Every Product Analytics Term You Need To Know with Resource Links To Learn More

What is product analytics?

Product analytics is the application of data and analytics to detect patterns of usage and identify opportunities for product improvement in order to improve business outcomes and solve customer pain points.

In the sections below, you’ll find an alphabetized list of terms related to product analytics and links to learn more about them.

A, B, C


Activation: A point or phase at the end of Onboarding during which a new user obtains enough value from the product such that they become a current active user of the product.

Active user: A user who has done some action in your product during a given time period.

Acquisition cohort: A group of users who started using your product (in other words, were new) during the same time period.

Aha moment: The moment or set of actions within your product that leads users to first discovering value.

Attention game: In the three games of engagement framework, products playing the attention game try to maximize the amount of time users spend in-product. Industries that typically play this game today are media, gaming and any company displaying advertisements to you.


Behavioral cohort: A group of users who performed (or didn’t perform) certain actions within a defined time period in your product.

Behavioral persona: A group of users who have a distinct way of using your product.Understanding the behavioral personas within your product will inform how you design the experience to meet the needs and habits of different types of users.

Bracket retention: A flexible version of N-day retention where you can look at the proportion of users who return during custom time frames that you define.


Churn rate: The proportion of users who used your product on Day 0 but did not return again; the inverse of your unbounded retention rate.

Cohort: A group of users who share some common characteristic. See acquisition date cohorts and behavioral cohorts.

Compass: In Amplitude, Compass identifies the user behaviors that best predict retention.

Conversion window: The amount of time a user has to complete a funnel from the time they enter it.

Core user: People who are using your app at a regular frequency and in the “expected” way. This can describe one of your behavioral personas.

Critical event: An action that users take in your product that aligns closely with the value you want your product to deliver. When measuring engagement metrics like retention and stickiness, it is more valuable to look at the users who perform this critical event as your pool of “active users,” as opposed to users who do any arbitrary action. The critical event is also usually closely tied to the value exchange in your product.

Critical path funnel: The series of actions you anticipate users taking in order to complete your critical event.

Current user: Someone who has been using your product consistently for some period of time. In Amplitude, this is defined as a user who used the product during the last interval and the current interval.

Customer-value exchange: Describes the potential value that products deliver to customers in exchange for the customer’s investment of time and money.

D, E, F, G


Dormant user: Users who were once actively using your product and then became inactive. In Amplitude, this is defined as a user who did not use the product in the current interval but was active in the previous interval. You can think of dormant users as people who you have the potential to resurrect.


Engagement: A measure of how much or how often users interact with your product or with a feature.

Engagement loop: An interaction framework describing the actions and triggers that current engaged users cycle through when they use your product regularly.

Event: In event-based analytics, an event is an action performed by the user or taken by the product.

Event property: An attribute of an event that provides more detail about that event. These are up to you to track, and depend on the information you think is necessary to understand a particular event. For example, if you had a ‘Checkout’ event, some event properties might include ‘total amount’, ‘number of items’, and ‘payment method’.

Event Segmentation (Amplitude chart): In Amplitude, the Event Segmentation chart lets you accomplish deeper segmentation on your events and the users who perform them.

External trigger: Specific sensory stimuli that companies/products use to nudge users into taking action.


First value exchange: The first exchange of value between a new user and the business.

H, I, J, K


Habit Formation phase: This follows the Onboarding and Value Discovery phases of new user retention. Once a user has discovered value in your product, you need to make sure they develop a habit so that they keep coming back over time. Users who successfully pass through the Habit Formation phase become current users of your product.


Internal trigger: Feelings and emotions that manifest in the mind and cue users to take action on their own.

Instrumentation: The process of recording events and attributes as they happen in your product.


KPI (Key Performance Indicator): A measurable value that demonstrates how well an organization is achieving its current objective.

L, M, N, O, P


Ladder of engagement: The ongoing learning journey a new user embarks on to become an expert.

Leading indicator: A value or measurement that can be used to indicate future business outcomes. Good north star metrics are leading indicators of success. On the other hand, metrics like ARPU and monthly revenue are lagging indicators.

Lifecycle: A feature in Amplitude that breaks out your active user base into new, current, resurrected, and dormant users during any time interval. Lifecycle helps you measure the health of your product and can identify imbalances, for example if your churn is outpacing new user acquisition.


N-day retention: Retention method that measures the proportion of users who are active in your product on a specific Nth day after an initial event.

New user: Someone who is using your product for the first time. In Amplitude, this is a user who is in their first interval of using the product.

North star metric: Defines the relationship between the customer problems the product team is trying to solve and the revenue the business aims to generate by doing so.

Non-cumulative stickiness: In Amplitude stickiness chart, depicts the proportion of users who were active in your product for exactly X number of days.


OKR (Objectives and Key Results): A framework for defining and tracking company, team, or personal objectives.

Onboarding: A series of steps within your product designed to show new users how they can use the product to obtain value.

Onboarding phase: This is the first phase of new user retention and is defined as the first day of use for this playbook. During this phase, a new user of your product completes the onboarding experience and uses the product for the first time. It’s critical that you get users to experience your product’s core value as quickly as possible.


passive user: People who might not be contributing or using your app in the core way that you designed, but are still coming back at a regular frequency to do something. This can describe one of your behavioral personas.

Path analysis: A method of measuring the most common sequences of events that users take in your product.

Pathfinder: A feature in Ampllitude that enables you to explore the actions users take to or from any point in your product (i.e. path analysis). Pathfinder aggregates the paths users take so you can see the percentage of users or sessions that followed each sequence.

Personas: In Amplitude, Personas automatically groups users into clusters based on similarities in behavior. This is one way to identify behavioral personas in your product.

Power user: People who use your product with a very high frequency or use a “power” feature that the majority of users don’t take advantage of. This can describe one of your behavioral personas. product usage interval: How often (daily, weekly, monthly, etc.) users naturally use your product. When determining your product usage interval using the framework in Chapter 2, this is the time interval at which 80% of users complete the critical event a second time.

Power user curve: Coined by Andrew Chen, this chart depicts the proportion of users who were active in your product for exactly X number of days (also see non-cumulative stickiness)

Product analytics: The application of data and analytics to detect patterns of usage and identify opportunities for product improvement in order to improve business outcomes and solve customer pain points.

Product usage interval: The frequency (daily, weekly, monthly, etc.) with which you expect people to use your product.

Productivity game: In the three games of engagement framework, products playing the productivity game create an easy and reliable way to complete an existing task or workflow for the user. This game is predominant in business-to-business software.

Properties: User properties and event properties can give you a deeper analysis into how users are engaging with your app. User properties are attached to users and reflect the current state of the user at the time of the event while event properties are attached to events and reflect the state of the event that was triggered.

Pulse: A chart view in Ampliude’s Lifecycle feature that depicts the ratio of incoming users to outgoing users for a particular time period. This ratio is calculated as follows: (# of new users + # of resurrected users) / (# of dormant users).

Q, R, S


Resurrected user: Someone who was once actively using your product, who then became dormant for a period of time, and then became active again. In Amplitude, this is defined as a user who who used the product sometime before the previous interval but not in the previous interval, and now active in the current interval.

Retention: A measure of how many users return to your product over time after some initial event (usually first use).

Retention curve: A line graph depicting user retention over time. It shows the percentage of users who return to the product during a specified time period after acquisition.

Retention lifecycle: The flow of active users between the different stages of user retention: new, current, and resurrected user retention.

Retention lifecycle framework: A framework for analyzing retention depending on whether a user of your product is new, current, or resurrected.


Segment/Segmentation: A segment is a subset of users who share a common characteristic, like a user property. Segmentation is the act of dividing a chart by this characteristic; for example, graphing a retention curve by country.

Social proof: The phenomenon wherein a large group of people conform to each others’ behaviors and actions. If users of your product get their friends and acquaintances to also become users, this can be a point of social proof for you.

Stickiness: A measure of the frequency with which people use your product; specifically, a measure of the number of days out of a week or a month that a user was active, or did a specific action.

Stickiness (Amplitude chart): In Amplitude, the Stickiness chart lets users plot cumulative stickiness, non-cumulative stickiness, and stickiness over time.

T, U, V


Three games of engagement: A framework for thinking about your product’s engagement strategy, which includes the attention game, transaction game, and productivity game.

Transaction game: In the three games of engagement framework, products playing the transaction game help customers make purchase decisions with confidence. Companies you will often find playing this game are e-commerce platforms.

Trigger: A stimulus that brings users back to a product. This can be an external trigger, such as an email or push notification, or an internal behavioral trigger, such as a pre-existing habit.


Unbounded retention: This retention method measures the proportion of users who came back to your product on a specific day or later. For example, Day 30 unbounded retention would give you the percentage of users who returned on Day 30, or any day after Day 30. This is the inverse of your churn rate.

User Composition: A view in Amplitude that lets you quickly visualize the breakdown of different user properties for a specific group of users.

User property: Any characteristic that is tied to an individual user. Examples of user properties are country, device type, age, gender, referral source, plan type, number of photos uploaded, number of units of in-game currency, and current level in a game.


Value Discovery phase: This follows the Onboarding phase of new user retention and precedes the Habit Formation phase. During this time, it’s important to show your product’s core value as many times as possible.

Value exchange: Modern management theory defines a product as a medium of value exchange between a user and a business. The product offers value to a customer—entertainment, efficiency, status etc.—and the customer in return offers compensation to the business.

W, X, Y, Z