HEART Framework: Google’s 5-Metric Model for Measuring User Experience (2026 Guide)

What Is the HEART Framework?

The HEART framework is Google’s five-metric model for measuring the quality of a user experience: Happiness, Engagement, Adoption, Retention, and Task Success. It was developed in 2010 by Kerry Rodden, Hilary Hutchinson, and Xin Fu on Google’s research team, and first published at ACM CHI as Measuring the User Experience on a Large Scale. The goal was to give product teams a small, focused set of user-centered metrics they could pair with their existing analytics, instead of drowning in raw clickstream data.

More than fifteen years later, HEART remains the most-cited UX measurement framework in product. It is used inside Google, Spotify, Atlassian, and most modern product orgs because it does two things at once: it covers both attitudinal signals (what users say) and behavioral signals (what users do), and it pairs naturally with the Goals–Signals–Metrics (GSM) process for turning fuzzy product goals into trackable numbers.

The Five HEART Metrics, Explained

H — Happiness

Attitudinal measures of how users feel about the product. Typically collected through scale-based surveys (CSAT, NPS, SUS, custom satisfaction items) and qualitative follow-ups. Happiness is the only HEART metric that requires asking the user; you cannot infer it from logs.

Common signals: satisfaction rating, perceived ease, recommendation intent, perceived speed. Common metrics: CSAT % top-2-box, average SUS score, NPS, average 1–7 happiness score.

E — Engagement

The depth of interaction per session, for active users. Engagement is not the same as adoption; it specifically describes how intensely current users use the product.

Common signals: sessions per user per week, time in app, photos uploaded, messages sent. Common metrics: average sessions per active user, median session duration, activity events per user per week.

A — Adoption

New users in a defined period, or new users of a specific feature. Adoption answers “Is anyone picking this up?” — it is the leading indicator before retention.

Common signals: first-time users of a feature, upgrades, conversions to paid plan. Common metrics: % of monthly actives who used Feature X at least once, weekly new signups, % of trial users who completed activation.

R — Retention

The rate at which users return over a defined period. Retention is the closest HEART metric to a business outcome — it is what every product compounds against over time.

Common signals: users active in week 2, week 4, week 12; reactivation; churn. Common metrics: D1/D7/D30 retention curves, monthly active retention, gross/net revenue retention for B2B.

T — Task Success

Whether users can complete the things they came to do. Task Success is the most under-instrumented HEART metric — most teams measure adoption and retention but never check whether the users they retained are actually succeeding.

Common signals: task completion, errors per task, time to complete, search-to-success rate. Common metrics: task completion rate %, average Single Ease Question (SEQ) score, error rate, time on task.

Expert insight: “When we created HEART, the goal wasn’t to add five more metrics to a dashboard — it was to give designers and PMs a vocabulary for choosing the right metric for the question they were actually asking,” — Kerry Rodden, framework co-author, in her published HEART reference notes (kerryrodden.com/heart).

The Goals–Signals–Metrics (GSM) Process

HEART without GSM is a checklist. HEART with GSM is a measurement system. GSM is the three-step exercise the original authors paired with the framework:

1. Goals — For each HEART category that matters to your project, write one sentence describing what success looks like for the user. Not for the business.
2. Signals — List the observable behaviors or stated attitudes that would tell you the goal is being achieved (or missed).
3. Metrics — For each signal, choose the precise number you will track over time.

Worked example: a new in-app onboarding flow

Notice that Engagement and Retention were intentionally left off. The team is measuring an onboarding flow, not the whole product. This is the entire point of GSM — deliberately scoping which HEART metrics matter for this project.

Why HEART Works (and What It Replaced)

Before HEART, most product orgs split into two warring camps: a quant analytics team chasing dashboard numbers, and a qual UX team running studies that no one tied to KPIs. HEART’s contribution was to make both legitimate inputs to the same scorecard. Happiness lives next to Engagement. SEQ scores live next to D7 retention. The framework forces a team to admit that users’ stated experience and observed behavior are both part of ‘the metric.’

A 2023 industry survey by ProductPlan found that HEART is among the top three UX measurement frameworks adopted by mature product teams, alongside North Star and AARRR. Atlassian, Spotify, GoDaddy, and dozens of public design system case studies cite HEART as the model their measurement plan is built on.

How to Implement HEART in Five Steps

1. Pick the project, not the product

Do not try to instrument HEART for “the whole app.” Pick a single project: a feature launch, a redesign, a flow you suspect is broken. The metrics framework should be scoped to a decision you are about to make.

2. Choose 2–3 HEART categories

Resist the urge to fill in all five. The original Google paper explicitly recommends picking the categories that map to the project’s goal. A new feature launch is usually Adoption + Task Success. A retention play is usually Retention + Happiness. A redesign of a complex form is usually Happiness + Task Success.

3. Run the GSM exercise as a workshop

Get the PM, designer, researcher, and engineer in the room for 60–90 minutes. For each HEART category you picked, write the Goal sentence first, then brainstorm Signals, then narrow to one or two Metrics. Force every metric to have a numeric target.

4. Instrument behavioral metrics in analytics; instrument attitudinal metrics in research

Engagement, Adoption, Retention, and the behavioral part of Task Success belong in your analytics platform (Mixpanel, Amplitude, PostHog, GA4). Happiness and the perceptual part of Task Success (SEQ, post-task confidence) belong in research — surveys, AI-moderated interviews, or in-product micro-surveys.

5. Set a review cadence

Review HEART metrics at the cadence that matches the decision: weekly for an active launch, monthly for a steady-state product area, quarterly for a strategic theme. Without a cadence, HEART becomes a slide that gets shown once and forgotten.

The Modern Approach: HEART With AI-Moderated Research

The traditional weakness of HEART has always been the attitudinal half — Happiness and the perceptual side of Task Success. Behavioral metrics are essentially free once analytics is wired up; surveys and qualitative follow-ups are not. Most product teams either (a) skip Happiness entirely and rely on the proxy of NPS once a quarter, or (b) run a quarterly Typeform survey nobody analyses past the average score.

This is exactly the gap an AI-native research platform like Koji closes. With Koji you can:

• Bundle every attitudinal HEART signal into one continuous study. Use Koji’s structured questions (six types: open_ended, scale, single_choice, multiple_choice, ranking, yes_no) to drop in a SEQ scale, a Happiness scale, a CSAT single_choice, and a feature adoption yes_no — in a single 4-minute interview.
• Run the Happiness survey continuously, not quarterly. Koji’s AI moderator runs interviews 24/7 against a shareable link or in-product widget, so Happiness is a streaming metric, not an annual event.
• Get the why alongside every score. Each scale question is followed by an AI-driven open-ended probe (“What made you rate it that way?”), and Koji’s thematic analysis engine clusters the explanations into themes automatically. You see your Happiness score and the top three reasons it moved — in the same dashboard.
• Replace the read-out slide with live reports. Koji’s real-time report aggregation updates the HEART scorecard the moment new responses arrive, eliminating the multi-week gap between data collection and stakeholder review.

Teams using AI-assisted research tools report 60% faster time-to-insight than teams running the same studies manually (Forrester, State of Customer Insights 2024), and Koji’s own customers run HEART attitudinal scorecards in days instead of the typical 2–3 week survey cycle.

Five Common HEART Mistakes to Avoid

1. Trying to track all five categories on every project. GSM exists to scope you down. Two categories well-instrumented beats five categories half-instrumented.
2. Confusing Engagement with Adoption. Engagement = depth among existing actives. Adoption = first-time uptake. Mixing them produces meaningless dashboards.
3. Skipping Happiness because ‘we’ll do NPS quarterly.’ NPS is a brand-loyalty metric. It is not a Happiness signal at the feature level. Run a fast SEQ + 1–7 satisfaction post-task instead.
4. Defining Task Success as ‘they clicked the button.’ A click is not a success. The user has to complete the underlying job. Pair behavioral completion with a SEQ score and a post-task open-ended probe.
5. No baseline and no target. A HEART metric without a previous reading and a target number is a vanity number. Always show ‘current vs prior period vs target.’

When NOT to Use HEART

HEART is overkill for very early-stage discovery (“do people have this problem at all?”) — use Mom Test customer interviews or Jobs-to-Be-Done switch interviews instead. HEART also under-serves pure e-commerce funnels, which are better measured with conversion-rate optimisation. HEART shines for product features and experiences with repeat use, which is most B2B and consumer SaaS.

Related Resources

• Structured Questions in AI Interviews — the six question types Koji supports for HEART scale and yes/no items
• System Usability Scale (SUS): Complete Guide — the standard Happiness instrument for HEART
• Customer Effort Score (CES): How to Measure and Reduce Friction — a complement to Task Success in HEART
• How to Build an NPS Survey That Actually Drives Action — NPS as a coarse Happiness proxy
• Top Tasks Analysis: Identify the Few Tasks That Matter — the natural input to scoping Task Success
• UX Research Process: A Complete Framework for 2026 — where HEART fits in the broader research workflow