HEART

Kerry Rodden, Google (2010)

Measures user experience quality across five dimensions. Designed for UX teams who need to quantify subjective experience, not just business outcomes.

How it works

HEART uses a Goals–Signals–Metrics (GSM) process for each dimension. First, define your goal for that dimension. Then identify user signals that indicate progress. Finally, pick a metric that quantifies the signal. This three-step process prevents teams from tracking metrics that don’t connect to real user outcomes. The five dimensions are intentionally broad—you’re not expected to track all five at once. Pick the 2–3 most relevant to your current priorities.

Components

Happiness

User attitudes and satisfaction

Engagement

Depth and frequency of interaction

Adoption

New users and feature uptake

Task Success

Efficiency and completion of user goals

How to implement

  1. Choose 2–3 HEART dimensions most relevant to your product goals right now. Don’t try to cover all five.
  2. For each dimension, run the GSM exercise: Goal → Signal → Metric. Write it down.
  3. Define data collection. Some dimensions (Happiness) require surveys; others (Engagement) use product analytics.
  4. Establish baselines before making changes. You need a "before" to measure improvement.
  5. Review quarterly. Swap dimensions as product priorities shift.

In practice

Google

Developed HEART internally to measure UX quality across products. Gmail used it to discover that "Task Success" (email send completion rate) was more predictive of satisfaction than "Engagement" (time in app), leading to a focus on reducing friction rather than increasing usage.

YouTube

Applied HEART to shift focus from "Engagement" (views) to "Happiness" (satisfaction surveys after watching). This led to the recommendation algorithm changes that prioritize watch satisfaction over clickbait.

Best for

UX-driven teams, design organizations, and products where user experience quality is the primary competitive advantage. Works well alongside business metrics frameworks.

When to avoid

Early-stage startups where business model validation matters more than UX polish. Also poor for products where the primary challenge is distribution, not experience—if users never find you, measuring their happiness is premature.

Limitations

Less prescriptive about business outcomes (revenue, unit economics). Requires careful signal/metric definition per dimension—the framework provides categories, not specific metrics.

Pairs well with