The HEART Framework
Google's framework for choosing user-centred metrics across five dimensions — Happiness, Engagement, Adoption, Retention, Task Success — so you measure UX quality, not just raw usage.
▸ Try the interactive toolThe framework
The HEART framework, developed at Google, is a structured approach to selecting user-centred metrics across five dimensions of user experience: Happiness, Engagement, Adoption, Retention, and Task Success. It exists to help teams measure UX quality and product health beyond pure usage counts — to capture how well the product actually serves users, not just how much it's used.
HEART's real contribution is paired with the Goals–Signals–Metrics process: for each relevant dimension, you state the goal (what good looks like), identify the signal (the user behaviour that indicates it), and define the metric (how you'll measure that signal). Not every dimension applies to every product or feature — the framework's discipline is choosing which of the five matter for what you're measuring, then deriving real metrics rather than reaching for whatever's easy to count.
Happiness · Engagement · Adoption · Retention · Task success.
For each relevant dimension: state the goal → find the signal (user behaviour) → define the metric.
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What it prevents
Teams default to measuring whatever's easy to count (usage, page views) rather than what actually reflects UX quality — HEART forces a more deliberate, user-centred choice.
| The shortcut | What it costs | What it gives you instead |
|---|---|---|
| Counting usage only | Raw usage misses happiness, task success, and real UX quality. | HEART measures the dimensions of experience, not just activity. |
| Easy-to-count metrics | Teams measure what's convenient, not what matters. | Goals–Signals–Metrics derives the right metric from the goal. |
| Applying all five everywhere | Forcing every dimension onto every feature dilutes focus. | HEART's discipline is choosing the dimensions that actually apply. |
| No link to a goal | Metrics with no stated goal can't be interpreted. | Starting from the goal makes each metric meaningful. |
Step by step
Choose the dimensions that apply
Of the five — Happiness, Engagement, Adoption, Retention, Task Success — pick the ones relevant to what you're measuring. A new feature might emphasise Adoption and Task Success; a mature product, Retention and Happiness.
State the goal for each
For each chosen dimension, articulate what success looks like in plain terms. The goal is the anchor that makes the eventual metric meaningful.
Identify the signal
Find the observable user behaviour that indicates the goal is being met — the thing users do when the experience is good. Signals bridge abstract goals and concrete metrics.
Define the metric
Turn the signal into a specific, measurable metric. This is the number you'll track — derived deliberately from the goal, not grabbed because it was easy.
Measure and interpret against the goal
Track the metrics and read them against the goals they came from. A metric divorced from its goal is just a number; tied to its goal, it tells you about the experience.
A short illustration
A team measured a feature purely by usage — how many times it was used — and concluded it was successful because the count was high. But the usage number said nothing about whether users were actually succeeding at their task or were happy with the experience; high usage could even mean users struggling and retrying.
Applying HEART with Goals–Signals–Metrics, they chose Task Success and Happiness as the relevant dimensions, stated goals (users complete the task efficiently; users are satisfied), found signals (task completion without errors; positive feedback), and derived real metrics. The fuller picture showed that high usage was partly users retrying after failures — a task-success problem the raw count had hidden as apparent success.
The HEART metric set
The deliverable is, for each chosen dimension, a goal → signal → metric chain — a deliberate, user-centred set of measures.
| Dimension | Measures | Example signal |
|---|---|---|
| Happiness | Satisfaction, attitude | Survey scores, feedback |
| Engagement | Depth of involvement | Frequency, intensity of use |
| Adoption | Uptake of new | New users of a feature |
| Retention | Continued use | Return rate over time |
| Task success | Effectiveness | Completion rate, error rate |
Why it matters
HEART exists to stop teams from mistaking activity for quality. Its Goals–Signals–Metrics discipline ensures you measure what good experience actually looks like — derived from a goal — rather than whatever happens to be easy to count.
The two disciplines that make HEART valuable are selection and derivation. Selection: not every dimension applies to every product or feature, so the framework forces a deliberate choice of which of the five matter here — resisting the urge to measure all five badly. Derivation: the Goals–Signals–Metrics chain ensures each metric traces back to a goal, so it can actually be interpreted, rather than being a convenient number with no meaning attached. Together these guard against the most common UX-measurement failure — grabbing usage counts because they're available and reading them as quality. Used well, HEART captures whether users are happy, succeeding, and coming back, which is what 'product health' actually means.
Common mistakes
Activity isn't experience. Use HEART to measure the dimensions that reflect real quality.
Convenient numbers aren't meaningful ones. Derive metrics from goals via Goals–Signals–Metrics.
Not every dimension applies everywhere. Choose the relevant ones deliberately.
A metric divorced from its goal can't be interpreted. Always start from the goal.
When not to use it
- Pure business-outcome measurement. HEART is for UX quality; for revenue and economics, use the financial tools (Tools 14–16).
- When one dimension obviously dominates. If only Task Success matters for a feature, don't force the other four.
- As a vanity exercise. A HEART metric you won't act on is as useless as any other unused metric.
Where this sits in the toolkit
HEART supplies the product-level UX metrics that sit in the hierarchy (Tool 02).
The 'E' in HEART connects to the breadth/depth engagement work (Tool 10).
HEART metrics can become input metrics in the tree (Tool 05).
Deriving metrics from goals mirrors outcome-based OKRs (Module 2).
Run Goals–Signals–Metrics for one dimension
Pick a feature and one HEART dimension that matters for it (say, Task Success). State the goal, identify the signal (what users do when it's working), and define the metric.
Compare your derived metric to the raw usage count you might have grabbed instead. What does yours capture that the count misses?
If your goal-derived metric reveals something the usage count hid — like users succeeding versus just clicking — you've seen why HEART measures experience rather than activity.
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