcome-based metric that reflects past performance (e.g. revenue).

The amount of time between idea conception and experiment delivery.

A predictive metric indicating future performance (e.g. trial-to-paid conversion).

A Google tool for building dashboards and visualising data.

The projected revenue a customer will generate during their relationship with a business.

The number of unique users engaging with a product each month.

Small bits of UI text (e.g. button labels, error messages) that guide user behaviour.

The most basic version of a product that can still deliver value and be tested.

A single, high-level metric that best captures the product’s value to users.

A measure of customer loyalty based on likelihood to recommend.

The process of guiding users to experience value early in their journey.

The percentage of users who consent to marketing or data collection.

Tailoring content, experiences or messaging based on user data.

Comparing key metrics before and after a change to assess impact.

A go-to-market strategy where the product itself drives acquisition and retention.

Descriptive insights (e.g. surveys, interviews) about user motivations and pain points.

Numerical data that measures user behaviour at scale.

When existing users bring in new users, often incentivised.

Re-testing existing features after changes to ensure nothing is broken.

The ability to keep users engaged and returning over time.

A graph showing how user retention changes over time.

Grouping customers based on Recency, Frequency, and Monetary value.

A prioritisation framework using Reach, Impact, Confidence, and Effort.

A projection of future performance based on current data (e.g. MRR x 12).

A tool to determine how many users are needed for a statistically significant test.

Dividing users into groups based on behaviour, demographics, or value.

A programming language used to manage and query databases.

How frequently users return to a product, often measured as DAU/MAU.

The time it takes for a user to reach a meaningful outcome or benefit.