Product Metrics

Net Promoter Score measures customer loyalty by asking how likely a customer is to recommend your product or service. It is the most widely used customer experience metric, providing a single number that captures sentiment and predicts growth through word-of-mouth.

Customer satisfaction score measures how satisfied customers are with a specific interaction, product, or experience. Unlike NPS which measures loyalty, CSAT captures satisfaction at a moment in time, making it ideal for evaluating specific touchpoints in the customer journey.

Daily active users measures the number of unique users who engage with your product on a given day. It is the primary engagement metric for consumer and SaaS products, indicating whether your product has become a daily habit for its users.

Monthly active users counts the number of unique users who engage with your product within a 30-day rolling window. MAU is the broadest measure of your engaged user base and a key metric for growth, monetisation, and investor reporting.

The DAU/MAU ratio measures what proportion of monthly active users engage with your product every day. It is the most widely used indicator of product stickiness, revealing how deeply embedded your product is in users' daily routines.

Retention rate measures the percentage of users or customers who continue to use your product over a given period. It is the most important growth metric because sustainable growth is impossible when users leave faster than they arrive.

Feature adoption rate measures the percentage of users who use a specific feature within a given period. It tells product teams whether new features are resonating with users and which existing features are underutilised, guiding investment decisions and roadmap priorities.

Time to value measures how long it takes a new user or customer to experience the core value of your product. It is the most important onboarding metric because users who reach value quickly are dramatically more likely to retain, expand, and advocate.

Product-market fit score measures how disappointed users would be if they could no longer use your product. Based on the Sean Ellis survey method, it is the most direct measure of whether a product has achieved the level of value delivery that sustains organic growth.

Session duration measures the length of time a user spends actively engaged with your product during a single session. It is an engagement depth metric that indicates whether users are finding enough value to invest meaningful time in your product.

Customer effort score measures how much effort a customer had to exert to accomplish a goal with your product or service. Research shows that reducing effort is more predictive of customer loyalty than increasing satisfaction, making CES a powerful complement to NPS and CSAT.

App ranking refers to the position of a mobile application in app store search results and category charts. It is the primary driver of organic app discovery, directly influencing download volume, install cost, and the long-term viability of a mobile growth strategy.

Cost per install measures the average amount spent to acquire a single app installation through paid advertising. It is the foundational efficiency metric for mobile user acquisition, connecting ad spend directly to the volume of new users entering the app.

Weekly active users measures the number of unique users who engage with your product at least once during a seven-day window. It bridges the gap between daily and monthly active users, making it the right engagement metric for products with natural weekly usage patterns.

User retention rate measures the percentage of users who return to your product after their first visit or sign-up. It is the most important indicator of product-market fit and long-term product health, because no amount of acquisition can compensate for a product that fails to retain its users.

User activation rate measures the percentage of new sign-ups who reach a predefined activation milestone that signals they have experienced the product's core value. It is the bridge between acquisition and retention, determining whether new users become engaged users or quietly disappear.

Feature stickiness measures how frequently users return to a specific feature over time. While feature adoption rate tells you how many users try a feature, stickiness tells you whether they keep coming back to it, making it a stronger indicator of genuine feature value and long-term product engagement.

Time to first value (TTFV) measures the elapsed time from when a user signs up to when they experience their first meaningful engagement with the product. It captures how quickly the product delivers on its promise, which directly influences whether new users convert into retained customers. Shorter TTFV means higher activation rates, lower early churn, and faster revenue realisation.

Funnel conversion rate measures the percentage of users who complete a multi-step process from entry to final outcome. It captures the efficiency of any sequential workflow: onboarding flows, purchase funnels, feature adoption paths, or trial-to-paid journeys. The metric reveals not just how many users convert overall, but where in the sequence users drop off and how large each drop-off is.

A/B test performance is the statistical comparison of how different variants perform against each other on defined success metrics. It captures whether a proposed change (new design, copy, pricing, feature) produces a meaningfully better outcome than the current experience. Rigorous A/B testing replaces opinion-driven decisions with evidence, enabling product teams to invest in changes that demonstrably improve outcomes.

A business context layer is the part of a data stack that holds a company model of how its business works, so that a metric is never just a number. It records how metrics drive each other, who is accountable for each one, how each metric is defined, and the history of decisions taken and how they turned out. It sits above the semantic layer and the warehouse, which are its sources, and exists so that people and AI agents can act on a number with context rather than guessing at what it means or what to do about it.

A/B testing analysis is the process of comparing two or more variants shown to randomly assigned groups to decide whether a change produces a meaningfully better outcome on a defined success metric. It combines the observed difference between variants with the statistical confidence that the difference is real and not chance. Done well, it replaces opinion with evidence and tells you which changes are worth shipping.

Average session duration is the mean length of time visitors spend on your site or app during a single session. It is a proxy for engagement, showing whether people stay and explore or leave quickly. Read it alongside the actions visitors take, because time alone does not prove value.

Custom event conversion rate is the percentage of users or sessions that complete a specific event you have defined, out of those who reached the step before it. Unlike a generic purchase conversion, the event is one you instrument yourself, such as a workflow activated or a report shared. It measures whether the behaviours you actually care about are happening, not just the ones an analytics tool ships by default.

Dashboard utilisation rate is the share of published dashboards that are actively viewed within a given period, usually the trailing 30 days. It measures whether the reporting a team builds is genuinely used or quietly abandoned. A low rate is a warning that effort is going into dashboards nobody opens.

Device performance analysis is the practice of measuring how well a product loads, responds and stays stable across the range of devices, browsers and networks that real users bring. It exposes where the experience is fast for some users and broken for others, so teams stop optimising for the high-end devices they happen to test on.

Discussion engagement rate is the share of an audience that actively participates in a discussion rather than only viewing it. It counts replies, comments, reactions, and votes against the people who saw the thread. A high view count with a low engagement rate means people are reading but not contributing, which is the quiet signal of a community that consumes without participating.

Drop-off analysis is the study of where and how many users leave a multi-step flow before completing it. It breaks a funnel, such as signup, onboarding, or checkout, into ordered steps and measures the percentage of users lost at each one. The point is not just to know that people leave, but to find the exact step where they leave and why.

Event-driven engagement analysis is the practice of measuring how specific in-product events shape a user engagement, rather than reading engagement as a single aggregate score. It treats each meaningful action a user takes as an event, then studies which events lead to deeper, more durable engagement and which precede drop-off. The result is a clear map from the things users do to the outcomes you care about, so you can design the product around the actions that matter.

Event frequency analysis measures how often users perform a given action over a period and studies how that rate changes across users, segments, and time. Where a raw event count tells you something happened, frequency analysis tells you whether it is becoming a habit, a one-off, or a fading behaviour. It is the foundation for understanding stickiness, because the cadence of a key action is usually a better predictor of retention than whether the action happened at all.

Event tracking rate is the percentage of user actions that your analytics system successfully captures and records as events. It tells you how trustworthy your product data is. A low tracking rate means decisions are being made on an incomplete picture of what people actually do.

Extension performance analysis is the practice of measuring how well a browser extension, plugin, or app add-on contributes to acquisition, engagement, retention, and revenue across its full lifecycle. It looks at the funnel from store listing through active use, so you can tell whether an extension is pulling its weight or quietly leaking value. Done properly, it turns a vague sense that an extension is popular into a specific account of where it helps and where it hurts.

Feature delivery cycle time is the elapsed time from when active work on a feature begins to when that feature is live in production for users. It measures the speed of your delivery pipeline end to end, not the effort inside any single stage. A shorter cycle time means faster feedback, smaller batches, and a tighter loop between an idea and the evidence of whether it worked.

Feature development cycle time is the elapsed time it takes for a feature to travel from the first line of code written to running in production for users. It measures how quickly your engineering team turns intent into shipped value. A shorter cycle time means faster feedback, smaller batches, and a tighter loop between idea and impact.

Feature flag impact analysis is the practice of measuring the difference a flagged feature makes to a target metric by comparing the group exposed to the feature against the group that was not. It turns a release switch into a controlled experiment. The output is a clear answer to one question: did turning this feature on move the number we cared about, and by how much.

Funnel analysis is the practice of measuring how users move through an ordered sequence of steps towards a goal, counting how many reach each step and how many fall away in between. It locates the step where the most users drop off, so effort goes where it matters. The result is a clear, comparable view of progress and leakage at every stage.

Funnel conversion analysis is the practice of measuring the conversion rate at each step of a funnel and multiplying them to understand the end-to-end rate. It isolates the step-by-step rates so a weak point shows up as a low conversion rather than a vague drop in volume. The result is a precise view of how efficiently users move from one step to the next.

Geographic performance measures how well a business performs across the regions it sells into, comparing revenue, growth, acquisition cost and retention for each market. It turns a single global number into a map of strong and weak territories. That map tells you where to invest more, where to fix the funnel, and where concentration risk is building.

Geographic performance analysis is the practice of comparing regions against each other to find where a business wins efficiently, where it overspends, and where growth is hiding. It moves beyond a single revenue map to ask why each region performs the way it does. The output is a ranked, explained view of every market that guides where to invest and where to fix the funnel.

Goal achievement rate is the percentage of set goals that a person, team, or organisation fully achieves within a defined period. It shows how reliably ambition turns into delivered outcomes. A high rate signals that goals are well scoped and the team executes against them, while a low rate points to either unrealistic targets or weak follow-through.

Goal progress tracking is the practice of measuring how far a goal has advanced toward its target at any point during the period, expressed as the percentage of the target already reached. It turns a goal from a deadline you discover you have missed into a live position you can read and act on. Done well, it shows not just where a goal stands today but whether the current pace will get it over the line in time.

Mobile vs desktop performance is the comparison of a key outcome, usually conversion rate, between visitors on mobile devices and visitors on desktop. It surfaces the device performance gap, the difference in how well each platform turns visits into the result you care about. A persistent gap almost always points to a fixable experience problem rather than a difference in intent.

Page abandonment rate is the percentage of visitors who land on a page and leave before completing the action that page is meant to drive. It is a precise signal of friction, because it isolates the moment a visitor decided the page was not worth continuing. Unlike a broad bounce metric, it ties directly to a specific outcome you care about.

Page load time impact is the measured effect that the time a page takes to load has on downstream outcomes like conversion rate, bounce rate, and revenue. It connects a technical metric to a commercial one so the team can see what a slow page actually costs.

Page and screen views is the total count of times pages on a website or screens in an app are loaded or viewed within a period. A page view is fired on the web, a screen view is its equivalent inside a mobile or desktop app, and reporting tools combine them into one volume metric.

A participant engagement score is a single composite number that summarises how actively a participant took part in a session, course, event, or programme. It blends signals like attendance, interaction, contribution, and completion into one weighted figure so engagement can be tracked and compared.

Pin engagement rate is the share of people who interact with a Pinterest Pin, through saves, clicks, closeups, and reactions, relative to how many times the Pin was shown. It tells you whether a Pin earns attention once it reaches a feed, separate from how far it travels. A high engagement rate is the signal Pinterest uses to distribute a Pin more widely.

Profile enrichment rate is the share of records in a database that have been filled with complete, accurate data beyond what the contact first supplied. It measures how much of your data is actually usable for targeting, scoring, and routing. A low rate quietly degrades every downstream process that depends on knowing who a record represents.

Session frequency is the average number of separate sessions a user starts in a defined period, such as sessions per active user per week. It measures how often people come back, which is the clearest behavioural signal of whether a product has become a habit. A rising figure means the product is pulling users back without prompting.

Silent user identification is the practice of finding active accounts that have quietly stopped engaging, even though they still hold a paid licence or open seat. The silent user rate is the share of users who have gone dormant against a defined activity threshold. It surfaces churn risk early, while there is still time to act, rather than waiting for a cancellation.

Sprint goal achievement rate is the percentage of sprints in which the team meets the goal it set at sprint planning. It measures whether a team can reliably deliver on a stated outcome, not just whether it stays busy. Unlike point-counting metrics, it rewards finishing the thing that mattered.

Thread engagement rate is the share of an audience that takes part in the conversation around a post, measured as engaged participants divided by reach. It captures whether content sparks a back-and-forth rather than a passive view. A high thread engagement rate means people are replying, quoting, and continuing the discussion, not just scrolling past.

Time between events is the average elapsed time that separates two related events in a sequence, such as the gap between a signup and a first purchase or between two consecutive logins. It turns a sequence of timestamps into a single duration you can track and compare. A shortening interval usually signals stronger engagement or a smoother process, while a lengthening one points to friction or fading interest.

A user activity score is a single weighted number that summarises how engaged a user is, built from the actions they take inside a product over a period. It rolls behaviours like logins, key feature use, and time in app into one figure so teams can compare users and spot who is thriving or slipping.

A user adoption funnel is the sequence of stages a user passes through on the way from first sign-up to becoming a regular, value-getting user, with a conversion rate measured at each step. It shows where users progress and where they drop, turning the path to adoption into a set of numbers a team can act on.

User adoption rate is the share of users who actively use a product or feature out of everyone who has access to it. It measures whether the people who could use something actually do, which is the gap that separates a sign-up from a customer who sticks. Tracked over time, it shows whether onboarding, activation, and value delivery are working.

User engagement cohort analysis groups users by when they started and tracks how their engagement changes over the weeks and months that follow. Instead of a single blended number, it shows whether each cohort holds, decays, or recovers. This reveals whether product changes are improving the experience for the people who join after them.

A user engagement score is a single composite number that combines the actions a user takes into one weighted measure of how deeply they use a product. It rolls signals such as frequency, depth, and breadth of use into a figure you can compare across users and track over time. The point is to turn scattered usage events into one number a team can act on.

User flow analysis is the practice of measuring the sequence of screens and actions users move through inside a product, and where they stall or leave along the way. It traces the routes people actually take, not the route you designed, and quantifies the completion and drop-off at each step. The result shows you which screen is leaking and what it costs you downstream.

User group effectiveness is a measure of how well a defined group of users reaches a target outcome compared with other groups or with the population as a whole. It splits a blended product or business result by segment so you can see which groups succeed, which lag, and by how much. The result tells you where the average is hiding a group that is either carrying the number or dragging it down.

User journey analysis is the practice of measuring how users progress through the full sequence of stages from first contact to a meaningful outcome, and where they stall or leave along the way. It spans the whole experience, not a single screen, and quantifies the conversion and time spent at each stage. The result shows you which stage of the journey holds people back and what that costs downstream.

User productivity analysis is the study of how much useful output each active user produces in a given period, used to find where adoption, friction, or workflow design is helping or holding people back. It turns raw usage logs into a comparable measure of value created per user. The point is not to rank people, it is to find the parts of the product or process that make some users far more effective than others.

User segmentation analysis is the practice of dividing a user base into groups that share traits or behaviour, so you can see how value, retention, and conversion differ across the groups rather than reading a single blurred average. It answers which users are worth more, which are at risk, and why the headline number behaves the way it does. The output is not just groups, it is the explanation of what makes one group behave differently from another.

User story size consistency measures how uniform the size of user stories is across a backlog or sprint, usually by comparing the spread of story point estimates against their typical value. Consistent sizing makes velocity predictable and planning trustworthy, while wildly varying story sizes make a sprint forecast little better than a guess. The metric is a health check on estimation discipline, not a target to game.

Variant performance analysis is the practice of comparing two or more versions of an experience to measure which one drives a target outcome more effectively and by how much. It quantifies the lift one variant produces over a control, then tests whether that difference is large enough to trust. The discipline turns a vague sense that something works into a measured, defensible decision.

Workflow drop-off analysis measures how many users enter a multi-step process and how many abandon it at each step, so you can see exactly where a flow leaks. It turns a single completion number into a step-by-step map of where momentum is lost and why. The biggest drop-off step is usually the cheapest place to recover lost users.